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Drawing upon the cognitive-affective processing system (CAPS) framework, the current study proposes a dual-pathway model that suggests self-serving leadership has a positive influence on employee knowledge hiding. The study also examines the mediating effects of relative deprivation and emotional exhaustion, as well as the moderating effect of political skill, to provide a comprehensive understanding of these relationships.

This study employed two-wave time-lagged survey data collected from 644 employees in 118 teams within a company based in Shenzhen, China. Moreover, hierarchical linear modeling (HLM) was used to test the hypothesized relationships.

The results indicated that self-serving leadership positively influenced employee knowledge hiding, and this relationship was mediated by relative deprivation and emotional exhaustion. Additionally, political skill was found to negatively moderate both the direct relationship between self-serving leadership and relative deprivation and emotional exhaustion, and the indirect path from self-serving leadership to employee knowledge hiding through relative deprivation and emotional exhaustion.

This study makes a unique contribution to the knowledge management literature in several ways. First, it introduces self-serving leadership as a predictor of employee knowledge hiding, expanding the current understanding of this phenomenon. Second, it offers a novel conceptualization, suggesting that employees coping with self-serving leadership may experience relative deprivation and emotional exhaustion, and these factors can predict their engagement in knowledge hiding. Third, the research findings on the moderating role of political skill push the boundaries of the knowledge-hiding literature, providing new insights into the conditions under which this behavior occurs.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

This paper aims to emphasize economic complexity, tourism, information and communication technology (ICT), renewable energy consumption and foreign direct investment (FDI) as the determinants of carbon emissions.

These economies rely on the tourism sector, and Asian countries rank among the top tourism economies worldwide in terms of tourism receipts. This study uses a series of empirical estimators, i.e. cross-sectional augmented auto-regression distributive lag and panel cointegration, to validate the main hypotheses.

The econometric results confirm an inverted U-shaped association between economic complexity and carbon emissions, validating the economic complexity index induced environment Kuznets curve hypothesis for the selected Asian economies.

Finally, the empirical results admit articulating some imperative policy suggestions to attain a sustainable environment on behalf of outcomes.

Furthermore, ICT and renewable energy consumption are environment-friendly indicators, while FDI and the international tourism industry increase environmental pressure in selected countries. In addition, this study also explores the interaction between renewable energy and ICT with FDI and their effects on carbon emissions. Interestingly, both interaction terms positively respond to the environmental correction process.

Because ICT with FDI may not reduce environmental pollution unless the energy used in FDI projects is greener. Moreover, in Asian economies, industrial and other sectors could increase environmental quality via the role of ICT in FDI.

这些经济体依赖旅游业, 就旅游收入而言, 亚洲国家在全球旅游经济体中名列前茅。本研究使用一系列经验估计量, 即 CS-ARDL 和面板协整来验证我们的主要假设。

本文强调经济复杂性、旅游、信息和通信技术 (ICT)、可再生能源消费和外国直接投资 (FDI) 作为碳排放的决定因素

计量经济学结果证实了经济复杂性与碳排放之间的倒 U 型关联, 验证了 ECI 对选定亚洲经济体的环境库兹涅茨曲线 (EKC) 假设。

最后, 实证结果承认阐明了一些必要的政策建议, 以代表结果实现可持续环境。

此外, 信息通信技术和可再生能源消耗是环境友好型指标, 而外国直接投资和国际旅游业增加了选定国家的环境压力。此外, 本研究还探讨了可再生能源和 ICT 与外国直接投资之间的相互作用及其对碳排放的影响。有趣的是, 这两个交互项都对环境校正过程做出了积极响应。

ICT 与 FDI 可能不会减少环境污染, 除非 FDI 项目中的能源使用更环保。此外, 在亚洲经济体中, 工业和其他部门可以通过 ICT 在 FDI 中的作用提高环境质量。

环境库兹涅茨曲线; 外商直接投资;信息和通信技术; 可再生能源;旅游;亚洲主要旅游经济体

文章类型： 研究型论文

This study investigates the relationship between urbanization and carbon dioxide emission in the Central African Economic and Monetary Community from 1990 to 2019. The literature reveals that the relationship between urbanization and carbon dioxide emissions is still debatable and the existing findings are inconclusive.

Carbon dioxide is the regressand; while, urbanization, gross domestic product (GDP) and financial development (FD), rule of law (ROL) and government effectiveness (GEF) are the regressors. Johansen Fisher and Kao residual co-integration tests alongside the fully modified and dynamic ordinary least squares.

The results show a significant positive relationship between urbanization and carbon dioxide emissions. The causality tests results show that carbon dioxide granger causes urbanization, GDP and FD unit directionally.

The countries' governments should effectively improve their legal systems to regulate carbon dioxide emissions. Urbanization laws should be implemented to limit urbanization environmental deteriorating effects on carbon dioxide emissions. This occurs as the countries practiced unregulated urbanization which increases population's environmental impacts. The study recommends sustainable green urbanization policies for environmental conservation through tree planting and horticulture. Balance development in urban and rural areas is vital to decongest the urban cities' pressure in the states. The governments should motivate the private sector with rural investments captivating policies to limit rural urban migration.

The findings contribute value by supporting a positive link between urbanization and carbon dioxide emissions in the CEMAC zone. The causality tests findings confirm the view that carbon dioxide granger causes urbanization, GDP and FD unit directionally. This value addition is essential to the governments and policy makers to mitigate urbanization and carbon dioxide emissions in the CEMAC region.

The purpose of this paper is to present a variable structure multiple model adaptive estimator (VSMMAE) for liaison navigation system. Liaison navigation is an autonomous navigation method where inter-satellite range measurements are used to estimate the orbits of all participating spacecrafts simultaneously.

To overcome the problem caused by an inaccurate initial state, a navigation algorithm is designed based on the multiple model adaptive estimation technique. The multiple models are constructed by different initial error covariance matrices. To reduce the computational cost, the likely-model set (LMS) algorithm is adopted to eliminate the unlikely models.

It is specified that the performance of the liaison navigation based on the extended Kalman filter (EKF) is sensitive to the initial error. Simulation results show that the VSMMAE outperforms the EKF in the presence of a large initial error.

The presented algorithm is applicable to spacecraft autonomous navigation.

A novel navigation algorithm based on the VSMMAE is developed. It is an effective method for the liaison navigation system.

The aim is to analyze the European eco-management because the global warming has become a topical issue impacting the whole world. Individual countries are trying to minimize all the catalysts of global warming, such as carbon emissions. This paper addresses this issue and analyzes the performance of European eco-management for the purpose of future energy investments being environmentally.

This paper develops a fuzzy decision-making model to study the performance indicators of selected countries based on EMAS III standard. It employs interval type-2 fuzzy DEMATEL to evaluate the performance factors and TOPSIS methodology to assess five selected European countries' performance in relation to eco-friendly, emission and renewable energy.

Eco-friendly energy plays the most critical role in this respect followed by emissions and renewable energy which constitute significant factors. The novelty of this study is identifying significant criteria regarding environmental and energy efficiency of investments and making performance assessments of European countries with a new fuzzy decision-making model. Both expert opinions and datasets are used for the analysis. This paper supports previous research about energy efficiency investments in Europe.

The innovative feature of this study is identifying significant criteria regarding environmental and energy efficiency of investments and assessing the performance of European countries with a new fuzzy decision-making model. The fact that the analysis only concerns the European region is an important limitation. In future analyses, other groups of countries can be examined. Innovations can be made regarding the method applied. In this context, analyses can be done utilizing different fuzzy numbers. Finally, the importance of the criteria can be calculated with other methods such as SWARA.

The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments is done in this manuscript.

Analysis of European eco-management performance was done for environmentally friendly and efficient energy investments. A fuzzy decision-making model is constructed. The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments.

In this study, the author intend to investigate the impacts of renewable energy use and environmental taxation on sustainable development measured by the adjusted net savings (ANS).

This study employs the quantile regression (QR) for a set of 24 Organization for Cooperation and Economic Development (OECD) countries over the period 1994–2018.

The main empirical findings of estimates show that access to renewable energy and environmental taxation generate positive and significant effects in increasing the ANS for most quantiles. Hence, they are practical tools for achieving sustainable development goals (SDGs).

This study has important implications for governments and policymakers of the OECD countries. Therefore, governments can use subsidies and incentives to promote the adoption of renewable energy sources, energy-efficient technologies and sustainable practices. Similarly, by imposing taxes on pollution and resource use, governments can encourage the adoption of cleaner technologies and practices toward more sustainable behavior.

This paper is based on a novel measure of sustainable development (ANS) and a novel econometric method (QR).

The purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining.

We employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTC–CPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the data’s inherent properties.

The findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the “anticipatory regulatory decline effect”, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use.

This study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the “anticipatory regulatory decline effect” and the “mining-driven regulatory surge”, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.

This study aims to examine the environmental impact of fossil fuel use in newly industrialized countries (NICs), focusing on the relationship between economic growth, energy consumption and environmental sustainability from 1971 to 2020.

The research uses pool mean group autoregressive distributive lag, fully modified ordinary least squares and dynamic ordinary least squares econometric models to analyze long-run data. These methods enable a detailed assessment of how economic and industrial factors affect environmental pollution.

The study finds that nonrenewable energy consumption is positively linked to environmental pollution, with a 1% increase leading to a 6.25% rise in pollution. Industrialization, urbanization and globalization also significantly increase pollution, with increments of 2.51%, 3.97% and 2.11%, respectively. Conversely, economic growth has a beneficial effect, reducing pollution by 2.59% for every 1% increase in growth.

Policymakers should balance economic growth with environmental sustainability by reducing nonrenewable energy consumption and supporting renewable energy adoption.

This research provides fresh insights into the dynamics between economic growth and environmental pollution in NICs. By using advanced econometric techniques over an extended period, it offers a view of how economic and industrial activities influence environmental outcomes, highlighting the dual role of economic growth in both promoting development and reducing pollution.

The purpose of this study is to map the intellectual structure of sustainable finance and accounting (SFA) literature by identifying the influential aspects, main research streams and future research directions in SFA.

The results are obtained using bibliometric citation analysis and content analysis to conduct a bibliometric review of the intersection of sustainable finance and sustainable accounting using a sample of 795 articles published between 1991 and November 2023.

The most influential factors in the SFA literature are identified, highlighting three primary areas of research: corporate social responsibility and environmental disclosure; financial and economic performance; and regulations and standards.

SFA has experienced rapid development in recent years. The results identify the current research domain, guide potential future research directions, serve as a reference for SFA and provide inspiration to policymakers.

SFA typically encompasses sustainable corporate business practices and investments. This study contributes to broader social impacts by promoting improved corporate practices and sustainability.

This study expands on previous research on SFA. The authors identify significant aspects of the SFA literature, such as the most studied nations, leading journals, authors and trending publications. In addition, the authors provide an overview of the three major streams of the SFA literature and propose various potential future research directions, inspiring both academic research and policymaking.