What do we learn from Nexus between trade diversification and structural change: informing the future about climate action and Sustainability.

Economic complexity is considered key a driver of social change, structural change, and economic development. Economic complexity is mostly used to capture issues apropos product diversification of exports, trade, technological innovation, human knowledge, and skills. The current study has conducted a detailed bibliometric review of economic complexity, export quality, and trade diversification. In doing so, the authors used the literature up to 2021 to unveil economic complexity's contextual information that witnessed structural change, social change, and trade indicators. The current study is the first integrative review to report the theoretical contribution, future research agendas, and thematic analysis of economic complexity, export quality, and export diversification. Our study, on the subject of economic complexity, export diversification, and import diversification in the period from 1966 to 2021, was carried out by systematically scanning 386 documents, and it is one of the pioneering studies in this field. In addition, economic diversity, development, and economic complexity; export diversification, import diversification, trade openness, and economic growth; energy, environmental Kuznets curve, and economic complexity; and sustainability and economic diversification are the four main research topics of the study. The findings are discussed apropos of economic complexity and exports, methodological aspects of economic complexity, and environmental issues nexus with economic complexity. The current study reports novel findings toward a path for achieving SDG-9 (industry and innovation) and SDG-13 (climate action). The biometric review enables researchers and policymakers to understand export quality, economic complexity, and the trade nexus and report future research directions for achieving sustainable growth in industries and innovation.

Exploring the nexus between agriculture and greenhouse gas emissions in BIMSTEC region: The role of renewable energy and human capital as moderators.

Agriculture and the food system emit a considerable amount of Greenhouse Gas (GHG) emissions in the atmosphere. Hence, current researchers, policymakers, and other stakeholders are calling for improving the environmental performance of agriculture. This study utilizes the countries of The Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) to investigate the effect of agriculture value-added, pesticide use, renewable energy adoption, human capital, and economic growth on greenhouse gas emissions. The moderation effect of renewable energy use and human capital is also introduced to see whether they can offset agriculture's emissions in these BIMSTEC economies. Having reported a state-of-the-art literature review, the econometric procedure applies the second-generation unit root tests, panel cointegration and panel quantile regression for three preferred model specifications. The result from the Panel quantile regression method reveals a U-shaped relationship between agriculture value-added and greenhouse gas emissions, suggesting the significance of a small farming system. Human capital has a negative effect, whereas pesticide use has a positive effect on greenhouse gas emissions. Furthermore, the moderation effect of human capital and pesticide use suggests that human capital is not significant enough to offset the effect of pesticide use on the greenhouse gas emissions, whereas the interaction of renewable and pesticide use suggests that renewable energy adoption in the agriculture sector can mitigate the effect of pesticide use on GHG emissions. Finally, the conclusions of the study support the achievement of few sustainable development goals.

Econometric analysis of COVID-19 cases, deaths, and meteorological factors in South Asia.

The pandemic has affected almost 74 million people worldwide as of 17 December 2020. This is the first study that attempts to examine the nexus between the confirmed COVID-19 cases, deaths, meteorological factors, and the air pollutant namely PM2.5 in six South Asian countries, from 1 March 2020 to 30 June 2020, using the advanced econometric techniques that are robust to heterogeneity across nations. Our findings confirm (1) a strong cross-sectional dependence and significant correlation between COVID-19 cases, deaths, meteorological factors, and air pollutant; (2) long-term relationship between all the meteorological variables, air pollutant, and COVID-19 death cases; (3) temperature, air pressure, and humidity exhibit a significant impact on the COVID-19 confirmed cases, while COVID-19 confirmed cases and air pollutant PM2.5 have a statistically significant impact on the COVID-19 death cases. In this way, the conclusion that high temperature and high humidity increase the transmission of the COVID-19 infections can also be applied to the regions with greater transmission rates, where the minimum temperature is mostly over 21 °C and humidity ranges around 80% for months. From the findings, it is evident that majority of the meteorological factors and air pollutant PM2.5 exhibit significant negative and positive effects on the number of COVID-19 confirmed cases and death cases in the six countries under study. Air pollutant PM 2.5 provides more particle surface for the virus to stick and get transported longer distances. Hence, higher particulate pollution levels in the air increase COVID-19 transmission in these six South Asian countries. This information is vital for the government and public health authorities in formulating relevant policies. The study contributes both practically and theoretically to the concerned field of pandemic management.

Meteorological factors, COVID-19 cases, and deaths in top 10 most affected countries: an econometric investigation.

This paper examines the nexus between the Covid-19 confirmed cases, deaths, meteorological factors, including an air pollutant among the world's top 10 infected countries, from 1 February 2020 through 30 June 2020, using advanced econometric techniques to address heterogeneity across the nations. The findings of the study suggest that there exists a strong cross-sectional dependence between Covid-19 cases, deaths, and all the meteorological factors for the countries under study. The findings also reveal that a long-term relationship exists between all the meteorological factors. There exists a bi-directional causality running between the Covid-19 cases and all the meteorological factors. With Covid-19 death cases as the dependent variable, there exists bi-directional causality running between the Covid-19 death cases and Covid-19 confirmed cases, air pressure, humidity, and temperature. Temperature and air pressure exhibit a statistically significant and negative impact on the Covid-19 confirmed cases. Air pollutant PM2.5 also exhibits a significant but positive impact on the Covid-19 confirmed cases. Temperature indicates a statistically significant and negative impact on the Covid-19 death cases. At the same time, Covid-19 confirmed cases and air pollutant PM2.5 exhibit a statistically significant and positive impact on the Covid-19 death cases across the ten countries under study. Hence, it is possible to postulate that cool and dry weather conditions with lower temperatures may promote indoor activities and human gatherings (assembling), leading to virus transmission. This study contributes both practically and theoretically to the concerned field of pandemic management. Our results assist in taking appropriate measures in implementing intersectoral policies and actions as necessary in a timely and efficient manner. Causal relations of Meteorological factors and Covid-19 (2 models used in the study).

Nexus between environmental, social and economic development in South Asia: evidence from econometric models.

This study investigates the impact of various economic, social and environmental indicators on economic growth in South Asian countries. Using the data throughout 1990-2017, a panel data estimation method is adopted with sophisticated econometric approaches. The obtained results indicate a long-term positive effect of biological capacity, financial development, human development index, income inequality on economic growth while the effect of energy use is the opposite. The findings of the study suggest that governments and associated bodies must promote financial development, human development, and biocapacity to not only attain economic growth in the long-run and but dissuade ecological footprint, and income inequality at the same time while matching the energy consumption with the bio-capacity of each economy.

Energy consumption, FDI, and urbanization linkage in coastal Mediterranean countries: re-assessing the pollution haven hypothesis.

Foreign direct investment (FDI) and the consumption of non-renewable energy have been on the increase in the coastal Mediterranean countries (CMCs) over the last few decades. Both trigger growth, but the environmental impact could be far-reaching as environmental distortions are mainly human-induced. This study examines the environmental issues facing CMCs. Specifically, we investigate whether the pollution haven hypothesis holds for CMCs. We employ a quantile panel data analysis for CMCs to account for heterogeneity and distributional effects of socioeconomic factors. The result reveals that the influence of FDI on environmental degradation is a function of the indicators utilized and also depends on the initial levels of environmental degradation. The results suggest that the pollution haven hypothesis does not hold for CMCs. However, we also find that energy consumption significantly increases environmental degradation for all indicators and across the observed quantiles. The effects of economic growth and urbanization on the environment were mixed for the different indicators and across quantiles. We recommend that it is pertinent for CMCs to limit their "dirty" energy sources and substitute them with renewables to promote environmental sustainability.

Renewable energy, urbanization, and ecological footprint linkage in CIVETS.

Emerging economies are mostly plague by a massive consumption of non-renewable energy amidst an ever inceasing urbanization rate with little or no attention to the quality of the environmental. As such, this paper investigates the relationship between renewable energy, urbanization, economic growth, trade openness, and ecological footprint in CIVETS countries, namely, Colombia, Indonesia, Vietnam, Egypt, Turkey, and South Africa. The study employs augmented mean group estimator, panel cointegration, and causality tests. The findings reveal that renewable energy improves environmental quality, and trade is not particularly harmful to the environment. However, non-renewable energy consumption and urbanization are the chief contributors to environmental degradation in the CIVETS countries. Economic expansion mitigates environmental deterioration in Colombia, South Africa, and Turkey, but contributes to pollution in Egypt, Indonesia, and Vietnam. Finally, the causality test suggests that urbanization drives environmental degradation. Policy directions are discussed.

Ecological footprint, urbanization, and energy consumption in South Africa: including the excluded.

The study explores the relationship between ecological footprint, urbanization, and energy consumption by applying the ARDL estimation technique on data spanning 1965-2014 for South Africa. After applying the unit root test that accounts for a break in the data, the Bayer and Hanck (J Time Ser Anal 34:83-95, 2013) combined cointegration test affirms cointegrating relationship among the variables. Findings further reveal that economic growth and financial development exact a deteriorating impact on the environment in the short run. However, the same was not true for both energy use and urbanization. While urbanization and energy use promote environmental quality in the long run, financial development and economic growth degrade it further. The long-run findings of our study are confirmed to be robust as reported by the fully modified OLS (FMOLS), dynamic OLS (DOLS), and the canonical cointegrating regression (CCR) estimates. The direction of causality supports the energy-led growth hypothesis for South Africa. Policy outcomes and directions, and the possibility of promoting sustainable growth without degrading the environment are discussed.

Lesion segmentation from multimodal MRI using random forest following ischemic stroke.

Understanding structure-function relationships in the brain after stroke is reliant not only on the accurate anatomical delineation of the focal ischemic lesion, but also on previous infarcts, remote changes and the presence of white matter hyperintensities. The robust definition of primary stroke boundaries and secondary brain lesions will have significant impact on investigation of brain-behavior relationships and lesion volume correlations with clinical measures after stroke. Here we present an automated approach to identify chronic ischemic infarcts in addition to other white matter pathologies, that may be used to aid the development of post-stroke management strategies. Our approach uses Bayesian-Markov Random Field (MRF) classification to segment probable lesion volumes present on fluid attenuated inversion recovery (FLAIR) MRI. Thereafter, a random forest classification of the information from multimodal (T1-weighted, T2-weighted, FLAIR, and apparent diffusion coefficient (ADC)) MRI images and other context-aware features (within the probable lesion areas) was used to extract areas with high likelihood of being classified as lesions. The final segmentation of the lesion was obtained by thresholding the random forest probabilistic maps. The accuracy of the automated lesion delineation method was assessed in a total of 36 patients (24 male, 12 female, mean age: 64.57±14.23yrs) at 3months after stroke onset and compared with manually segmented lesion volumes by an expert. Accuracy assessment of the automated lesion identification method was performed using the commonly used evaluation metrics. The mean sensitivity of segmentation was measured to be 0.53±0.13 with a mean positive predictive value of 0.75±0.18. The mean lesion volume difference was observed to be 32.32%±21.643% with a high Pearson's correlation of r=0.76 (p<0.0001). The lesion overlap accuracy was measured in terms of Dice similarity coefficient with a mean of 0.60±0.12, while the contour accuracy was observed with a mean surface distance of 3.06mm±3.17mm. The results signify that our method was successful in identifying most of the lesion areas in FLAIR with a low false positive rate.