The low commercialization rate of academic patents is a common challenge for developing countries. This study evaluates the impact of provincial policies on the commercialization of academic patents using spatial regression models and propensity score matching methods. It also provides a reference for optimizing university patent management. Considering provincial patent policies enacted in China in 2016 as the treatment, the results show no significant effect of provincial patent policies on rights definition and implementation economics on the commercialization of academic patents. However, promotion standard policies have a significant positive effect on the commercialization of academic patents. A significant negative spatial spillover effect is observed on the commercialization of academic patents among universities with similar innovation capabilities. Furthermore, non-geographic distances, such as innovation distance, positively affect competition among universities and their commercialization of academic patents. Therefore, local governments should consider the needs of academic inventors for title promotion when designing and introducing patent incentive policies for universities.
In the context of the rapid growth of corporate green investment and the rapid dissemination of information brought about by Internet technology, it is important to explore the relationship between investor attention and corporate environmental responsibility. Unfortunately, an in-depth research on the relationship between investor attention and firms' environment, society, and governance (ESG) performance remains unexplored. The results show a mutual inhibition between investor attention and firms’ ESG performance. Each 1% increase in investor attention decreases ESG performance by 0.252%, while each 1% increase in ESG performance decreases investor attention by 2.296%. Thus, ESG performance dominates this mutual influence. Moreover, ESG performance positively affects ESG performance and investor attention of neighboring firms. Each 1% increase in ESG performance increases ESG performance and investor attention of neighboring firms by 0.371% and 0.983%, respectively. Investor attention negatively affects investor attention and ESG performance of neighboring firms. Each 1% increase in investor attention decreases ESG performance and investor attention of neighboring firms by 0.04% and 0.104%, respectively. Further research reveals significant regional and organizational heterogeneity in the relationship between investor attention and ESG performance. The findings provide theoretical and empirical insights for further improvement of the ESG system and continued strengthening of investor guidance by regulators.
As industrial automation supplants labor, there are important consequences on the labor market, especially for the vast rural population in developing countries. This study investigates how industrial automation, particularly the use of robots, affects the employment and mobility of young rural workers and the decision-making of their elderly parents in China. Using longitudinal data from rural Chinese households and a shift-share approach, we find automation in urban areas decelerates the rural-to-urban migration. This slowdown is accompanied by a reduction in rural workers' employment opportunities and an increase in their propensity to co-reside with their elderly parents in their home counties, which in turn alters their parents' financial decisions, including labor supply, savings, and family transfers. Notably, rural workers' decision to stay and co-reside with their senior parents fosters more frequent family interactions, contributing to notable improvements in the elders' mental and physical health. Our study underscores the complex effects of automation on labor mobility, family relationships, and the overall well-being of the rural populace in the face of technological advancements.
Although tourism is often treated as one of the crucial industries for the construction of low-carbon cities (LCCs), there is no systematic evidence on whether there is a causal relationship. This research aims to explore and empirically test the causal link between LCC initiatives and inbound tourism of cities using a spatial difference-in-differences approach with balanced panel data of 59 Chinese major tourism cities from 2000 to 2017. The results show that urban tourism by foreign tourists exhibits significant spatial spillover effects. Compared to non-LCCs, the number of foreign tourists on LCCs increased by 4.7 percentage points and the average length of stay of foreign tourists increased by 3.6 percentage points. The direct impact of the LCC initiative on foreign tourists was significant, while the indirect impact was insignificant. The findings of the study not only deepen the researchon sustainable tourism behavior of inbound tourists, but also provide valuable references for cities to participate in the competition in the international tourism market through low-carbon development.
The ecological impact of microplastics (MPs) in coastal environments has been widely studied. However, the influence of small microplastics in the actual environment is often overlooked due to measurement challenges. In this study, Hangzhou Bay (HZB), China, was selected as our study area. High-throughput metagenomic sequencing and micro-Raman spectrometry were employed to analyze the microbial communities and microplastics of coastal sediment samples, respectively. We aimed to explore the ecological impact of MPs with small sizes (≤ 100 μm) in real coastal sediment environments. Our results revealed that as microplastic size decreased, the environmental behavior of MPs underwent alterations. In the coastal sediments, no significant correlations were observed between the detected MPs and the whole microbial communities, but small MPs posed potential hazards to eukaryotic communities. Moreover, these small MPs were more prone to microbial degradation and significantly affected carbon metabolism in the habitat. This study is the first to reveal the comprehensive impact of small MPs on microbial communities in a real coastal sediment environment.
In the search for high-temperature superconductivity in hydrides, a plethora of multi-hydrogen superconductors have been theoretically predicted, and some have been synthesized experimentally under ultrahigh pressures of several hundred GPa. However, the impracticality of these high-pressure methods has been a persistent issue. In response, we propose a new approach to achieve high-temperature superconductivity under ambient pressure by implanting hydrogen into lead to create a stable few-hydrogen binary perovskite, Pb4H. This approach diverges from the popular design methodology of multi-hydrogen covalent high critical temperature (Tc ) superconductors under ultrahigh pressure. By solving the anisotropic Migdal–Eliashberg equations, we demonstrate that perovskite Pb4H presents a phonon-mediated superconductivity exceeding 46 K with inclusion of spin–orbit coupling, which is six times higher than that of bulk Pb (7.22 K) and comparable to that of MgB2, the highest Tc achieved experimentally at ambient pressure under the Bardeen, Cooper, and Schrieffer framework. The high Tc can be attributed to the strong electron–phonon coupling strength of 2.45, which arises from hydrogen implantation in lead that induces several high-frequency optical phonon modes with a relatively large phonon linewidth resulting from H atom vibration. The metallic-bonding in perovskite Pb4H not only improves the structural stability but also guarantees better ductility than the widely investigated multi-hydrogen, iron-based and cuprate superconductors. These results suggest that there is potential for the exploration of new high-temperature superconductors under ambient pressure and may reignite interest in their experimental synthesis in the near future.
Phyllosphere is the largest interface between the atmosphere and terrestrial ecosystems and serves as a major sink for atmospheric microplastics (MPs). It is also a unique habitat for microbiota with diverse ecological functions. This field study investigated the characteristics of atmospheric MPs adsorbed on leaves with automatic technology, and found their abundance was 3.62 ± 1.29 items cm−2. MPs on leaves were mainly below 80 µm, and dominated by polyamide, polyethene, and rubber. MPs on leaves correlated significantly with the structure and functions of the phyllosphere bacterial community (PBC). Both the MPs abundance and size distribution (MSD) were positively correlated with the α diversity and negatively correlated with the β diversity and network complexity of PBC. PBC functions of environmental and genetic information process were negatively correlated with MPs abundance, and functions related to human diseases and cellular process were positively correlated with MSD significantly. The relative abundance of Sphingomonas was significantly correlated with the MSD, suggesting that Sphingomonas might emerge as the key genus involved in the pathogenicity of PBC mediated by MPs. These results highlighted the ecological health risks of atmospheric MPs as they can be transferred anywhere and potentially increase the pathogenicity of local phyllosphere microflora.
The transfer of farmland is an important area of rural development research; however, the impact of rural social networks has been neglected in studies. The aim of this study is to explore the effects, mechanisms, and heterogeneity of neighbors’ behavior on the process of land renting by farmers. Based on the data of the China Family Panel Studies in 2018, this research empirically analyzes the impact of community-level, local social interactions on the land rental behavior of farmers and its mechanisms using a spatial probit model. The results of this study indicate that neighbors’ land rental behavior positively and significantly affects that of other farmers in the same village. In addition, neighbors’ land rental encourages other farmers in the same village to follow suit through an increase in the perceived importance of the Internet among the farmers. In addition, there is heterogeneity in neighborhood influence. Notably, the impact of social networks on the renting out of the land by farmers, as evidenced in this study, is a key factor in accelerating the circulation of rural land and promoting rural development, thus contributing to the process of rural revitalization and its recording in the literature.
The infamous numerical sign problem poses a fundamental obstacle to particle- based stochastic Wigner simulations in high-dimensional phase space. Although the existing particle annihilation (PA) via uniform mesh significantly alleviates the sign problem when dimensionality D <= 4, the mesh size grows dramatically when D >= 6 due to the curse of dimensionality and consequently makes the annihilation very inefficient. In this paper, we propose an adaptive PA algorithm, termed sequential-clustering particle annihilation via discrepancy estimation (SPADE), to overcome the sign problem. SPADE follows a divide-and-conquer strategy: adaptive clustering of particles via controlling their number-theoretic discrepancies and independent random matching in each cluster. The target is to alleviate the oversampling problem induced by the overpartitioning of phase space and to capture the nonclassicality of the Wigner function simultaneously. Combining SPADE with the variance reduction technique based on the stationary phase approximation, we attempt to simulate the proton-electron couplings in six- and 12-dimensional phase space. A thorough performance benchmark of SPADE is provided with the reference solutions in six-dimensional phase space produced by a characteristic-spectral-mixed scheme under a 733*803 uniform grid, which fully explores the limit of grid-based deterministic Wigner solvers.
In the era of a green economy, green innovation has become a way for enterprises to gain competitive advantage, and it is of great theoretical and practical significance to explore the driving force of enterprises' green innovation. This study explores the peer effect of an enterprise's green innovation and conducts an empirical test using data from 3338 Chinese listed companies in 2020. The results show a significant positive peer effect of enterprises' green innovation, and the green innovation of individual enterprises increases by 0.869 for each unit increase in industry-average green innovation. Further research shows that market power is the channel by which peer influence affects an enterprise's green innovation. Moreover, regional heterogeneity exists in the strength of the peer effect, which varies according to firm maturity and board size. These findings provide a reference for enterprises and governments to promote green transformation.
The plastisphere may act as reservoir of antibiotic resistome, accelerating global antimicrobial resistance dissemination. However, the environmental risks in the plastisphere of field microplastics (MPs) in farmland remain largely unknown. Here, antibiotic resistance genes (ARGs) and virulence factors (VFs) on polyethylene microplastics (PE-MPs) and polybutylene adipate terephthalate and polylactic acid microplastics (PBAT/PLA-MPs) from residues were investigated using metagenomic analysis. The results suggested that the profiles of ARG and VF in the plastisphere of PBAT/PLA-MPs had greater number of detected genes with statistically higher values of diversity and abundance than soil and PE-MP. Procrustes analysis indicated a good fitting correlation between ARG/VF profiles and bacterial community composition. Actinobacteria was the major host for tetracycline and glycopeptide resistance genes in the soil and PE-MP plastisphere, whereas the primary host for multidrug resistance genes changed to Proteobacteria in PBAT/PLA-MP plastisphere. Besides, three human pathogens, Sphingomonas paucimobilis, Lactobacillus plantarum and Pseudomonas aeruginosa were identified in the plastisphere. The PE-MP plastisphere exhibited a higher transfer potential of ARGs than PBAT/PLA-MP plastisphere. This work enhances our knowledge of potential environmental risks posed by microplastic in farmland and provides valuable insights for risk assessment and management of agricultural mulching applications.
Mineral scale refers to the hard inorganic solids nucleated on substrates or deposited from the aqueous phase. The formation and deposition of barium sulfate and strontium sulfate in various industries, such as water treatment and oilfield operations, can significantly impact facility operations, posing serious threats. Machine learning (ML) approaches have been adopted recently in scale threat predictions to address the limitations of conventional scaling prediction models. However, there are few reports on collecting sulfate mineral scaling data, employing ML methods for data analysis, and evaluating the modeling results to gain deeper insights of sulfate mineral scaling process and to improve the accuracy of sulfate scaling threat prediction. Despite comprehensive experimental studies, the literature does not provide adequate guidance for identifying the influence on the solubility of barium sulfate and strontium sulfate under different aqueous environments and actual operating conditions. To this end, this study collected 1600 experimental datasets of barium/strontium sulfate from the literature to construct and evaluate the reliability and versatility of a ML-based model for sulfate solubility calculations. Single neural networks, hybrid neural networks, and optimization algorithms were employed to build solubility prediction models for barium sulfate and strontium sulfate across a wide range of temperatures, pressures, and different ions. The model's applicability in predicting sulfate scaling threats in various actual operating environments demonstrated its broad usability, consistent with its actual performance. This study marks the first stride towards constructing a reliable model for identifying the scaling trends of barium sulfate and strontium sulfate across various operating conditions, underscoring the importance of developing robust and accurate prediction models to address challenges in various industrial systems.