Empirical Results Research Articles

Consequences of omitting non-lethal wildlife impacts from stated preference scenarios

Stated preference (SP) research on willingness to pay (WTP) for improvements to wildlife populations focuses almost universally on measures related to whether organisms live or die. Preferences for changes in non-lethal harm to wildlife are generally overlooked. To evaluate the consequences, this article develops a theoretical model and corresponding discrete choice experiment (DCE) to evaluate whether and how the omission of information on non-lethal wildlife harm influences WTP estimation, grounded in a case study of marine plastic reductions in the North Atlantic. The theoretical model suggests that when environmental programs have both lethal and non-lethal impacts on wild species, DCEs that omit information on the latter may not produce valid welfare measures. Empirical results show that the omission of this information has multiple impacts on welfare estimates, largely consistent with theoretical predictions. Results suggest that welfare estimates for wildlife improvements can be confounded by the omission of information on non-lethal harm from SP scenarios. Results also demonstrate the hazards of excluding potentially welfare-relevant information from SP scenarios when respondents might assume relationships between omitted information and other material included in the questionnaire.

Task-Adaptive Multi-Source Representations for Few-Shot Image Recognition

Conventional few-shot learning (FSL) mainly focuses on knowledge transfer from a single source dataset to a recognition scenario with only a few training samples available but still similar to the source domain. In this paper, we consider a more practical FSL setting where multiple semantically different datasets are available to address a wide range of FSL tasks, especially for some recognition scenarios beyond natural images, such as remote sensing and medical imagery. It can be referred to as multi-source cross-domain FSL. To tackle the problem, we propose a two-stage learning scheme, termed learning and adapting multi-source representations (LAMR). In the first stage, we propose a multi-head network to obtain efficient multi-domain representations, where all source domains share the same backbone except for the last parallel projection layers for domain specialization. We train the representations in a multi-task setting where each in-domain classification task is taken by a cosine classifier. In the second stage, considering that instance discrimination and class discrimination are crucial for robust recognition, we propose two contrastive objectives for adapting the pre-trained representations to be task-specialized on the few-shot data. Careful ablation studies verify that LAMR significantly improves representation transferability, showing consistent performance boosts. We also extend LAMR to single-source FSL by introducing a dataset-splitting strategy that equally splits one source dataset into sub-domains. The empirical results show that LAMR can achieve SOTA performance on the BSCD-FSL benchmark and competitive performance on mini-ImageNet, highlighting its versatility and effectiveness for FSL of both natural and specific imaging.

Affordance, digital media literacy, and emotions in virtual cultural heritage tourism experiences

This study aims to investigate the effect of both the affordance of virtual reality (VR) technology and users’ digital media literacy on users’ emotions, which leads to recommendations to use VR experiences in cultural heritage tourism. A survey was conducted at a realistic VR studio in Gyeongju World Culture Expo Park, South Korea using high-quality and sophisticated VR content that accurately reflects users’ physical movements in real-time rendering. A total of 157 visitors participated in the study, and the measurement model and structural model were analysed using partial least square-structural equation modelling (PLS-SEM). The findings show that the cognitive, physical, sensory, and functional affordance of VR devices, as well as user digital media literacy, had a significant effect on the visitors’ positive emotional experiences. In addition, visitors’ positive emotions had a significant effect on the recommendation intention. This empirical result shows that digital media literacy is a new major variable that affects the virtual experience of cultural heritage sites.

Public finance instruments and output growth in Nigeria

This study investigates the empirical relationship between the aggregated and disaggregated components of fiscal policy variables and output growth in Nigeria using the vector error correction mechanism for the period of 1981 to 2021. This formulation was an improvement over previous empirical studies of the impact of public finance instruments on output growth in Nigeria. Our findings suggest that fiscal policy instruments (total expenditure, capital expenditure, recurrent expenditure, total revenue, primary fiscal balance, domestic debt, external debt, oil tax revenue, and non-oil tax revenue) exert significant impacts on output growth and most of the empirical results obtained support the hypothesised relationships between public finance instruments and output growth in Nigeria. However, aggregate federal government expenditure exerts a significant negative impact on output growth, while on the disaggregated scale, capital expenditure exerts a significant positive influence on output growth whereas recurrent expenditure exerts a significant negative impact on output growth. Given these findings, we recommend that the government of Nigeria, through its fiscal authorities, should adopt growth-enhancing fiscal policies that would engender macroeconomic stability and will be potent in refocusing recurrent expenditure towards ensuring productivity growth.

Early warning of systemic risk in stock market based on EEMD-LSTM.

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

Commercial banks’ capital structure and performance in Vietnam: Panel data model approach

The study delves into the factors that influence the capital structure of commercial banks in Vietnam. Capital structure, measured by the net debt-to-asset ratio, is a key indicator of bank leverage. The study uses pooled OLS, fixed effects, and random effects models to analyze the impact of factors such as net interest-earning rate, Corporate Income Tax rate, and liquidity on commercial banks’ profitability and their capital structure. Data from 26 banks during the 2010–2022 period in Vietnam was collected for the analysis. The results indicate that factors such as Tax, ROA, growth, and liquidity have significant impacts on the capital structure of the banks. Specifically, Tax has a beta coefficient of –0.05967, ROA has a beta of –0.01796, growth has a beta of 0.000509, and liquidity has a beta of –0.00045. The study found that ROA, Tax, and liquidity are negatively related to the capital structure of Vietnamese commercial banks, meaning that an increase in these factors leads to a decrease in the banks’ total debt-to-total assets and vice versa. The empirical results suggest that commercial banks can manage their capital structure through these factors to reduce their debt-to-asset ratio, resulting in reduced credit risk, improved asset quality, and increased business efficiency. However, lowering the debt-to-asset ratio may also lead to reduced profits from lending activities, particularly when interest rates are high.

Monetary value of health-a practical decision-making framework combining equity considerations and WTP.

We estimate the first monetary value of a health gain in Croatia to inform the debate about the appropriate "demand-side" cost-effectiveness thresholds in Croatia but also Central and Eastern Europe, where such debates are still uncommon. We test the empirical support for two equity considerations: age and severity operationalized as proportional shortfall (PS), and propose a pragmatic framework for combining equity considerations with the monetary value of health into a single threshold. We used the contingent valuation method to elicit the willingness to pay per Quality-Adjusted Life Year (QALY) in Croatia, using a representative sample of the population (N = 1,500, online survey). 29 EQ-5D health states were valued using payment scales and open-ended question as payment vehicles. To test the hypotheses, we used both parametric tests and non-parametric tests. Multilinear regression was employed to test the theoretical validity of the results. The monetary value of a health gain in Croatia is equivalent to 1.15 of GDP per capita (equaling €17,000). Age of patients seems to be an important equity-related characteristic. The WTP per QALY in the age-neutral risk group (€11,900) was nearly equivalent to the WTP per QALY in the adult (neutral) risk group (€11,700) but lower by 16% compared to the WTP per QALY estimated in children (€14,200; p = 0.00). WTP estimates are theoretically valid and to, a small degree, scale sensitive. There is a positive association between the level of proportional shortfall and willingness to pay. To increase the usefulness of our results for the policy-makers, we combine the elicited preferences into a single decision-making framework and construct several cost-effectiveness thresholds based on willingness to pay and equity-related preferences. Based on empirical results, cost-effectiveness thresholds could range up to €20,308 for the most severe health conditions in children or could be lowered to €16,777 for less severe health conditions. In Central and Eastern Europe, in spite of a growing understanding of the importance of further developing value-based assessment frameworks there has been very little empirical research to guide, inform and promote this development. Countries in this region use mainly GDP-based thresholds without empirical evidence to support such important decisions. This may lead to thresholds that are too high, with detrimental consequence for the pricing and reimbursement systems.

Uncertainty, precautionary saving, and investment: Evidence from prescheduled election cycles

AbstractWe show empirically that firms increase cash holdings starting as early as one year before prescheduled (i.e., predictable) elections. Then, for four quarters around elections when uncertainty and external financing costs are high, firms decrease investment and draw down saved cash balances to avoid tapping external financing. We use a dynamic model of firm investment and saving to demonstrate the importance of anticipation of future financing costs to firms' pre‐election precautionary saving behavior. Theoretically, if election uncertainty were only to affect potential investment, lower firm investment would result in higher contemporaneous cash balances, which is inconsistent with our empirical results.

Estimation of industry-level basic digital capital services in China: A variable depreciation rate estimation method based on DSGE

Accurately measuring the input of digital capital is of great significance for understanding the development pattern of China's digital economy, nurturing new quality productivity, and achieving high-quality development. However, existing representative studies often assume a constant depreciation rate for capital goods, which does not align with reality. In this paper, we address this limitation by introducing dynamic stochastic general equilibrium (DSGE) model that estimates the variable depreciation rate for each capital good. By setting the depreciation rate as a function of capital maintenance expenditures and utilization rates, we provide a more realistic approach. The empirical results obtained through Monte Carlo simulation demonstrate that the trend of depreciation rates for all types of capital goods aligns with China's economic development. Additionally, we find that investment-specific technology shock plays a significant role in affecting changes in the depreciation rates of capital goods. This shock leads to an increase in capital utilization rates and a decrease in capital maintenance expenditures, resulting in higher depreciation rates. Notably, the depreciation rate of basic digital capital is more sensitive to exogenous shock compared to non-digital capital. Furthermore, the paper estimates the basic digital capital service of 19 industries in China from 1981 to 2020. The results indicate a steady increase in the services provided by basic digital capital, with particularly rapid growth observed in industries such as information transmission, software and information technology services, and the financial sector. When comparing our estimation results with those of representative literature, we find that the national-level productive capital stock and the basic digital capital services estimated using our methodology closely align with existing literature. This validation confirms the effectiveness of our methodology and the reliability of the estimated industry-level basic digital capital services.

Interactive effects of AI awareness and change-oriented leadership on employee-AI collaboration: The role of approach and avoidance motivation

AI application has been met with great concern in the hospitality field given its efficacy enhancement but threats to employee tasks and jobs. This study investigates the interactive impacts between AI awareness and change-oriented leadership on hospitality employees' protective behaviors, including whether and how the interactions influence employees’ collaboration with AI. A two-wave survey of hotel employees and an experiment of potential hotel practitioners were conducted with 478 and 252 valid respondents respectively. Empirical results show how the interactions predicted employee-AI collaboration via the two motivation types of approach and avoidance, as per protection motivation theory (PMT). The results offer change-oriented leadership as a coping strategy for employees to overcome perceived AI threats and engage in active collaboration with AI. In addition, the study reveals the positive role of AI awareness in embracing change and discusses the theoretical contributions and practical implications of AI-adopted hospitality.

Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application

This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization problems. Firstly, the perturbation conditions in the “teaching” and “learning” stages of the original TLBO algorithm are interpreted geometrically, based on which the search capability of the TLBO is enhanced by adjusting the range of values of random numbers. Second, a strategic restructuring has been ingeniously implemented, dividing the algorithm into three distinct phases: pre-course self-study, classroom blended learning, and post-course consolidation; this structural reorganization and the random crossover strategy in the self-learning phase effectively enhance the global optimization capability of TLBO. To evaluate its performance, the BLTLBO algorithm was tested alongside seven distinguished variants of the TLBO algorithm on thirteen multimodal functions from the CEC2014 suite. Furthermore, two excellent high-dimensional optimization algorithms were added to the comparison algorithm and tested in high-dimensional mode on five scalable multimodal functions from the CEC2008 suite. The empirical results illustrate the BLTLBO algorithm’s superior efficacy in handling high-dimensional multimodal challenges. Finally, a high-dimensional portfolio optimization problem was successfully addressed using the BLTLBO algorithm, thereby validating the practicality and effectiveness of the proposed method.

Enhanced bi-level optimal scheduling strategy for distribution network with multi-microgrids considering source-load uncertainties

With the increasing integrations of renewable energy resources into distribution networks (DNs) and microgrids (MGs), the imperative for an effective market scheduling mechanism becomes paramount to enhance the operational safety, reliability, and economic efficiency of distribution grids. Taking advantage of bi-level programming theory, this study meticulously formulates a comprehensive optimization scheduling model for the multi-MGs distribution network. The upper-level optimization objective is to minimize both the operational losses and total costs of the DN. Concurrently, the lower-level optimization pursues the maximization of daily operational revenue for MGs. Recognizing the pervasive impact of the inherent uncertainty associated with renewable energy sources on system safety and reliability, a cutting-edge scenario-based stochastic planning framework is introduced. The methodology integrates a heuristic matrix matching approach to effectively handle the intricate challenges posed by uncertainties from wind and photovoltaic generations. Moreover, in addressing the proposed nonlinear models, a sophisticated method is employed, utilizing the second-order cone relaxation and linearization methods. These methods meticulously transform the upper and lower-level models into second-order cone planning and mixed-integer linear programming issues, respectively. Finally, the proposed methodologies are rigorously scrutinized and validated with intricate case studies, providing a nuanced understanding of their efficacy. The empirical results underscore the theoretical feasibility and superiority of the proposed scheduling scheme. Notably, the operational performance of the DN as well as the economic viability of multiple MGs can also be significantly improved.

Who Suffers the Most During the COVID‐19 Pandemic? Evidence From Thailand

This study investigates Thailand's recent labor market disruption induced by the coronavirus disease 2019 (COVID‐19) pandemic using its Labor Force Survey data from 2018 to 2021. We provide empirical evidence to validate the varied effects among different demographic categories. Workers that are the most adversely affected by the COVID‐19 pandemic are (1) in high‐risk sectors, (2) less educated, (3) young workers, and (4) parents. Our empirical results show that the unemployment rate positively relates to marital status and sectorial risk levels—that is, married and public sector employees are less likely to be unemployed. Furthermore, less occupational flexibility and parenthood decrease wages, and these effects are more prominent for women, suggesting the country's “she‐cession.”

Portfolio management based on a reinforcement learning framework

AbstractPortfolio management is crucial for investors. We propose a dynamic portfolio management framework based on reinforcement learning using the proximal policy optimization algorithm. The two‐part framework includes a feature extraction network and a full connected network. First, the majority of the previous research on portfolio management based on reinforcement learning has been dedicated to discrete action spaces. We propose a potential solution to the problem of a continuous action space with a constraint (i.e., the sum of the portfolio weights is equal to 1). Second, we explore different feature extraction networks (i.e., convolutional neural network [CNN], long short‐term memory [LSTM] network, and convolutional LSTM network) combined with our system, and we conduct extensive experiments on the six kinds of assets, including 16 features. The empirical results show that the CNN performs best in the test set. Last, we discuss the effect of the trading frequency on our trading system and find that the monthly trading frequency has a higher Sharpe ratio in the test set than other trading frequencies.

The Impact of Corporate Social Responsibility on Labor Investment Efficiency: Evidence from China

This study examines the impact of corporate social responsibility (CSR) on labor investment efficiency utilizing a sample of China’s listed companies. The empirical results demonstrate that CSR improves labor investment efficiency, and the effect is significant in terms of both overinvestment and underinvestment. Findings from cross-sectional tests indicate that CSR has a more significant effect on labor investment efficiency in non-state-owned firms and firms with more financing constraints or higher labor adjustment costs. The conclusion is robust after utilizing a 2SLS regression, replacing indicators for labor investment efficiency and accounting for the impact of non-labor investment. In general, the results support stakeholder theory and confirm that CSR can enhance external monitoring and improve firms’ investment behavior.

Impact of new maize variety adoption on yield and fertilizer input in China: Implications for sustainable food and agriculture

CONTEXTCrop variety adoption is considered as one of the main strategies for farmers to enhance productivity under stress brought by the climate change, with less environmental impacts. It holds an upstream position in the whole life cycle of agricultural clean production, compared to the field management and agricultural non-point source pollution treatment. OBJECTIVEThis paper aims to (i) assess the impact of new maize variety adoption on maize yield and fertilizer input; (ii) analyze the main determinants of farmers' new maize variety choice in the context of climate change, and (iii) identify the heterogeneity of small-scale and large-scale farmers in maize variety choice and its impacts. METHODSBased on plot-level data collected from three major grain producing provinces in China, we employ a treatment effects model to correct the potential bias resulting from the farmers' self-selection and estimate the direct impacts of farmers' new maize variety adoption and the main determinants of such adoption. RESULTS AND CONCLUSIONSThe empirical results indicate that the adoption of new maize varieties significantly increases maize yield by 9.4% but fertilizer use by 16.6 kg/mu (1 mu = 1/15 ha) as well, and this trade-off between economic impacts and environmental impacts is more pronounced among small-scale farmers. However, in the face of natural disasters, adopting new maize varieties results in a significant reduction in yield losses by 1.4% and a decrease in fertilizer input for natural disaster management by 2.45 kg/mu, as compared to the adoption of old varieties. What's more, our findings suggest that a higher frequency of pest and disease disasters significantly increases the probability of new maize variety adoption by farmers, while a higher frequency of climate disasters significantly decreases the probability. SIGNIFICANCEThe estimated impacts of new maize varieties point out the need of synergistically optimized resistance and nutrient use efficiency for crop breeding, which have important policy implications for sustainable food and agriculture. Also, the significant factor analysis contributes to the proposal of feasible ways to promote the farmers' adoption behavior.

Geopolitical risk, supply chains, and global inflation

AbstractThis paper investigates the effects of global geopolitical risks and global supply chain pressures on global inflation for the monthly period of 1999M1–2022M12. The investigation is based on a structural vector autoregression model, where the effects of global oil prices and global monetary policy are controlled for. Four alternative measures of inflation are used, including headline, core, food, and energy inflation. The empirical results show that disruptions in global supply chains are the main drivers of global inflation in the long run as the corresponding shocks explain the lion's share of volatilities in headline inflation (by 32%), core inflation (by 30%), and food inflation (by 22%), followed by oil price shocks and policy rate shocks. In comparison, energy inflation is explained the most by oil price shocks (by 55%) followed by supply chain shocks and policy rate shocks. Positive supply chain pressure and oil price shocks have positive and statistically significant effects on headline inflation even after five years, whereas positive policy rate shocks have negative and statistically significant effects on headline inflation in the long run. In contrast, positive shocks to geopolitical risk result in higher headline inflation only up to one year, with insignificant effects in the long run. Several policy implications follow.

Micro-Grinding Parameter Control of Hard and Brittle Materials Based on Kinematic Analysis of Material Removal

This article explores the intricacies of micro-grinding parameter control for hard and brittle materials, with a specific focus on Zirconia ceramics (ZrO2) and Optical Glass (BK7). Given the increasing demand and application of these materials in various high-precision industries, this study aims to provide a comprehensive kinematic analysis of material removal during the micro-grinding process. According to the grinding parameters selected to be analyzed in this study, the ac-max values are between (9.55 nm ~ 67.58 nm). Theoretical modeling of the grinding force considering the brittle and ductile removal phase, frictional effects, the possibility of grit to cut materials, and grinding conditions is very important in order to control and optimize the surface grinding process. This research introduces novel models for predicting and optimizing micro-grinding forces effectively. The primary objective is to establish a micro-grinding force model that facilitates the easy manipulation of micro-grinding parameters, thereby optimizing the machining process for these challenging materials. Through experimental investigations conducted on Zirconia ceramics, the paper evaluates a mathematical model of the grinding force, highlighting its significance in predicting and controlling the forces involved in micro-grinding. The suggested model underwent thorough testing to assess its validity, revealing an accuracy with average variances of 6.616% for the normal force and 5.752% for the tangential force. Additionally, the study delves into the coefficient of friction within the grinding process, suggesting a novel frictional force model. This model is assessed through a series of experiments on Optical Glass BK7, aiming to accurately characterize the frictional forces at play during grinding. The empirical results obtained from both sets of experiments—on Zirconia ceramics and Optical Glass BK7—substantiate the efficacy of the proposed models. These findings confirm the models’ capability to accurately describe the force dynamics in the micro-grinding of hard and brittle materials. The research not only contributes to the theoretical understanding of micro-grinding processes but also offers practical insights for enhancing the efficiency and effectiveness of machining operations involving hard and brittle materials.

Privatisation and government spending efficiency: An empirical analysis in Europe

AbstractAcademic literature has made an effort to demonstrate the positive effects of privatisation reforms on government performance and economic growth. However, there is no sufficient evidence to support the benefits of privatisation in terms of government spending efficiency. This study analyses the correlation between privatisation and government spending efficiency. Our empirical results do not support a positive effect of privatisation on government spending efficiency. These findings are relevant because they suggest that, although privatisation has been usually seen as a tool to balance public finances, it does not mean that government spending efficiency will be higher after privatising State‐owned enterprises.

A personalized product recommendation model in e-commerce based on retrieval strategy

In recent years, online shopping is one of the routine parts in people’s life. It is convenient and takes less effort to purchase it. Regarding the increasing revolution of e-commerce businesses, recommendation engine plays a crucial role in them. Recommendation engines are very popular and easy to implement to their platform nowadays. Due to the extremely high competition of e-commerce businesses, the operation needs to integrate the recommender wisely. This study presents a comprehensive approach to improving user experience and engagement on e-commerce platforms through the implementation of an implicit personalized product recommendation engine. Collaborating with the H&M Group, the research combines the strength of each recommending algorithms which are collaborative filtering, popularity, and Bayesian personalized ranking to develop a robust recommendation system. By leveraging a retrieval strategy that combines multiple algorithmic techniques and evaluating candidates using machine learning models which comprise LightGBM and Deep Neural Network, the study achieves promising results. The authors utilize two popular technical metrics to evaluate their models which are mean average precision at K candidates (MAP@K) and mean average recall at K candidates (MAR@K). The empirical result indicates that the LightGBM model has remarkable performance than Deep Neural Network model, which are 0.06 versus 0.02 respectively in MAP@K and 0.03 versus 0.01 respectively in MAR@K when both recommending ways is at 50 items. Overall, this research contributes a novel framework that addresses the challenges of analyzing large-scale data, cold-start problems, and personalization, thereby enhancing the user experience, and driving sales on e-commerce platforms.