Empirical Outcomes Research Articles

Innovation Strategies for Ecological Sustainability: Effects of Natural Resources and Energy Consumption

ABSTRACTTo attain global ecological sustainability within the framework of ecological modernization, this study scrutinizes the multifaceted interaction among environmental‐related patent technologies (ERPT), natural resources, energy consumption (both renewable and nonrenewable energy usage) and globalization on the ecological footprint. To assess the novel aspects of the ongoing objective, this study employs Disc/Kraay, fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) regression to generate comparative results across a panel of technologically advanced countries and resource‐rich least‐developed countries from 1990 to 2022. The empirical outcomes indicate that economies leading in eco‐friendly technologies and renewable sources of energy enhance environmental sustainability by minimizing their ecological footprint over time. In contrast, countries that rely heavily on natural resource extraction to meet energy demands tend to exacerbate ecological degradation. The process of globalization demonstrates the same trend for both developed and developing countries over the long term significantly elevating the level of ecological footprint. The Dumitrescu and Hurlin causality test discloses a reverse causal connection among concerning variables such as natural resources, ERPT, and energy reliance excluding globalization over the extended duration. The recommendations derived from the results suggest that governments in developing countries should strategically promote the adoption and dissemination of innovative, eco‐friendly resource extraction technologies. Meanwhile, governments in developed countries should focus on overhauling their ecological policies to play a crucial role in supporting and advancing ecological sustainability efforts in developing nations.

Resistance is futile – explanatory theory must go on! A commentary on Robert W. Lake's ‘On Resisting the Seduction of Theory’

This commentary echoes Robert Lake's epistemological critiques of theory as Truth-seeking or Understanding-oriented in a material world of contentious socio-spatial change. But instead of blaming incessant theory culture, I make the case for a kind of mid-range theory that might offer practically adequate social knowledge for positive real-world interventions. In line with pragmatist thinking in critical Geography, I argue that context-specific explanatory theory can overcome the sort of Cartesian anxiety and academic escapism identified in Lake's paper. Good theory is about conceptualising and teasing out those analytical connections within thick descriptions that can shed useful light on the purposive practices of actors and their constitutive power relations and ultimately make the difference through illuminating empirical outcomes and the possibilities for changing them. While we should indeed resist the temptation of ‘theoretical theory,’ explanatory theory must go on.

An overview of the phytochemistry of medicinal bark (trunk, stem or root) from the most popular southern African species

Abstract The ten countries that make up southern Africa are collectively a hot-spot of medicinal plant knowledge, with a unique preference for the utilization of bark over leaves from possibly hundreds of species. The most popular 86 medicinal bark species were identified in an earlier survey of various muthi markets around Johannesburg, and are listed in the current review, out of which chemical data was found for 63 and tabulated. The chemistry of medicinal bark species is, however, incomplete, since many scholars focused their research on metabolites of interest to their research groups, such as essential oils, lipophilic compounds, alkaloids or saponins, to the exclusion of other specialized metabolites present in the respective biota. From the current phytochemical analysis, the medicinal potential of bark relative to leaves is not obvious, as it is dependent on factors such as quantity of specialized metabolites (potency), their identities and anecdotal accounts from traditional healers. Nevertheless, the preference for bark may be due to empirical outcomes in therapy. Southern African medicinal bark species demonstrate an extremely diverse pool of unique/new or relatively unheard of natural products, such as calondendrolide from C. capense, combretastatin from C. caffrum, capensin from C. capense, crotohalimaneic acid from C. sylvaticus, ekebergins from E. capensis, entandrophragmin from E. caudatum, lysisteisoflavone from E. lysistemon, kigelinone from K. africana, holstinones from O. holstii, piptadeniaoside from P. africanum, rauvolfianoids from R. caffra, tetrapterosides from T. tetraptera, voacangine from V. thouarsii, warburganal from W. salutaris and mucronine from Z. mucronata. The review concludes by briefly commenting on pharmacokinetic aspects associated with ingestion or topical application of bark metabolites.

The definition of highly cited researchers: the effect of different approaches on the empirical outcome

Abstract In 2001 onetime and since 2014 annually, Clarivate (and the former Thomson Reuters) has used publication and citation data to identify exceptional researchers—highly cited researchers (HCRs)—in nearly all disciplines. The approach used by Clarivate has not been without criticism. HCRs can be defined differently; the approach of Clarivate is one possibility among several others. HCRs can be identified by considering field-normalized citation rates or absolute numbers of citations; inclusion or exclusion of self-citations; full counting or fractional counting of publications; all authors, only corresponding authors or only first authors; short, long or varying citation windows; and short or long publication periods. In this study, we are interested in the effect different approaches have on the empirical outcomes. One may expect HCRs lists with large overlaps of authors, since all approaches are based on the same (bibliometric) data. As we demonstrated with five different variants of defining HCRs, the selection among these options has a significant influence on the sample of selected researchers and their characteristics that are thereby defined as highly cited. Some options have a stronger influence on the outcome than other options such as the length of the citation window or the focus on all authors versus only the corresponding author. Based on the empirical results of this study, we recommend that the user of HCR lists should always be aware of the influence these options have on the final lists of researchers.

Optimizing Car License Plate Recognition Through Gray Wolf Optimization Algorithm

License plate recognition is an essential part of contemporary surveillance systems since it is helpful in many applications, including parking management, vehicle access control, traffic control, and law enforcement. This project aims to provide a robust and dependable method for detecting license plates that will outperform existing approaches in accuracy and dependability. This observation method uses contemporary technology to address challenging troubles related to license plate recognition. Our methodology is primarily based on the Faster R-CNN structure, an established model for picture item detection. The novel thing, even though, is how Gray Wolf Optimization—which draws notion from the searching conduct of gray wolves—is mixed with the Faster R-CNN network. The accuracy is greatly improved by this synergistic combination, which also improves detection abilities. Moreover, an improved ResNet-50 model is blanketed to improve the classification system similarly, ensuring accurate license plate detection in several situations. The extensively utilized "car license plate detection" dataset is used to assess the recommended technology very well, confirming its efficacy in practical settings. The empirical outcomes show exceptional performance, with a median precision of 98.21%, demonstrating how nicely the hybrid method works to attain the very best stage of license plate detecting accuracy. This painting establishes a new benchmark in license plate identity using cutting-edge technology and innovative techniques, starting the door for enhanced safety and surveillance.

Association between empirical antibiotic regimens in emergency department and prognosis of septic patients: A single-Centre real-world study.

In this study, we aimed to explore the association between the choice of empirical antibiotic therapy and outcomes in ED patients with sepsis. Patients admitted to ED with sepsis were identified from a single center in the United States, and the data is stored in the MIMIC-IV-ED database. Propensity score matched model was used to match patients receiving empirical mono or combination antibiotic therapy. Logistic regression model was used to assess the associations between empirical antibiotic therapy and in-hospital mortality. A total of 11,380 ED patients with sepsis were included in the data analysis. After PSM, 3920 pairs of patients were matched between the empirical mono-antibiotic therapy group and combination antibiotic therapy group. No significant benefit was observed among the empirical combination antibiotic therapy patients compared with the mono-antibiotic therapy in in-hospital mortality (OR, 0.96; 95% CI, 0.81-1.15; P: 0.684). Empirical quinolones mono-therapy was associated with significantly lower mortality compared to cephalosporins (OR, 2.12; 95% CI, 1.35-3.50; P:0.002), penicillins (OR, 1.87; 95% CI, 1.08-3.34; P:0.029) and vancomycin mono-therapy (OR, 2.15; 95% CI, 1.19-3.97; P:0.012). Empirical combination antibiotic therapy was not associated with reduced mortality in ED patients with sepsis. Compared with cephalosporins, penicillins and vancomycin, quinolone mono-antibiotic therapy was significantly associated with a decreased risk of in-hospital mortality, especially in patients with respiratory tract infections.

Exploring the evolution of scientific publication on portfolio optimization in the light of artificial intelligence: A bibliometric study

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) has profoundly influenced various domains, including portfolio optimization. In today’s dynamic and interconnected global economy, understanding the development of scientific publications in this field is crucial for both academics and practitioners. This paper aims to conduct a comprehensive bibliometric study of the scientific literature on portfolio optimization, focusing on the impact of AI, ML, and DL advancements. By analyzing key trends, influential publications, and emerging research areas, this study provides valuable insights into the progression of portfolio optimization research in the context of these transformative technologies, helping to map future directions and identify knowledge gaps in the field. This paper endeavors to present an exhaustive synthesis of the most recent advancements and innovations within the domain of portfolio optimization, particularly as influenced by progressive developments in AI, ML and DL from 1996 to 2024. Employing a rigorous bibliometric analysis, this study scrutinizes the structural and global paradigms governing this field. The analytical framework integrates several dimensions, including: (1) comprehensive dataset interrogation, (2) critical evaluation of source repositories, (3) contributions of seminal authors, (4) geographical and institutional affiliations, (5) document-centric analysis, and (6) exploration of keyword dynamics. A corpus of 745 bibliographic entries, meticulously curated from the Web of Science database, forms the basis of this inquiry, which utilizes advanced Scientometric network methodologies to extrapolate substantive research insights. The discourse culminates in a robust critique of the inherent strengths and methodological limitations, while delineating strategic avenues for future research, with the objective of steering ongoing scholarly discourse in the realm of portfolio optimization. The empirical outcomes of this study enhance the understanding of prevailing intellectual trajectories, thus laying a fortified foundation for future investigative pursuits in this critically evolving discipline.

Finding the Efficiency of ConvBi-LSTM Over Anticipation of Adversaries in WBANs

Introduction: Wireless Body Area Networks (WBANs) are similar to custom Wireless Sensor Networks, so these networks are prone to adversaries through their activities, but in healthcare applications, security is necessary for the patient data. Moreover, providing reliable healthcare to patients is essential, and for the right treatment, correct patient data is required. For this purpose, we need to eliminate anomalies and irrelevant data created by malicious persons, attackers, and unauthorized users. However, existing technologies are not able to detect adversaries and are unable to maintain the data for a long duration while transferring it. Aims: This research aims to identify adversarial attacks and solutions for these attacks to maintain reliable smart healthcare services Methodology: We proposed a Convolutional-Bi-directional Long Short-Term Memory (ConvBiLSTM) model that provides a solution for the detection of adversaries and robustness against adversaries. Bi-LSTM (Bidirectional-Long Short Term Memory), where the hyperparameters of BiLSTM are tuned using the PHMS (Prognosis Health Monitoring System) to detect malicious or irrelevant anomalies data. Result: Thus, the empirical outcomes of the proposed model showed that it accurately categorizes a patient's health status founded on abnormal vital signs and is useful for providing the proper medical care to the patients. Furthermore, the Convolution Neural Networks (CNN) performance is also evaluated spatially to examine the relationship between the sensor and CMS (Central Monitoring System) or doctor’s device. The accuracy, recall, precision, loss, time, and F1 score metrics are used for the performance evaluation of the proposed model. Conclusion: Besides, the proposed model performance is compared with the existing approaches using the MIMIC (Medical Information Mart for Intensive Care) data set.

Analyzing the Heterogeneity of Economic Development and Carbon Emissions in the GCC Countries: Based on Panel Quantile Regression Approach

This study explores the effects of energy use, tourism development, financial growth, foreign direct investment on economic growth, and the carbon emissions nexus for GCC countries from 1980 to 2018. GCC nations are currently facing higher demand and use of energy sources, FDI, tourism development, and improving financial sector that represents the major sustainable environmental and economic growth challenges. This study applies a Panel Quantile-Regression model that accepts distributional and unobserved individual heterogeneity. Furthermore, to avoid a response variable bias, some concerned cause variables are heterogeneous across quantiles in our method. The empirical outcomes indicate that the influence of FDI on CO2 emissions is statistically significant and negative, while tourism growth, energy use and financial growth have an ever-increasing impact on carbon emissions. Likewise, tourism growth decreases economic growth while FDI and energy use have been found to affect economic progress positively. Finally, the research outcomes also support policymakers with significant strategic recommendations.

The Effect of Investor Sentiment on Stock Returns: Evidence from a Frontier Market

The conventional theory of finance considers that investors are rational and make informed investment decisions and any change in pertinent risk is expected to be the result of changes in fundamental factors. However, the relationship between sentiment and stock-return challenges the hypothesis of an efficient market. This study is based on the theory of behavioural finance, which explores the impact of investor sentiment on the yearly stock returns of 141 companies listed on the Dhaka Stock Exchange (DSE) from July 2017 to June 2021. Two distinct sentiment dimensions are analysed, stock-specific investor sentiment, measured by the change in trade volume and market-wide sentiment, measured by the change in the DSEX index. The regression model includes firm characteristics variables: firm size (SIZE), price-to-NAV ratio (PNAV), sale growth (SG), and Earning Per Share (EPS) as well as macroeconomic control variables: inflation rate (INF) and short-term interest rate (INT). By employing a cluster random effects regression model, it is observed that stock-specific investor sentiment has a significant positive influence on stock returns, while market-wide sentiment does not. This effect persists even after controlling for macroeconomic factors across firm size, valuation, and growth. Hence, the empirical outcomes imply that the optimistic investor sentiment boosts stock returns whereas the pessimistic sentiment of the investors has a reverse impact.

Scoping Review: Association of Inpatient Hospital Design Features With Patients' Clinical Outcomes.

Objective: To identify associations between inpatient hospital design features and empirical patient clinical outcomes as well as changes over time. Background: A growing body of literature has emerged evaluating the association of hospital design features with measurable clinical outcomes during inpatient hospital admissions. However, there has been limited effort to evaluate the scope and quality of studies examining individual, inpatient hospital design features on empirical patient clinical outcomes. Methods: Primary research articles published in English between 1980 and 2021 evaluating inpatient clinical outcomes were included. Key terms for hospital designs and clinical outcomes were used. Ovid Medline, Embase, CINAHL, Web of Science, Scopus, Cochrane Library, Elsevier Embase, and Google Scholar were searched on May 28, 2021. Data were independently extracted by two authors, with arbitration from the third author. Results: Forty-six research articles were included for analysis. Predominantly studied inpatient hospital design features included: single versus multibedded rooms/wards, windows, furnishings, installed lighting, ward size and spatial arrangement, noise level, air ventilation, and patient visibility. Although nearly half (43%) of the articles lack appropriate methods to account for residual confounding, a trend of improvement in the use of appropriate methods was identified with 68% of studies in the last decade having appropriate methods. Studies demonstrating positive associations were more likely to be cited than those with negative associations (average citation per article, 508 vs. 27). Conclusion: Our study demonstrates the use of empirical patient clinical outcomes as a feasible approach to evaluate hospital design features, and identified an incremental improvement in the methods being applied.

Hybrid LSTM–Attention and CNN Model for Enhanced Speech Emotion Recognition

Emotion recognition is crucial for enhancing human–machine interactions by establishing a foundation for AI systems that integrate cognitive and emotional understanding, bridging the gap between machine functions and human emotions. Even though deep learning algorithms are actively used in this field, the study of sequence modeling that accounts for the shifts in emotions over time has not been thoroughly explored. In this research, we present a comprehensive speech emotion-recognition framework that amalgamates the ZCR, RMS, and MFCC feature sets. Our approach employs both CNN and LSTM networks, complemented by an attention model, for enhanced emotion prediction. Specifically, the LSTM model addresses the challenges of long-term dependencies, enabling the system to factor in historical emotional experiences alongside current ones. We also incorporate the psychological “peak–end rule”, suggesting that preceding emotional states significantly influence the present emotion. The CNN plays a pivotal role in restructuring input dimensions, facilitating nuanced feature processing. We rigorously evaluated the proposed model utilizing two distinct datasets, namely TESS and RAVDESS. The empirical outcomes highlighted the model’s superior performance, with accuracy rates reaching 99.8% for TESS and 95.7% for RAVDESS. These results are a notable advancement, showcasing our system’s precision and innovative contributions to emotion recognition.

Enhancing Talent Intelligence Evaluation with Improved Pre-training Models

In the past few years, there has been a noticeable surge in the utilization of artificial intelligence techniques within the realm of talent evaluation, particularly in response to the exponential growth of big data. Conventional talent evaluation approaches, typically relying on the integration of expertise and system-based title appraisal, are plagued with challenges including errors in subjective judgment. The strategic importance of intelligent talent assessment cannot be overemphasized, especially for discovering and fostering high-potential talent across various domains, inclusive of research. This paper introduces an innovative method, termed TE-RCB, auxiliary applications in the evaluation of actual talent titles. The initial stage of this method’s execution involves the meticulous construction of a talent evaluation dataset. This data is sourced from actual talent title applications and standardized scores provided by simulation expert engineers. The subsequent step is the application of the RoBERTa-WWM-large model, employed for the vectorization of pertinent information indispensable for talent evaluation. Further, the TextCNN model is utilized for the extraction of critical features from the talent-related information, succeeded by the application of the BiLSTM model to delve into deeper semantic correlations within the talent attributes. Comprehensive experiments are conducted on both the talent evaluation dataset and the publicly available dataset. The empirical outcomes decisively underline the superior accuracy of the proposed method, asserting its efficacy in addressing the pertinent challenges in the domain of intelligent talent assessment.

Asymmetric Impact of Carbon Emission, Oil Price Shocks on Economic Growth in Nigeria (1981 – 2022)

The study examines the asymmetric impact of carbon emissions and oil prices on economic growth in Nigeria. The research is conducted using annual time series data running from 1986 to 2019. The study employs Nonlinear autoregressive distributed lag models (NARDL) as the estimation technique. The variables used in the analysis were Gross Domestic Product (GDP), Carbon Emission (CO2), and Oil Price (OP). The NARDL result shows that carbon emission has a negative and significant impact on economic growth in the long run and the oil price has a positive and significant impact on economic growth in both the short-run and long-run. Based on the empirical outcomes of the result obtained, the following recommendations were offered: The implementation of an economic diversification policy from oil reliance towards dependence on other sustainable energy types (renewable energy resources) should be sustained and strengthened especially. The private sector should also tap into the opportunities that are available in the oil sector, such as the decarbonization programme in order to reduce carbon emissions from fossil fuels.

The Influence of External Audit Quality on Discretionary Accruals and Real Earnings Management Practices: An Analysis of Malaysian Firms

This paper aims to analyse the mitigating effects of external audit quality (EAQ) factors on earnings management (EM) practices. Data were collected from firms listed on Bursa Malaysia’s main market, covering the years 2011 through 2022. Panel regression was employed to analyse the data. The findings of this study confirmed a significant negative association between audit reputation, audit quality, audit opinions, and EM of listed firms in Malaysia. Audit fees and audit tenure were found to be not significant in relation to EM. The study included five control variables in the analysis, and only economic value added (EVA) was found to be significant. The findings suggest that a number of audit quality factors are indicative of EM among listed firms. The authors extend, as well as contribute to, the growing literature on the EAQ, and therefore, wider corporate governance literature. Thus, it provides originality by presenting empirical evidence and outcomes to fully understand how discretionary accrual and real EM affect EAQ in the Malaysian context. Therefore, stakeholders should place higher concern on the selection of an external auditor, and investors should take into account the external audit factors when making investment decisions. Measuring the effectiveness of EAQ allows decision-makers to evaluate how effective employed governance measures are in improving shareholders’ perceptions of financial information quality and mitigating EM practices.

Promoting environmental sustainability: A policy perspective on hydroelectric power generation, foreign direct investments, and financial development

In today's rapidly evolving world, characterized by the menace of global warming and the ever-pressing call for sustainable energy solutions, the trilogy of hydroelectric power generation, foreign direct investment, and environmental quality is a compelling and indispensable dimension; all of which necessitate simultaneous consideration. In this quest, this research article aims to explore the nexus between hydroelectric power generation, foreign direct investment, and sustainability—within the framework of financial development. To this end, we have utilized annual data of the world's ten largest hydroelectric power-generating countries in the year 2021 for the period 1992–2019, and utilize the Westerlund-Edgerton LM Bootstrap, Augmented Mean Group, and Dumitrescu-Hurlin panel bootstrap causality methods as our main econometrics' batteries. Based on the empirical outcomes that study entails that financial development, foreign direct investments, and increasing economic growth reduce the environmental quality. We observed that hydroelectric power generation is an important driving force on environmental quality. The empirical outcomes further infer that as the financial development levels of countries increase, the positive impact of hydroelectric power generation on environmental quality becomes more significant. The study findings will help policymakers to organize environmental quality strategies that are more realistic and accurate.

Experimenting with a strong dual necessity approach to social progress

Different social progress indices assess well-being and development differently and thus reflect distinct conceptions of social progress. Broadly, these conceptions fall into two categories: ‘subjective’—the attitudes of individuals; and ‘objective’—external standards. Our starting point is a strong ‘dual necessity’ claim, namely that the two categories of conception have joint-special significance. Such a strong dual necessity conception, when operationalized, demands very low compensability/degree of substitution between the two independent indices, objective and subjective. Experimenting with this idea, we hypothesize that implementing low substitution empirically influences outcomes. Previous research presented this concept and exemplified initial empirical outcomes. The present work enhances and broadens this research by employing the constant elasticity of substitution (CES) function, which allows for the adjustment of substitution levels and comparison of resulting rankings. Furthermore, we demonstrate the results using two different well-known and commonly used indices: Human Development Index (HDI) and Sustainable Development Goals Index (SDGI). Our findings show that using a low substitution approach alters the social progress rankings of many countries in comparison to the original components’ rankings and to a high substitution combination of the two. This holds particularly true for countries in the middle of the original rankings. The paper’s original contribution is that it establishes the CES function as a useful device for implementing the idea of ‘dual necessity.’ Importantly, by experimenting with low substitution, and exemplifying its empirical significance with the CES function, the paper also contributes to further validating a strong dual necessity conception and justifies its own measurement. Such measurement should be of value to social scientists, practitioners and policymakers who care for balanced social progress: social progress that does not neglect either of the two aspects (subjective or objective).

Unraveling the environmental Kuznets curve: The influence of economic diversity, energy efficiency, and industrial dynamics on carbon emissions in developing economies

A new path of economic development among emerging and developing nations has a distinct impact on the environment than seen in the past. The current study attempts to examine how these growth patterns in the developing world have impacted the degradation of the environment. This study contends that merely considering GDP per capita and the proportion of manufacturing in GDP fails to encapsulate the complete growth dynamics of developing and emerging countries. Consequently, such an approach does not adequately reflect the impacts of environmental degradation. As a result, the economic complexity index (ECI) is introduced to the model to reflect the full effects of new growth trajectories on CO2 emissions by using the Panel Fully Modified OLS (PFMOLS) model of 67 emerging and developing countries during 1996–2020. The results indicate that the complexity of developing and emerging economies, on the one hand, raises CO2 emissions, likely through expanding economic activities (the scale effect). Moreover, ECI reduces CO2 emissions by moving the economy toward more high-tech and environmentally friendly technologies and industries and favorable changes in the energy mix (the efficiency effect). Overall, the empirical outcomes emphasize that the final impact of ECI on the environment was negative in most samples, indicating an improving impact of economic complexity on environmental degradation, reflecting that the “efficiency effect” outweighed the “scale effect.” The findings imply that technology and knowledge transfer are essential for energy efficiency and sustainability.

The Impact of Pollution and Carbon Emission Control on Financial Development, Environmental Quality, and Economic Growth: A Global Analysis

The global environment has recently been facing sustainability threats owing to industrial and economic expansions. Accordingly, this study empirically examines the impact of carbon emissions and the directional causality between carbon emissions and environmental quality, financial development, and economic growth. We used data from 65 economies from 2010 to 2021, applying fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Generally, the findings from the analysis revealed that the estimated coefficients of carbon emissions were negative and significant across the model, except for greenhouse gas emissions, which produced an insignificant result in developed economies. This result proves that an increase in carbon emissions and other forms of pollution are detrimental to environmental quality, economic growth, and financial development. Further results revealed that fossil fuels are positively and significantly related to the economic growth and financial development of selected countries. Empirical outcomes indicate that ineffective control of environmental pollution and carbon emissions is a major challenge to the economic growth trajectories of the selected countries, especially in emerging economies. The results from directional relationships revealed that bi-directional causality exists between CO2 and GDP; between total greenhouse gas emissions and economic growth, with no directional relationship of CO2 emissions to financial development and vice versa; and economic growth to CO2 emissions from gaseous fuel consumption and vice versa. Generally, this outcome indicates that improved environmental quality control can accelerate economic growth and financial development worldwide. This study provides insights to governments, policymakers, international organizations, researchers, and many other stakeholders. This study suggests that stricter fiscal and monetary policies, laws, and regulations, such as environmental taxes and carbon emission taxes, with strong implementation strategies, especially in emerging economies, are strongly recommended worldwide. Further recommendations suggest the development of technologically innovative policies that can counter all the impacts of devastating human activities on the environment, and these are encouraged. A greater consumption of renewable energy and the use of other innovative machines that are environmentally friendly and can help control various forms of pollution and carbon emissions have been encouraged globally.

Towards the cost of health in Africa: examining the synergistic effect of climate change and renewable energy on health expenditure

AbstractClimate change has become the centre of discussion among scholars and practitioners globally. This is believed to have severe consequences for the environment and human health, leading to significant implications for health financing. However, empirical investigations examining the climate change-health nexus remain scarce and underexplored. Hence, this study contributes to knowledge by exploring the direct and synergistic influence of renewable energy and climate change on health expenditure (i.e., national, public, private and external) in Africa. The study employs annual data from a panel of 47 African countries between 2006 and 2019, which are estimated using the generalised method of moments. Robustness checks are performed using alternative proxies for climate change and the Driscoll-Kraay estimation technique. The empirical outcomes reveal that high CO2 emissions increase the various indices of health expenditure, implying environmental degradation triggers higher costs in health spending. The empirical results further show that renewable energy presents a negative and significant influence on health expenditure, showing that health expenditure decreases due to the adoption of renewable energy. Additionally, the study finds that renewable energy weakens the effect of carbon emissions on health spending, including public, private and external health expenditures. This implies that the severe impact of climate change on health expenditure is reduced through the intervening role of renewable energy sources. The study recommends that policymakers implement targeted policies towards reducing carbon emissions and increasing renewable energy consumption to improve health outcomes. Governments must also promote universal health coverage to ensure inclusive coverage as climate change becomes increasingly apparent.