Do financial development and energy efficiency ensure green environment? Evidence from R.C.E.P. economies

The issue of climate change and environmental degradation has been prevailing for the last few decades. Yet economies are further expanding due to free trade agreement which accelerates the trade of energy and carbon intensive commodities across the regions. A prominent example of such free trade is the Regional Comprehensive Economic Partnership (R.C.E.P.), which mostly remains ignored. The current research study explores the influence of financial development (F.D.) and energy efficiency (E.N.E.F.) on carbon emissions in the R.C.E.P. economies. Also, this study analyses the role of economic growth and renewable energy on environmental quality during the period from 1990 to 2020. Panel data approaches such as slope heterogeneity, crosssection dependence, and the second-generation panel unit root test are used. The non-normally distributed variables are found cointegrated. Therefore, a novel method of moments quantile regression is used. The results demonstrate that F.D. and economic growth are positively associated with CO2 emissions. At the same time, E.N.E.F. and renewable energy consumption (R.E.C.) significantly reduce the emissions level and promote a green environment in all quantiles. The environmental Kuznets curve is found valid in the R.C.E.P. economies. These results are robust as validated by Fully-Modified Ordinary Least Square – a parametric approach. A two-way significant causal association exists between carbon-economic growth, carbon-F.D., carbon- R.E.C., and carbon-E.N.E.F.. The findings suggest an enhancement in R.E.C., improvement in the E.N.E.F. approaches, and implications for green F.D. in the region

Whether CEO Succession Via Hierarchical Jumps is Detrimental or Blessing in Disguise? Evidence from Chinese Listed Firms

This study investigates the impact of hierarchical jumps in the CEO’s succession on firms’ financial performance. To contemplate deeply, hierarchical jumps have been categorized into high and low level evaluating the positive impact of high-level hierarchical jump on firms’ performance. Moreover, this study has also formulated hierarchical intensity signifying the idea that despite neglecting senior board members during hierarchical jumps, still marginal increment in the firms’ growth has been observed. Using panel regression technique along with 2sls instrumental regression, this research reveals that hierarchical jumps in CEOs successions are more conducive only if the incumbent CEOs are selected irrespective of age, degree or high hierarchical position within the hierarchical ladder. Lastly, this study enunciates that firms having high total assets boost their performance via hierarchical jumps emphatically

Analyzing Nexus between Economic Complexity, Renewable Energy, and Environmental Quality in Japan: A New Evidence from QARDL Approach

The economic complexity index is an effective dimensionality reduction tool that is applied to forecast and predict future economic growth, income, and environmental quality. Renewable energy plays an important role in mitigation of carbon dioxide emissions. This study explores the nexus between economic complexity, renewable energy, FDI, trade, and environmental quality in Japan for the period 1970Q1-2019Q4. We use carbon dioxide (CO2) emissions as dependent variable while economic complexity index (ECI), foreign direct investment (FDI) inflow, renewable energy (RNE), and trade as explanatory variables. This study applies a quantile autoaggressive approach for analysis; the result of this study suggests a long-run implication of the ECI, FDI, GDP, RNE, and trade for the CO2 emissions. While only RNE and trade show mixed results in the short run, the rest of the variables do not have short-run implications. This implies that emissions mostly result in the industrial production activities only in the long run and in some quantiles only in the short run. The Japanese government may adopt different measures to reduce the CO2 emissions in the country, such as carbon tax and tax exemption on renewable energy investment. Furthermore, the government may adopt the renewal energy in production, which could achieve sustainable development goal

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Project Governance and Project Performance: The Moderating Role of Top Management Support

Project governance (PG) has been primarily acknowledged as critical by researchers and practitioners in regard to successfully executing projects. However, project governance of public projects has received less attention from researchers. Therefore, in this study, we studied the effects of project governance and top management support (TMS) on project performance (PP) and their interactions in public sector projects. Using the lens of resource dependence theory (RDT), we hypothesize whether TMS moderates the impact of PG on PP. A quantitative deductive approach was employed to examine this relationship. Quantitative data were collected using a structured questionnaire from 346 project managers, team members, and stakeholders. Our results indicated that PG and TMS are positively significantly correlated with project performance. Moreover, we found that TMS acts as a quasi-moderator in the relationship between PG and PP

Project Governance and Project Performance: The Moderating Role of Top Management Support

Project governance (PG) has been primarily acknowledged as critical by researchers and practitioners in regard to successfully executing projects. However, project governance of public projects has received less attention from researchers. Therefore, in this study, we studied the effects of project governance and top management support (TMS) on project performance (PP) and their interactions in public sector projects. Using the lens of resource dependence theory (RDT), we hypothesize whether TMS moderates the impact of PG on PP. A quantitative deductive approach was employed to examine this relationship. Quantitative data were collected using a structured questionnaire from 346 project managers, team members, and stakeholders. Our results indicated that PG and TMS are positively significantly correlated with project performance. Moreover, we found that TMS acts as a quasi-moderator in the relationship between PG and PP

Investigating the Co-movement Nexus Between Air Quality, Temperature, and COVID-19 in California: Implications for Public Health

This research aims to look at the link between environmental pollutants and the coronavirus disease (COVID-19) outbreak in California. To illustrate the COVID-19 outbreak, weather, and environmental pollution, we used daily confirmed cases of COVID-19 patients, average daily temperature, and air quality Index, respectively. To evaluate the data from March 1 to May 24, 2020, we used continuous wavelet transform and then applied partial wavelet coherence (PWC), wavelet transform coherence (WTC), and multiple wavelet coherence (MWC). Empirical estimates disclose a significant association between these series at different time-frequency spaces. The COVID-19 outbreak in California and average daily temperature show a negative (out phase) coherence. Similarly, the air quality index and COVID-19 also show a negative association circle during the second week of the observed period. Our findings will serve as policy implications for state and health officials and regulators to combat the COVID-19 outbreak

Appearance-Based Salient Regions Detection Using Side-Specific Dictionaries

Image saliency detection is a very helpful step in many computer vision-based smart systems to reduce the computational complexity by only focusing on the salient parts of the image. Currently, the image saliency is detected through representation-based generative schemes, as these schemes are helpful for extracting the concise representations of the stimuli and to capture the high-level semantics in visual information with a small number of active coefficients. In this paper, we propose a novel framework for salient region detection that uses appearance-based and regression-based schemes. The framework segments the image and forms reconstructive dictionaries from four sides of the image. These side-specific dictionaries are further utilized to obtain the saliency maps of the sides. A unified version of these maps is subsequently employed by a representation-based model to obtain a contrast-based salient region map. The map is used to obtain two regression-based maps with LAB and RGB color features that are unified through the optimization-based method to achieve the final saliency map. Furthermore, the side-specific reconstructive dictionaries are extracted from the boundary and the background pixels, which are enriched with geometrical and visual information. The approach has been thoroughly evaluated on five datasets and compared with the seven most recent approaches. The simulation results reveal that our model performs favorably in comparison with the current saliency detection schemes

Salient region detection through salient and non-salient dictionaries.

Low-rank representation-based frameworks are becoming popular for the saliency and the object detection because of their easiness and simplicity. These frameworks only need global features to extract the salient objects while the local features are compromised. To deal with this issue, we regularize the low-rank representation through a local graph-regularization and a maximum mean-discrepancy regularization terms. Firstly, we introduce a novel feature space that is extracted by combining the four feature spaces like CIELab, RGB, HOG and LBP. Secondly, we combine a boundary metric, a candidate objectness metric and a candidate distance metric to compute the low-level saliency map. Thirdly, we extract salient and non-salient dictionaries from the low-level saliency. Finally, we regularize the low-rank representation through the Laplacian regularization term that saves the structural and geometrical features and using the mean discrepancy term that reduces the distribution divergence and connections among similar regions. The proposed model is tested against seven latest salient region detection methods using the precision-recall curve, receiver operating characteristics curve, F-measure and mean absolute error. The proposed model remains persistent in all the tests and outperformed against the selected models with higher precision value

Nexus Between Foreign Direct Investment Inflow, Renewable Energy Consumption, Ambient Air Pollution, and Human Mortality::A Public Health Perspective From Non-linear ARDL Approach

A huge foreign direct investment (FDI) inflow has been witnessed in China, though on the one hand, it brings a significant contribution to economic growth. On the other hand, it adversely affects the ambient air pollution that may affect human mortality in the country. Renewable energy (RE) usage meets the country's energy needs with no adverse effect on the environment. Therefore, this study is trying to empirically analyze the effect of FDI inflow on human morality and RE consumption in China. We used time-series data for 1998–2020 and applied a non-linear ARDL approach for the estimations. The empirical outcomes suggest that FDI inflow positively affects mortality and RE. There is also unidirectional causality running from RE and pollution to mortality. In addition, the relationship among the variable verifies the existence of a non-linear relationship. The government needs policy guidelines to further boost FDI inflow due to its positive aspects. However, to reduce the negative effect on the environment and human morality, the extensive usage of RE should be adopted. Indeed, proper legislation for foreign firms might be a good step toward quality environmental and longevity of human health in society