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For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.

This paper examines how firms respond to local government’s environment initiatives through textual analysis of government work reports (GWRs). This study aims to provide insights into how firms strategically respond to government’s environmental initiatives through their disclosure and investment practices.

This study uses a textual analysis of GWRs from China’s provinces. The frequency and change rate of environmental keywords in these reports are used as a measure of the government’s environmental initiatives.

This study finds that environmental disclosure scores in environmental, social and governance (ESG) reports increase with the frequency or change rate of environmental keywords in provincial GWRs. This effect is more pronounced for non-state-owned enterprises, firms in highly marketized provinces or those listed in a single capital market. However, there is no significant relationship between firms’ environmental investments and government initiatives, except for cross-listed firms in provinces with consistently high frequency of environmental keywords in their GWRs.

The findings indicate that government environmental initiatives can shape firms’ disclosure behaviors, yet have limited influence on investment decisions, suggesting that environmental disclosure could potentially be opportunistic. This underscores the need for more effective strategies to stimulate firms’ environmental investments.

This study provides valuable insights into the differential impacts of government environmental initiatives on firms’ disclosure and investment behaviors, contributing to the understanding of corporate environmental responsibility in the context of government initiatives.

The adoption and usage of information and communication technology (ICT) has introduced transformation in the tourism arena with ICT applications extensively used in tourism industry. In addition to ICT, an advanced infrastructure is essential for the development of tourism industry. Thus, the goal of present research is to probe the impact of ICT and infrastructure on tourism development (TD) in 28 Asian economies using method of moments panel quantile regression (MM-QR) model introduced by Machado and Silva (2019) applied to a panel data from 2008 to 2020. Empirical findings demonstrate that there is an asymmetric non-linear effect of ICT and infrastructure through all quantile range. This indicates that ICT has negative effect on TD in poor countries while positive impact in rich countries. Negative impact in poor countries may be due to higher establishment cost and information technology (IT) productivity paradox. However, results confirm the importance of ICT and infrastructure in endorsing the development of tourism sector in Asian nations by lessening time and money costs and facilitating travelers.

The purpose of this study is to investigate the effect of noncognitive skills on educational mismatch in the context of China and to further explore the potential mechanisms concerning how noncognitive skills determine mismatch outcomes.

This paper uses the Chinese Family Panel Survey of 2018, which provides the Big Five Personality Inventory to assess respondents' noncognitive skills and contains information on educational mismatch. The authors estimate the effects of noncognitive skills on educational mismatch by means of a probit model. Additionally, the correlated random effects (CRE) model and instrumental variable (IV) approach have also been exploited in the robust checks.

The findings show that the composite score of noncognitive skills reduces the probability of being overeducated and, conversely, increases the likelihood of being undereducated. When distinguishing the effects of different personality traits, the authors find significantly negative effects of agreeableness and openness on overeducation and a positive effect of openness on undereducation. With regard to heterogeneous analysis, the effects of noncognitive skills on educational mismatch exist mostly among white-collar employees and employees with fewer than 5 years of work experience. Finally, the authors provide two likely mechanisms related to job search effort and social capital, followed by the presentation of supporting evidence.

The results of this paper underline the importance of noncognitive skills in raising the quality of jobs that individuals can obtain. This suggests that the development of noncognitive skills should be encouraged to be integrated into formal education systems and social job training programs in China.

Despite a growing interest in its consequences in the labor market, the role of noncognitive skills in determining educational mismatch has rarely been discussed in developing countries. This study provides the first evidence regarding the effects of noncognitive skills on education mismatch in China. It contributes to the research on noncognitive skills' labor market outcomes and enhances the understanding of the factors driving educational mismatch.

This study aims to investigate the effect of seasonality caused by fasting as a religious practice on trading activity.

The authors use an unbiased sample of daily trading by individuals and institutions on the Boursa Kuwait. The authors use panel data on trading activities and Tobit regression models to examine the effect of Muslims’ religious practice of fasting during the holy month of Ramadan on trading behavior.

The authors find that during the holy month of Ramadan, Muslims’ religious practice of fasting leads to a decline in the frequency of both overall stock market trading and the ratio of individual trading volume to total trading volume. The authors find a significant decrease in individual buy-side trading as a proportion of total trading volume and simultaneously a significant increase in institutional buy-side trading.

This study’s findings have important implications for the main players in stock markets of countries with a Muslim majority. Market-makers should be aware of the significant increase in the proportion of institutional buy-side trading volume to total trading volume to minimize the cost of trading with better-informed traders (adverse selection).

To the best of the authors’ knowledge, this is the first study that investigates individuals’ trading activity during Ramadan.

This study aims to use regression Least-Square Support Vector Machine (LS-SVM) as a probabilistic model to determine the factor of safety (FS) and probability of failure (PF) of anisotropic soil slopes.

This research uses machine learning (ML) techniques to predict soil slope failure. Due to the lack of analytical solutions for measuring FS and PF, it is more convenient to use surrogate models like probabilistic modeling, which is suitable for performing repetitive calculations to compute the effect of uncertainty on the anisotropic soil slope stability. The study first uses the Limit Equilibrium Method (LEM) based on a probabilistic evaluation over the Latin Hypercube Sampling (LHS) technique for two anisotropic soil slope profiles to assess FS and PF. Then, using one of the supervised methods of ML named LS-SVM, the outcomes (FS and PF) were compared to evaluate the efficiency of the LS-SVM method in predicting the stability of such complex soil slope profiles.

This method increases the computational performance of low-probability analysis significantly. The compared results by FS-PF plots show that the proposed method is valuable for analyzing complex slopes under different probabilistic distributions. Accordingly, to obtain a precise estimate of slope stability, all layers must be included in the probabilistic modeling in the LS-SVM method.

Combining LS-SVM and LEM offers a unique and innovative approach to address the anisotropic behavior of soil slope stability analysis. The initiative part of this paper is to evaluate the stability of an anisotropic soil slope based on one ML method, the Least-Square Support Vector Machine (LS-SVM). The soil slope is defined as complex because there are uncertainties in the slope profile characteristics transformed to LS-SVM. Consequently, several input parameters are effective in finding FS and PF as output parameters.

The purpose of this study is to examine the impact of key audit matters (KAMs) on the timeliness of financial statement reporting, measured as audit report lag (ARL), within the context of Kuwait's evolving financial market.

Using a sample of 136 unique firms and 841 firm-year observations over the period 2016–2022, the study employs a random effects model on a panel data set to examine the correlation between the number and type of KAMs disclosed in audit reports and the length of ARL. In addition, we employ sub-sample analysis and two-stage least squares (2SLS) regression to enhance overall reliability.

The results indicate a positive relationship between an increased number of reported KAMs and the length of ARL. Specific categories of KAMs, such as those related to investments and the implementation of new standards, also significantly impact the delay. Additionally, the findings reaffirm the importance of several determinants of ARL, which is consistent with prior research.

This study is among the first to offer new insights by examining the relationship between both the number and specific types and/or categories of KAMs on ARL in emerging markets.

Field-effect transistor (FET) has excellent electronic properties and inherent signal amplification, and with the development of nanomaterials technology, FET biosensors with nanomaterials as channels play an important role in the field of heavy metal ion detection. This paper aims to review the research progress of silicon nanowire, graphene and carbon nanotube field-effect tube biosensors for heavy metal ion detection, so as to provide technical support and practical experience for the application and promotion of FET.

The article introduces the structure and principle of three kinds of FET with three kinds of nanomaterials, namely, silicon nanowires, graphene and carbon nanotubes, as the channels, and lists examples of the detection of common heavy metal ions by the three kinds of FET sensors in recent years. The article focuses on the advantages and disadvantages of the three sensors, puts forward measures to improve the performance of the FET and looks forward to its future development direction.

Compared with conventional instrumental analytical methods, FETs prepared using nanomaterials as channels have the advantages of fast response speed, high sensitivity and good selectivity, among which the diversified processing methods of graphene, the multi-heavy metal ions detection of silicon nanowires and the very low detection limit and wider detection range of carbon nanotubes have made them one of the most promising detection tools in the field of heavy metal ions detection. Of course, through in-depth analysis, this type of sensor has certain limitations, such as high cost and strict process requirements, which are yet to be solved.

This paper elaborates on the detection principle and classification of field-effect tube, investigates and researches the application status of three kinds of FET biosensors in the detection of common heavy metal ions. By comparing the advantages and disadvantages of each of the three sensors in practical applications, the paper focuses on the feasibility of improvement measures, looks forward to the development trend in the field of heavy metal detection and ultimately promotes the application of field-effect tube development technology to continue to progress, so that its performance continues to improve and the application field is constantly expanding.