Co-Designing Digital Health Intervention for Monitoring Medication and Consultation Among Transgender People in Underserved Communities: Collaborative Approach.

BACKGROUND: In many parts of the world, men who have sex with men and transgender individuals face criminalization and discrimination. As a result, they are less likely to seek medical help, despite experiencing higher rates of HIV/AIDS, mental health issues, and other health problems. Reaching key populations (KPs) with essential testing, care, and treatment services can be challenging, as they often have a higher likelihood of contracting and spreading the virus. They have limited access to antiretroviral (ARV) therapy (ART) services, which means that KPs may continue to serve as reservoirs for new HIV infections if they do not receive effective HIV programming. This ongoing issue complicates efforts to control the epidemic. Therefore, modeling a digital health system to track ARV medication access and use is crucial. This paper advocates for the use of digital interventions to manage the health of KPs in underserved regions, using Nigeria as a case study. OBJECTIVE: This study aims to assess digital health interventions for monitoring medication and consultations among transgender people in underserved communities. It also sought to determine whether a system exists that could support ART adherence in Nigeria. Additionally, the study evaluated design strategies to address privacy and confidentiality concerns, aiming to reduce nonadherence to ARV medications among KPs in Nigeria. METHODS: A qualitative approach was adopted for this research, involving a thematic analysis of information collected from interviews with clinicians and other health practitioners who work directly with these communities, as well as from an interactive (virtual) workshop. RESULTS: The findings from the thematic analysis indicate a need to increase attendance at ART therapy sessions through the implementation of an intensive care web app. Unlike previous solutions, this study highlights the importance of incorporating a reminder feature that integrates with an in-app telemedicine consultancy platform. This platform would facilitate discussions about client challenges, such as adverse drug effects, counseling sessions with clinical psychologists, and the impact of identity discrimination on mental health. Other data-driven health needs identified in the study are unique drug request nodes, client-led viral load calculators, remote requests, and drug delivery features within the web app. Participants also emphasized the importance of monitoring medication compliance and incorporating user feedback mechanisms, such as ratings and encouragement symbols (eg, stars, checkmarks), to motivate adherence. CONCLUSIONS: The study concludes that technology-driven solutions could enhance ART adherence and reduce HIV transmission among transgender people. It also recommends that local governments and international organizations collaborate and invest in health management services that prioritize health needs over identity.

Firm-level pollution and membership of emission trading schemes.

Several firms have joined emission trading schemes in response to the call for corporate climate action. Using a comprehensive international data set on corporate membership of emission trading schemes (ETSs), we find that members of the scheme emit more CO2 than non-participants. This result also holds when exploring the corporate discharge of sulphur and volatile organic compounds (VOCs). The magnitude of this relationship persists even in the long run showing little evidence of a reduction from the firms in polluting the environment. We also find that firms that select to exit the scheme continue to pollute at a higher rate in the following years. Firms that enter the scheme for the first time increase their pollution in the following years. Although we identify significant differences at a country and continental level on the effectiveness of ETSs, our results raise some concerns about ETSs' role.

The role of economic policy uncertainty and social welfare in the view of ecological footprint: evidence from the traditional and novel platform in panel ARDL approaches.

In the contemporary world, environmental degradation has become a concern for human beings. Accordingly, the impact of social welfare, economic policy uncertainty, natural resource rents, life expectancy, and trade openness are examined on ecological footprint (the most comprehensive proxy of environmental degradation) in 19 energy-intensive countries from 1997 to 2018. With this in mind, this study used the traditional panel ARDL and CS-ARDL approaches to evaluate how the study's variables influence ecological footprint. Notably, the results of the CS-ARDL approach are more robust due to cross-sectional dependence and slope heterogeneity problems. The outcomes revealed that economic policy uncertainty and trade openness affect the ecological footprint negatively in the short run and positively in the long run. Moreover, social welfare degrades the environment in the long run, and natural resource rents improve environmental quality by mitigating the ecological footprint in the short run and harming the environment in the long run. Besides, life expectancy does not significantly affect ecological footprint in the long or short run. Meanwhile, the results confirmed the bi-directional causal relationship between the study's variable and ecological footprint. Based on the outcomes, the way to adopt effective policies to improve the quality of the environment has been paved. Furthermore, a comprehensive policy framework for stricter environmental regulation is expected to be developed using the outcomes derived from this study.

Sign and Human Action Detection Using Deep Learning.

Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model's performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.

An empirical assessment of electricity consumption and environmental degradation in the presence of economic complexities.

To a large extent, the theories and concepts behind the effect of ecological footprint have been the paramount concern of the recent literature. Since the rising and falling of environmental degradation have been a continuous issue since the first phase of development, determinants such as economic complexity may play a critical role in achieving long-term sustainable development in the framework of environmental Kuznets curve (EKC) paradigm. Therefore, this research expands on the notion of an EKC paradigm for the world's top ten most complex economies by considering four variables, such as real GDP per capita, electricity consumption, trade openness, and a new putative factor of environmental obstacle, the economic complexity index (ECI). This is one of the first studies to look at the impact of ECI on the ecological footprint of a specific sample from 1998 to 2017. The findings demonstrate a continuous inverted U-shaped link between real GDP per capita, the square of real GDP per capita, and ecological footprint. The EKC hypothesis is found to be valid in the long term in the examined complex economies. The findings of the panel autoregressive distributed lag (ARDL) of the pooled mean group (PMG) and fully modified ordinary least squares (FMOLS) estimations demonstrate that in the long term, electric power usage contributed to the carbon footprints. Furthermore, the economic complexity index and trade openness increase environmental performance over time. To determine if there is causation between the variables, we employ the panel vector error correction model (VECM) framework. Particularly, the results show unidirectional causality running from electric power consumption to ecological footprint and bidirectional causal relationship between (1) economic growth and ecological footprint; (2) square of economic growth and ecological footprint; (3) economic complexity index and ecological footprint; and (4) trade openness and ecological footprint.

Do economic policy uncertainty and geopolitical risk surge CO2 emissions? New insights from panel quantile regression approach.

In recent times, economic policy uncertainty (EPU) and geopolitical risk (GPR) are increasing significantly where the economy and environment are affected by these factors. Therefore, the goal of this paper is to investigate whether EPU and GPR impede CO2 emissions in BRICST countries. We employ second-generation panel data methods, AMG and CCEMG estimator, and panel quantile regression model. The conclusions document that most of the variables are integrated at I (1), and there exists co-integration among considered variables of the study. Moreover, we note that EPU and GPR have a heterogeneous effect on CO2 emissions across different quantiles. EPU adversely affects CO2 emissions at lower and middle quantiles, while it surges the CO2 emissions at higher quantiles. On the contrary, geopolitical risk surges CO2 emissions at lower quartiles, and it plunges CO2 emissions at middle and higher quantiles. Furthermore, GDP per capita, renewable energy, non-renewable energy, and urbanization also have a heterogeneous impact on CO2 emissions in the conditional distribution of CO2 emissions. Based on the results, we discuss the policy direction.

The anthropogenic consequences of energy consumption in the presence of uncertainties and complexities: evidence from World Bank income clusters.

In environmental management, many studies have examined the energy consumption-emission nexus in detail. However, for the first time in the literature, this study considers how the Economic Complexity Index (ECI) and economic policy uncertainty (EPU) moderate the contribution of energy consumption to emissions for the four World Bank Income clusters. The system generalised methods of moments are applied to data for 109 countries from 1996 to 2016. Based on the main model (grouped clusters) estimations, the result revealed the existence of the environmental Kuznets curve (EKC) hypothesis. Also, an increase in air transport and consumption of energy releases more carbon emissions to the climate. Interestingly, ECI decreases carbon emission significantly while EPU does not have a significant impact. Moreover, the study revealed that ECI moderated the impact of other variables on emission, but EPU is not a significant moderator. Furthermore, a comparative analysis among the four incomes suggests that the EKC hypothesis holds only in the high-income clusters; ECI is a significant predictor of carbon emission in the four clusters, but it only decreases the emission in high-income clusters. This corroborates the debate on climate change and the productive capacity of high-income countries. Given the foregoing, several policy measures were recommended.

Designing policy framework for sustainable development in Next-5 largest economies amidst energy consumption and key macroeconomic indicators.

Global travel and tourism have enjoyed a significant boost due to the progress in air transport. However, the debate on air transport and the influx of foreign investments and global energy demand on economic development remains questionable. Therefore, this study is an attempt to contribute to the body of knowledge in the energy-tourism-led growth hypothesis literature. For this purpose, a novel approach to the effects of international tourism on economic growth is introduced for the Next-5 largest economies, namely (China, India, Indonesia, Turkey and the USA) between 1990 and 2018. Empirical results reveal a positive connection between foreign direct investment and income levels, electricity production and income levels, as well as between urbanization and economic growth. Moreover, the validation of the environmental Kuznets curve and the halo effect of foreign direct investment on the environmental degradation process provides a shred of more substantial evidence and fitting environmental instruments for policymakers. The empirical results encourage sustainable economic growth in these countries, mainly through the attraction of clean and high-technology foreign investment, the increase of the share of renewable energy sources in the energy mix and the regulation in the tourism industry. The novel contribution of this study to the empirical literature is the unification in the same research of the TLGH and the EKC for the Next-5 largest economies, establishing recommendations for tourism, energy efficiency and environmental correction process.

On the nexus between globalization, tourism, economic growth, and biocapacity: evidence from top tourism destinations.

Several studies have investigated the relationship between tourism, consumption of energy, globalization, and ecological footprint. However, the role of biocapacity alongside tourism development in environmental sustainability is yet to be documented in the extant literature. No doubt, the biocapacity of a country, its level of tourist's arrival, as well as globalization all contribute immensely to ecological footprint. Consequently, this study looks at long-run and causality connections with a special focus on bio-capacity. The study uses the pooled mean group-autoregressive distributed lag model (PMG-ARDL) methodology to test the causality relationship during 2016 international tourists' receipt from world tourism organization data files for 10 tourism destinations. Empirical result based on the panel PMG-ARDL confirms the environmental Kuznets curve (EKC) hypothesis for the 10 tourism destinations countries investigated. Furthermore, the panel ARDL estimator was used to estimate the short-run and long-run relationships simultaneously between biocapacity, tourist arrivals, GDP per capita, globalization, and ecological footprints. While the Dumitrescu and Hurlin panel causality test was used to establish causality relationships among the highlighted variables. The trade-off between economic growth and environmental quality suggests that tourist arrival dampens environmental quality. In addition, the study finds that growing biocapacity affects ecological footprints negatively. Furthermore, an increase in tourism-related activities, globalization, and economic production has the potential to damage the quality of the environment. To this end, given the study results, there is a need to pursue green tourism which can reduce environmental degradation and destruction of land caused by multiple tourism-related transportation and construction of tourist facilities respectively in the top ten tourist destination countries.

An evaluation of the causal effect between air pollution and renewable electricity production in Sweden: Accounting for the effects of COVID-19.

The COVID-19 pandemic has made a significant disruption in the renewable industry, and the effects will last longer. In this context, understanding how and which specific renewable power got affected due to this crisis is of crucial importance. This study examines the nexus between COVID-19 and Sweden's renewable electricity production from three sources of energy such as nuclear, solar, and wind, where the data ranges from January 1, 2019, to February 17, 2021. Since this study compares the period before and during the pandemic event, the study uses Air Quality Index as a measure of COVID-19 induced event and thus study the linkage between air quality and electricity production from three types of renewable energy. To analyse the above issue, several advanced techniques such as Wavelet Power Spectrum, Wavelet Coherence, Partial and Multiple Wavelet Coherence have been applied. The findings from the Wavelet Coherence approach demonstrate that COVID-19 has disrupted the linkage between wind energy generation and air quality, while the disruption in the case of solar and nuclear electricity generation has been minimal. Moreover, solar energy generation and air pollution both negatively affect each other, implying the need to generate solar power as well as reduce the level of air pollution in Sweden. In light of the above findings, the study discusses possible policy actions the country can take to fulfil its renewable development goals.

The effect of energy consumption on the environment in the OECD countries: economic policy uncertainty perspectives.

In this paper, we investigate the impact of energy use and economic policy uncertainties on the environment. To achieve this objective, we use the pooled mean group-autoregressive distributed lag methodology (PMG-ARDL) and Dumitrescu and Hurlin causality test on 22 Organisation for Economic Co-operation and Development (OECD) countries between 1985 and 2017. The PMG-ARDL estimation shows that energy use and economic policy uncertainties have a positive relationship with carbon dioxide emission (CO2) emission, while a negative relationship is confirmed between renewable and CO2 emissions in the long run. The short-run estimation shows a positive relationship between energy use, real gross domestic product, and per capita on CO2 emissions. The Dumitrescu and Hurlin causality results highlight a unidirectional running from real GDP and GDP per capita square to CO2 emissions. Furthermore, one-way causality exists between CO2 emissions to economic policy uncertainties. These results have policy implications on the macroeconomy which are discussed in detail in the concluding section.

Toward a sustainable environment: nexus between consumption-based carbon emissions, economic growth, renewable energy and technological innovation in Brazil.

This research investigates the drivers of consumption-based carbon emissions in Brazil by using a dataset covering the period between 1990 and 2018. These dynamics were examined by employing the ARDL bounds, DOLS, and gradual shift causality tests. The ARDL long- and short-run estimation outcomes reveal that: (a) renewable energy use stimulates the sustainability of the environment; (b) economic growth increases environmental degradation; and (c) technological innovation enhances the quality of the environment. In addition, the gradual shift causality test results disclosed that renewable energy consumption, economic growth, technological innovation and public-private partnership investment in energy can predict consumption-based carbon emissions in Brazil. Therefore, Brazilian policymakers should actively encourage the R&D of low-carbon technologies and renewable energy consumption. Domestic consumption levels, on the other hand, should be targeted, specifically those that are more energy-intensive and cause a rise in CO2 emissions due to consumption.

Determinants of green growth in developed and developing countries.

Considering the need for environmental sustainability while ensuring economic growth and development by 2030, this study uses data on 123 developed and developing countries to examine factors that influence green growth. The empirical results show that economic development positively influences green growth. However, trade openness is detrimental to green growth. Regarding energy-related factors, we find energy consumption negatively affecting green growth, but renewable energy consumption significantly improves green growth. In further analysis, we find that the influence of these factors differs between developed and developing countries. The result implies that countries at a different development level will require different strategies in achieving the Sustainable Development Goals in 2030. The results are robust to alternative identification strategies such as the System Generalised Method of Movement, which accounts for potential endogeneity.

Roadmap for climate alliance economies to vision 2030: retrospect and lessons.

The United Nations Climate Conference 25, held in December 2019, reached a significant agreement against implementing the Paris agreement come 2020. Bound by the contract, 189 countries who are party to the deal agreed to constrain worldwide temperature to ascend to 1.5° Celsius. To this end, the present study attempts to investigate the readiness of selected countries in the European Union to implement the agreement, which will better the quality of the global environment. In line with this, this study appraises the connection between economic growth, renewable and non-renewable energy consumption, on emissions in 11 countries in the European Union from 1990 to 2016. The study utilises the Pooled Mean Group-Auto Regressive Distributed Lag (PMG-ARDL) model estimator and Dumitrescu and Hurlin Panel Causality analysis to analyse the long-run and short-run impact and direction of causality among these factors, respectively. The long-run study's empirical results show a U-shaped Environmental Kuznets Curve (EKC) and a negative connection between renewable energy use and emissions in the EU-11 countries. In the short-run, non-renewable energy use worsens CO2 emissions while renewable energy use leads to a fall in emissions. Similarly, causality tests show a feedback mechanism between emissions and renewable energy use and between non-renewable energy and renewable use. Also, there is unidirectional causality from income to CO2 emissions, non-renewable energy use to CO2 emissions. The investigation recommends an expanded proportion of renewable energy sources in the EU countries' energy mix to cut down on emissions.

Does agricultural development induce environmental pollution in E7? A myth or reality.

Environmental degradation caused by various human activities has been a subject of attention over the globe. There is a concern on how to maintain a clean environment and at the same time achieve optimum production of food and non-food products amidst global energy demand. To this end, this study examines the impact of agricultural development, energy use, and economic growth on CO2 emissions in the emerging seven countries that comprises China, India, Brazil, Mexico, Russia, Indonesia, and Turkey for the annual time frequency from 1990 to 2016. The study uses a battery of econometrics techniques for soundness of analysis the consist of pooled mean group autoregressive distributed lag methodology, dynamic ordinary least squares, and fully modified ordinary least squares as estimation techniques alongside Dumitrescu and Hurlin causality test for the direction of causality analysis. Empirical results revealed that value-added agriculture and economic growth are drivers of CO2 emission in the E7 countries, and the rise in renewable energy causes a reduction in CO2 emissions, while in the short run, economic growth has a positive impact on emissions in the focus countries. Causality analysis shows that there is a feedback causality between economic growth and emissions, between value-added agriculture and energy usage, between emission and value-added agriculture, and between economic growth and agricultural development. Furthermore, energy use does not cause emissions directly; it causes economic growth and value-added agriculture which causes emissions. This position aligns with the advocacy of the United Nations Sustainable Development Goal (UN-SDG) Targets 7 and 13 of clean energy access and mitigation of climate changes issues.