Search

This paper examines the impact of policy uncertainty on air pollution, using the turnover of local officials in China as a quasi-natural experiment. The results from the two-way fixed effects model show that policy uncertainty caused by the turnover of different officials produces contrasting effects on air pollution. While the turnover of the mayor has no effect on air pollution, the turnover of the municipal party committee secretary deteriorates it. Two-Stage Least Square estimation confirms these results where the age and tenure of officials were used as instrumental variables of policy uncertainty. The results are robust after endogenous treatments and robustness tests. The mechanism analysis further indicates that policy uncertainty worsens air quality by weakening environmental regulation and crowding out green innovation. Moreover, the impact is more pronounced in smaller and less developed cities. The conclusions provide policy implications for environmental governance in the period of policy uncertainty. [ABSTRACT FROM AUTHOR]

Some patients experience sleep disturbances after endoscopy performed under sedation. This study aimed to evaluate the effects of propofol on sleep quality after gastrointestinal endoscopy (GE). This study was a prospective cohort study. This study enrolled 880 patients who underwent GE. Patients who chose to undergo GE under sedation received intravenous propofol, whereas the control group did not. The Pittsburgh Sleep Quality Index (PSQI) was measured before GE (PSQI-1) and 3 weeks (PSQI-2) after GE. The Groningen Sleep Score Scale (GSQS) was used before GE (GSQS-1) and 1 (GSQS-2) and 7 days (GSQS-3) after GE. There was a significant increase in GSQS scores from baseline to days 1 and 7 after GE (GSQS-2 vs GSQS-1, P <.001, GSQS-3 vs GSQS-1, P =.008). However, no significant changes were observed in the control group (GSQS-2 vs GSQS-1, P =.38, GSQS-3 vs GSQS-1, P =.66). On day 21, there were no significant changes in the baseline PSQI scores over time in either group (sedation group, P =.96; control group, P =.95). GE with propofol sedation negatively affected sleep quality for 7 days after GE but not 3 weeks after GE. [ABSTRACT FROM AUTHOR]

In many statistical modeling problems, such as classification and regression, it is common to encounter sparse and blocky coefficients. Sparse fused Lasso is specifically designed to recover these sparse and blocky structured features, especially in cases where the design matrix has ultrahigh dimensions, meaning that the number of features significantly surpasses the number of samples. Quantile loss is a well-known robust loss function that is widely used in statistical modeling. In this paper, we propose a new sparse fused lasso classification model, and develop a unified multi-block linearized alternating direction method of multipliers algorithm that effectively selects sparse and blocky features for regression and classification models. Our algorithm has been proven to converge with a derived linear convergence rate. Additionally, our algorithm has a significant advantage over existing algorithms for solving ultrahigh dimensional sparse fused Lasso regression and classification models due to its lower time complexity. Note that the algorithm can be easily extended to solve various existing fused Lasso models. Finally, we present numerical results for several synthetic and real-world examples, which demonstrate the robustness, scalability, and accuracy of the proposed classification model and algorithm. • This paper introduces a new pinball support vector machine. • We show quantile regression and classification have a unified optimized form. • We develop a new iterative algorithm to solve the unified optimized form. • The convergence and complexity analysis of the algorithm are also given. • The proposed algorithm can be extended to solve many existing models. [ABSTRACT FROM AUTHOR]

The dairy industry is an important contributor to global greenhouse gas (GHG) emissions. This study evaluates the potential for reducing GHG emissions from a typical large-scale dairy farm in the cold climate zone of northern China by integrating traditional and innovative biotechnologies. We conducted a life cycle assessment comparing a business-as-usual (BAU) scenario with a low-carbon dairy combined biotechnologies (LCBT) scenario. The BAU scenario reflects current practices, while the LCBT scenario incorporates integrated soil–crop system management (ISSM), methane inhibitors in cattle feed, and the use of microalgae and insects for manure treatment. The BAU scenario's total GHG emissions were 208.9 Gg CO 2 eq, with enteric fermentation accounting for 55%. The LCBT scenario demonstrated a 50% reduction in GHG emissions, achieving a net emission of 105.1 Gg CO 2 eq. Around half of the reduction is contributed by advanced biotechnology. Energy consumption increased under the LCBT scenario due to the high energy demands of insect and microalgae treatment processes, highlighting the need for a transition to clean energy, which may further reduce the emissions to 28% of that in BAU. The study underscores the potential of a comprehensive approach that combines sustainable agricultural practices with biotechnological advancements to reduce the carbon footprint of dairy farming. Policy implications include the need for regulatory support and incentives to facilitate the adoption of these technologies, particularly in cold climate regions where energy consumption for maintaining biological processes is a critical factor. [Display omitted] • The greenhouse gas reduction scenario achieves a 50% cut in dairy farming. • Advanced biotechnology contributes to a 60% greenhouse gas emissions reduction. • Advanced biotechnology will add 55.9 GWh energy use. • Clean energy will cut dairy farming emissions in cold regions to 28% of the original. [ABSTRACT FROM AUTHOR]