Socio-economic Planning Sciences最新文献

The flux of economic policy uncertainty casts a long shadow over the ecological footprint, amplifying the risk and the urgency for strategic planning that harmonizes economic stability with environmental stewardship. This complex interplay necessitates a reassessment of how economic strategies can be sculpted to mitigate environmental risks and foster sustainability. This research evaluates the asymmetric impact of economic policy uncertainty on the ecological footprints in the top 10 polluted nations (China, USA, India, Russia, Japan, Brazil, Germany, Indonesia, France, and Mexico) from 2001 to 2022. Prior studies relied on panel data methods without accounting for the unique features of individual economies. Conversely, the present investigation implements the Quantile-on-Quantile methodology to delve into the interconnection between the variables within each country. This sophisticated methodology enriches the exactness of our evaluation, providing a thorough worldwide standpoint and uncovering precise insights tailored to the distinctive attributes of each particular country. The outcomes disclose that economic policy uncertainties reduce ecological footprints across diverse quantiles in the selected economies. Additionally, the findings underscore multiple behaviors in these correlations across several quantiles. These results highlight the need for policymakers to take extensive measures to address the deviating impacts of economic policy uncertainty on ecological sustainability.

To improve the efficiency and viability of healthcare organizations, it is crucial to enhance both the quality of patient care and the well-being of healthcare staff, particularly nurses. They play a vital role as a key human resource in healthcare, and their well-being and job satisfaction are significantly influenced by effective scheduling. Improved scheduling practices not only enhance the quality of care provided but also contribute to better job satisfaction and overall well-being of the nursing staff. Despite the importance of this task, nurse scheduling is often conducted manually by head nurses, which is both challenging and time-consuming. This study proposes a mathematical model that addresses the nurse scheduling problem, with a specific application in a French hospital department. The objectives of the model are to accommodate a wide range of constraints flexibly, ensure balanced workloads and shifts, and align with the preferences of the nursing staff. A significant contribution of this research is the theoretical investigation and comparison of various workload balancing criteria tailored to the specific context of the problem. The study analyzes different workload balancing criteria to formulate an efficient scheduling solution. The effectiveness of the proposed approach is demonstrated through large experiments and practice feedback, confirming its potential to enhance both healthcare efficiency and nurses’ well-being.

Performance measurement of decision-making units (DMUs) with network structure is one of the main challenges in data envelopment analysis (DEA) field. The main purpose of this paper is to propose a novel network data envelopment analysis (NDEA) approach based on matrix of efficiency, common set of weights (CSW), multi-objective programming (MOP), and goal programming (GP) technique for performance measurement of peer DMUs in two-stage network structure. The advantages of the proposed NDEA approach can be summarized as follows: comparing all DMUs and sub-DMUs on the same base, considering all internal structures and relations and capability to extending this for all network structures, linearity of the proposed models, unique efficiency decomposing without any need to consider multiplicative, additive or leader-follower relations between overall and stages efficiency. To illustrate the usefulness and applicability of the proposed approach we applied it to a real application of non-life insurance companies in Taiwan.

Credit scoring models for non-traditional lending channels, such as peer-to-peer (P2P) lending platforms, are usually estimated only on the sample of accepted applicants. This may lead to biased estimates of the risk drivers. This issue can be addressed using a reject inference technique that includes the characteristics of rejected applicants in the model. Due to the low numbers of accepted applicants and default records, credit scoring models usually face a class imbalance problem. However, previous literature on sample selection models for credit scoring does not address the class imbalance issue. To fill this gap, we extend the Generalised Extreme Value (GEV) regression model for binary data to the sample selection framework. We consider the quantile function of the GEV distribution as a link function in both the selection and outcome equations. We use the copula function to model the dependence structure between the two equations for its flexibility. This proposal is called the Sample Selection Generalised Extreme Value (SSGEV) model and it is implemented in the R package BivGEV. We apply this model to a comprehensive dataset provided by Lending Club, and we show that parameter estimates obtained only on accepted P2P applicants are biased and coherently with the literature. The SSGEV model achieves a higher predictive accuracy than those obtained using univariate approaches or a sample selection probit model. Our proposal also provides more conservative estimates of the Value-at-Risk and the Expected Shortfall.

We propose a composite indicator for the assessment of tourism sustainability in European Union (EU) countries relatively to years 2019 and 2021. Our proposal is a revisitation of the Travel & Tourism Development Index (TTDI), which is based on a uniform weighting scheme, by means of the Multi-Directional Benefit of the Doubt (MD-BoD) model, an innovative non-compensatory method able to derive a country-specific preference structure from the data. Thanks to the MD-BoD model, each pillar and elementary indicator receives a score that reflects the potential improvement of the country relatively to an empirical benchmark, therefore it is possible to objectively identify weak and strong dimensions of tourism sustainability before and after the Covid-19 pandemic. As such, our composite indicator represents a valuable support for the design of policies aimed at increasing the resilience of the European tourism ecosystem.

This study analyzes the impact of Environmental, Social, and Governance (ESG) risk metrics on the financial performance of ETFs in the US and Europe from January 2020 to December 2023, particularly during the COVID-19 pandemic. It explores the influence of ESG investment trends on ETF performance and investor preferences between equities and bonds in response to ESG risk profiles. Results show a positive correlation between higher ESG standards and financial performance, measured by the Sharpe ratio, with a marked preference for bonds amidst increased ESG risks. This highlights the critical role of ESG considerations in investment strategies and risk management, providing insights for sustainable finance.

This paper aims to explore the correlation between individual income and tax evasion. We develop a novel theoretical model based on the argument that the level of public goods provision and the perceived fairness of public services significantly influence an individual's attitude towards taxation. The model reveals a concave pattern, indicating the presence of an income threshold. Initially, as an individual's income increases, tax evasion decreases due to the perceived fairness of their contributions relative to the benefits received from public services. However, beyond this threshold, the tax burden is perceived as unfair, leading to an increase in tax evasion as income levels rise. We also proof that this income threshold is influenced by the tax rate and enforcement system. We also conduct a panel data analysis on a sample of 35 OECD countries from 2007 to 2020 to support the theoretical findings. The empirical investigation confirms a U-shaped relationship between income and the underground economy. Our findings suggest that policymakers should introduce measures to improve the quality of public goods provision, considering the potential adverse effect of higher income on economic growth. This study is the first to theoretically investigate the non-linear relationship between income and tax evasion while also providing empirical evidence.

The integration of the Internet with traditional medical services is poised to transform health insurance policies. This study aims to explore the coordinating role of differentiated health insurance policies within the context of Internet healthcare. A four-stage sequential game decision-making model is developed within a queuing framework to address scenarios involving online patient referrals and misdiagnoses. The model begins by analyzing the equilibrium arrival strategy of patients, followed by the determination of optimal service capacity strategies for a nonprofit community health center (CHC) and optimal pricing strategies for a for-profit general hospital (GH). Additionally, the model describes an optimal differentiated subsidy strategy for the government aimed at minimizing total social costs. Analysis reveals that under certain conditions, an increase in the service price at GH relative to CHC can lead to a higher influx of online patients visiting GH in person. Furthermore, when the number of online patients exceeds a specific threshold, it not only prompts the government to increase the disparity in health insurance subsidies between the two hospital tiers but also encourages GH to reduce its service prices and offer free services to online patients. Numerical experiments explore the effects of government budgets, sharing ratios, and other variables on the system's equilibrium state, providing several managerial insights. Notably, when patients' misdiagnosis costs are partially covered, increasing GH's misdiagnosis cost-sharing ratio not only enhances the patient arrival rate but also enhances GH's profitability.

Environmental consciousness is a multi-dimensional construct that encompasses several dimensions related to pro-environmental attitudes, beliefs and behaviours. The academic literature has attempted to conceptualise and operationalise environmental consciousness over the last 20 years, resulting in a wide variety of measures.

However, the available measures are country-specific and with a predominant U.S. focus, based on convenience samples, and rather limited in terms of interpretability and external validity. To overcome these limitations, the present study develops an index of environmental consciousness at both the micro (consumer) and macro (country) levels, taking into account the four main dimensions of environmental consciousness: the affective, cognitive, active and dispositional dimensions. By analysing more than 27 000 “Eurobarometer 92.4” responses from consumers in the 28 EU Member States in 2019, this paper develops a comprehensive measure of consumer environmental consciousness that captures the heterogeneity across European countries. To assess the robustness of the index, the link between environmental consciousness and life satisfaction is also examined. The index is also compared with a big data-based index using Google Trends data on environmental search categories. The results show differences in environmental consciousness between European countries. The link between environmental consciousness and life satisfaction is also supported, in line with previous research in this area. Finally, the index appears to be strongly correlated with actual consumer search patterns on Google. The findings have implications for businesses willing to enter in new markets and policy makers on how to measure and assess environmental consciousness.