Quality Of Matter Research Articles

Do Emission Trading Systems, Green Technology, and Environmental Governance Matter for Environmental Quality? Evidence from the European Union

This study investigates how emission trading systems can diminish the ecological footprint in the European Union bloc by considering the contribution of eco-friendly technology and ecological governance through another market-based intervention (i.e., environmental tax). Using PQARDL in the 26 EU members for data during 2011–2021, the study demonstrates that carbon pricing emission trading systems successfully reduce emissions in medium and upper quantiles but have little influence on lower quantiles. As businesses and consumers face higher costs for carbon-intensive activities, they are encouraged to adopt cleaner technologies and more sustainable practices. Over time, this shift decreases overall carbon emissions and resource consumption, which helps lower ecological pressure. Additionally, Europe's robust regulatory framework and commitment to climate goals ensure that carbon pricing is enforced consistently, further amplifying its positive environmental impact. Eco-friendly technology innovation appears to be a powerful tool for achieving rapid environmental advantages, leading to a considerable reduction in environmental footprint across various levels. Climate-smart patents can significantly curb emissions within the ecosystem and help balance the economy and the environment. Environmental taxes work alongside carbon pricing and green patents to incentivize individuals and corporations to adopt more sustainable practices in the 26 EU members. By unraveling the multifaceted relationships underpinning emission trading systems, renewable energy, green patents, and environmental tax, this study provides actionable insights to guide strategic decisions and policy formulations to reduce the ecological footprint.

Human capital investments and income inequality in Nigeria: Does institutional quality matter?

The study examines the impact of human capital and institutional quality on income inequality from 1990 to 2021. This study adopts the Kuznets model as its theoretical framework. The unit root test revealed a mixed stationary in the variables which prompts the choice of Autoregressive Distributed Lag of which its bound test result shows that there is a cointegration in the variables. The long-run results show that government expenditure on education, government expenditure on health, government effectiveness, and domestic credit to the private sector have a positive but not significant effect on income inequality while in the short-run, government effectiveness and domestic credit to the private sector have a positive but not significant impact on income inequality. Moreover, only government expenditure on education affects income inequality in the Nigerian economy positively and significantly. Based on the findings revealed, the study recommends that the government should by all means and economic resources increase the budget of human capital to enhance the reduction of income inequality in the country and place more emphasis on providing education grants and incentives that encourage the reduction of brain drain.

Slovakiaʼs Roma population and unemployment: Does public transport quality matter?

Slovakiaʼs Roma population and unemployment: Does public transport quality matter?

COP28 roadmap: Do ICT, education, and renewable energy consumption matter for environmental quality? Quantile evidence for emerging economies

Renewable energy consumption (REC) is not the only potent tool that can ameliorate the difficulties associated with rising CO2 emissions and ecological distortions. Besides, the role of ICT penetration and education on environmental quality cannot be overshadowed by their contributions to economic growth and improved human well-being. This study scrutinizes the effects of ICT, education, and REC on the ecological footprint (EF) and CO2 emissions in the IMF-selected emerging economies. The study utilizes three variables for the ICT index, computed through principal component analysis. The augmented mean group estimator results suggest that ICT, education, and REC reduce the EF, whereas FDI contributes significantly to environmental degradation. On the flip side, ICT reduces CO2 emissions and EF in both the medium and upper quantiles. It suggests that ICT penetration heals the environment and that a higher level of ICT consumption could be a panacea for sustainable development in emerging economies. The causality results show evidence of a feedback causality between economic growth, REC, and CO2 emissions. However, a unidirectional causality flows from economic growth and FDI to EF. Finally, to maximize the benefits associated with the positive effects of REC and ICT on environmental betterment, several renewable energy and ICT policies have been mentioned and discussed.

Referee quality and article citations: a case study of a finance journal

ABSTRACT As a result of the open science movement, we take advantage of one of the few journals in business studies, the Journal of Financial Economics (JFE), which between 2009 and 2021 named some of their referees. This allows us to produce a novel hand-collected data set to explore the extent to which referee quality matters in identifying articles that will receive strong future citations. We find that prominent academics are more likely to identify articles that will exhibit a high number of citations. This relationship is most pronounced among referees who are in a relatively mature stage in their careers. These results indicate that high-quality researchers, especially those in a relatively mature stage, are more likely to identify the potential of an article. To maximize future citations, editors thus need to consider the research publication record of the referees when making their selections. Overall, we provide insights which are of interest and relevance across higher education research.

Rethinking energy poverty alleviation through financial inclusion: Do institutional quality and climate change risk matter?

The main aim of this paper is to determine the impact of financial inclusion on energy poverty alleviation. It also interrogates whether institutional quality and climate change risk significantly influence the financial inclusion-energy poverty alleviation link using balanced panel data from 34 Sub-Saharan African countries from 2004 to 2021. Evidence from Discroll-Kraay Fixed Effects and Two-Step Instrumental Variable Generalized Method of Moments (2SIV-GMM) depicts heterogeneous energy poverty-alleviating impact of financial inclusion, demonstrating that financial inclusion is more instrumental in lower-income than lower-middle-income countries. Also, the results indicate a significant positive moderating role of institutional quality and a detrimental effect of climate change risk on financial inclusion-energy poverty alleviation nexus. Nevertheless, Dynamic Panel Threshold Regression results reveal threshold effects of financial inclusion, institutional quality, and climate change risk on energy poverty alleviation. The paper professes that financial regulations in allocating green resources would aid in alleviating energy poverty in Sub-Saharan Africa.

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets

Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure. Using three population-based datasets (N=1813), we trained the machine learning models in the primary dataset (N=1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N=378, N=334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N=66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association. Sleep quality could predict individual DSS (r=0.43, R2=0.18, rMSE=2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r=0.67, R2=0.45, rMSE=2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS. Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS. The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.

Partner quality matters—overyielding in a maize/soybean mixture depends on the initiator of common mycorrhizal networks

Partner quality matters—overyielding in a maize/soybean mixture depends on the initiator of common mycorrhizal networks

Tourism-led growth hypothesis (TLGH) in Africa: does institutional quality matter?

Purpose While the tourism–growth nexus is one of the better researched themes in both tourism and economics literature, there is limited evidence on how institutional quality affects this link, especially in the context of the developing world. To address this gap, this study aims to investigate the tourism-led growth hypothesis (TLGH) through the lens of institutional quality, drawing on evidence from African economies. Design/methodology/approach This study adopts a longitudinal design, involving panel data on 43 countries over an 18-year period, and the data were analyzed using the difference generalized method of moments model. Findings Results indicated that international tourism earnings contributed to economic growth in the selected economies. However, institutional quality negatively moderated the tourism–growth relationship on the continent, particularly in low-income economies, while reinforcing this link in middle-income countries. Originality/value This study provides new insights into how the TLGH varies across different levels of institutional quality and development.

Promoting gender equality during uncertainty: which policies and institutional qualities matter?

Promoting gender equality during uncertainty: which policies and institutional qualities matter?

Impact of government expenditure on unemployment in Asian countries: does institutional quality matter?

Purpose Asian countries have had persistent unemployment levels. The purpose of this paper is to investigate the impact of government spending on unemployment. Furthermore, this paper investigates the moderating role of institutional quality on the government spending–unemployment nexus. Design/methodology/approach Using data from 35 Asian countries from 2000 to 2022, the dynamic ordinary least squares and fully modified ordinary least squares technique is used to tackle with aforementioned issue. In addition, pooled mean group estimation is applied to verify the robustness of the findings. Findings The results show that an increase in government expenditure and better institutions reduce the unemployment rate. Interestingly, the negative impact of government expenditure on unemployment will enhance and intensify with better institutional quality. Furthermore, trade openness and foreign direct investment decrease unemployment in Asian countries. The results are robust to various specifications. Practical implications Findings from this study provide important implications for governments. Governments should use public expenditure efficiently and enhance and improve institutional quality to reduce unemployment. Originality/value To the best of the author’s knowledge, this study pioneers the investigation of the moderating role of institutional quality in the relationship between government expenditure and unemployment in Asian countries.

Financial inclusion and the global net-zero emissions agenda: Does governance quality matter?

We inspect the impact of financial inclusion on carbon (CO2) emissions and the role that quality of governance plays using a global dataset from 119 countries between 2004 and 2020. We address endogeneity using the dynamic two-step generalized method of moment estimator and further test the robustness of results using the Driscol-Kraay estimator, which is crucial for addressing cross-sectional and temporal dependence. The findings showed that financial inclusion and quality of governance exhibited a positive and significant impact on CO2 emissions. Further analysis shows that the impact of financial inclusion and governance on CO2 emissions varies across countries at different stages of economic development. We also documented significant variations in the results across six geographical regions. Also, the evidence from moderation and marginal effect analysis revealed that the marginal effect of financial inclusion on CO2 emissions is contingent on governance quality and that improving governance quality (scores) conditions financial inclusion to minimize CO2 emissions across the globe. We, therefore, demonstrate that in countries with relatively higher (better) governance scores, such as the United Kingdom, Denmark, Singapore, and New Zealand, financial inclusion significantly mitigates CO2 emissions compared to countries with relatively lower (weak) governance scores, such as Iraq, Zimbabwe, Ghana, and Chad. Finally, mediation analysis also highlights that the effect of financial inclusion on CO2 emissions is mediated by renewable energy consumption, industrialization, and household consumption. The policy implications are discussed.

Institutional quality, the level of development and Japanese outward foreign direct investment

This paper studies the determinants of Japanese direct investment worldwide and in different country groups. We derive our research hypotheses from the Knowledge-Capital Model and test them empirically using the PPML model and statistical data for 179 target countries between 1995 and 2019. We examine the role of a host’s institutional quality in affecting the cost of FDI. We show that institutional quality matters in attracting Japanese FDI for developed, developing, and transitioning economies. We find the effect of institutional quality to be more pronounced in the latter group of countries, especially concerning industries characterized by high asset-specificity.

Does small firm dynamics matter for occupation diversity and job quality? Evidence from Brazil

Recent studies have explored the role of firm dynamics and the connection to job reallocation. However, limited attention has been devoted to the role of micro and small firms in this process, particularly about the quality of the jobs generated. Therefore, this article aims to measure the impact of micro and small firm dynamics on occupation diversification in Brazil, with specific emphasis on job quality. The study used data from 558 microregions within the Brazilian industrial sector between 2003 and 2015. Panel data were applied to three econometric models: Feasible Generalized Least Squares (FGLS), Driscoll–Kraay (DK) and Instrumental Variable (IV) models for robustness analysis, including Two-Stage Least Squares (2SLS), Limited Information Maximum Likelihood (LIML), and the Generalized Method of Moments with Continuously Updating Estimators (GMM-CUE). Our main finding reveals that the dynamics of micro and small firms positively impact occupation diversity in Brazil, leading to the creation of a wider range of job types. Furthermore, the frequency of change of firms from microenterprises to small businesses increases the occupation diversity in the Brazilian industrial sector. Our findings are significant in providing policy recommendations for developing countries to achieve a more diverse labor market.

Income inequality, governance quality, and environmental degradation in Asian countries: Does interaction of governance quality matter? Evidence from panel data

The objective of this research is to examine the effects of income inequality, governance quality, and their interaction on environmental quality in Asian countries. Time series data are obtained from 45 Asian countries for the period 1996–2020 for this empirical analysis. The research has performed various econometric tests to ensure the robustness and reliability of the results. We have addressed different econometric issues, such as autocorrelation, heteroskedasticity, and cross-sectional dependence, using the Driscoll-Kraay (DK) standard error estimation and endogeneity issues by the system generalized method of moments (S-GMM). The results of the study revealed that income inequality and governance quality have a positive impact on environmental degradation, while the interaction of governance quality with income inequality has a negative effect on it. In addition, economic growth, population growth, urbanization, and natural resource dependency are found to deteriorate the quality of the environment. The findings of the study offer insightful policies to reduce environmental degradation in Asian countries.

A step towards sustainable environment in OECD countries: Do natural resource depletion, resource tax, institutional quality, and green innovation matter?

AbstractProtecting and improving environmental quality is essential for sustainable development. This study considers natural resource depletion, resource tax, institutional quality, and green innovation, which are likely to impact environmental quality. These issues are addressed by Sustainable Development Goals (SDGs) 7, 12, and 18. The study aims to uncover the influence of the variables we have considered on environmental quality in a sample of 18 Organisation for Economic Co‐operation and Development (OECD) countries from 1994 to 2020. Before panel data analysis, we conducted preliminary tests to determine the most suitable techniques. The econometric procedure comprises four stages: unit root analysis, cointegration analysis, estimation of long‐run coefficients using two different methods, and panel causality analysis. The empirical findings suggest that resource tax and green energy innovation positively impact environmental quality for the panel in the long run. Results based on the country show that institutional quality has a negative impact on environmental quality in Portugal but a positive impact in Luxembourg. Although resource taxes positively impact environmental quality in Portugal, they harm Luxembourg. Lastly, natural resource depletion negatively impacts environmental quality in Luxembourg. There are no statistically significant results for other countries. The study concludes with policy recommendations.

Tax revenue instability in Sub‐Saharan Africa: Does institutional quality matter?

AbstractSub‐Saharan African (SSA) nations face a dual fiscal challenge: the need to increase tax revenue mobilization and to manage revenue instability. This paper examines the causal impact of institutional quality on tax revenue stability in SSA from 2000 to 2020. Using UNU‐WIDER data and the System GMM technique, the study finds that institutional quality reduces tax revenue instability, with a stronger effect on indirect tax revenues than on direct taxes. This result is robust to changes in the measure of tax revenue instability, the measure of quality of institutions, the sample, and the time horizon. The effect is particularly pronounced in resource‐rich countries and in countries experiencing economic growth. The article's results are relevant to tax policy, specifically the necessary adjustments to improve tax revenue stability through institutional quality.

Better performance of deep learning pulmonary nodule detection using chest radiography with pixel level labels in reference to computed tomography: data quality matters

Labeling errors can significantly impact the performance of deep learning models used for screening chest radiographs. The deep learning model for detecting pulmonary nodules is particularly vulnerable to such errors, mainly because normal chest radiographs and those with nodules obscured by ribs appear similar. Thus, high-quality datasets referred to chest computed tomography (CT) are required to prevent the misclassification of nodular chest radiographs as normal. From this perspective, a deep learning strategy employing chest radiography data with pixel-level annotations referencing chest CT scans may improve nodule detection and localization compared to image-level labels. We trained models using a National Institute of Health chest radiograph-based labeling dataset and an AI-HUB CT-based labeling dataset, employing DenseNet architecture with squeeze-and-excitation blocks. We developed four models to assess whether CT versus chest radiography and pixel-level versus image-level labeling would improve the deep learning model’s performance to detect nodules. The models' performance was evaluated using two external validation datasets. The AI-HUB dataset with image-level labeling outperformed the NIH dataset (AUC 0.88 vs 0.71 and 0.78 vs.0.73 in two external datasets, respectively; both p < 0.001). However, the AI-HUB data annotated at the pixel level produced the best model (AUC 0.91 and 0.86 in external datasets), and in terms of nodule localization, it significantly outperformed models trained with image-level annotation data, with a Dice coefficient ranging from 0.36 to 0.58. Our findings underscore the importance of accurately labeled data in developing reliable deep learning algorithms for nodule detection in chest radiography.

Does financial reporting quality matter for the auditee characteristics-audit fees relationship? Evidence from an emerging market

Does financial reporting quality matter for the auditee characteristics-audit fees relationship? Evidence from an emerging market

Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

PurposeTo assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction.Materials and methodsThis prospective, IRB-approved study enrolled 50 pediatric patients (mean age = 11.8 ± 3.1 years) undergoing clinical brain MRI. In addition to standard of care (SOC) compressed SENSE (CS = 2.5), 3D T1-weighted SPGR images were obtained with higher CS acceleration factors (5 and 8) to evaluate the ability of AI reconstruction to improve image quality and reduce scan time. Images were reviewed independently on dedicated research PACS workstations by two neuroradiologists. Quantitative analysis of signal intensities to calculate apparent grey and white matter signal to noise (aSNR) and grey-white matter apparent contrast to noise ratios (aCNR) was performed.ResultsAI improved overall image quality compared to standard CS reconstruction in 35% (35/100) of evaluations in CS = 2.5 (average scan time = 221 ± 6.9 s), 100% (46/46) of CS = 5 (average scan time = 113.3 ± 4.6 s) and 94% (47/50) of CS = 8 (average scan time = 74.1 ± 0.01 s). Quantitative analysis revealed significantly higher grey matter aSNR, white matter aSNR and grey-white matter aCNR with AI reconstruction compared to standard reconstruction for CS 5 and 8 (all p-values < 0.001), however not for CS 2.5.ConclusionsAI reconstruction improved overall image quality and gray-white matter qualitative and quantitative aSNR and aCNR in highly accelerated (CS = 5 and 8) 3D T1W SPGR images in the majority of pediatric patients.