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Most researchers consider CO 2 emissions to be the primary indicator of environmental degradation. Similarly, ecological footprint appears to be a significant proxy for environmental degradation in recent research due to its multifaceted impact on the natural environment. With this in mind, this study investigates fluctuations in CO 2 emissions and ecological footprint as indicators of environmental degradation in Bangladesh from 1980 to 2020, and how they are influenced by net savings, natural resource depletion, technological innovation, and democracy. The non-linear ARDL (NARDL)-based asymmetric analysis finds that positive changes in net savings, natural resource depletion, and democracy positively impact both parameters of environmental degradation in the long run. On the other hand, a positive change in technological innovation reduces these parameters in the long run. Likewise, negative changes in net savings and technological innovation reduce environmental degradation. In contrast, negative changes in natural resource depletion and democracy exacerbate these two parameters and degrade environmental quality in the long run. However, there are some variations in the short-run influence of the predictors on the predicted variable. Overall, the findings of this study suggest that policymakers must strategically exploit natural resources, net savings, technology diffusion, and democratic principles to preserve the natural environment in Bangladesh., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model's high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep learning models typically contain a large number of parameters, resulting in a 'heavy' model. A heavy model scores high on performance metrics but is incompatible with mobile and edge computing devices. Model compression techniques such as knowledge distillation enable the creation of lightweight, deployable models for these low-resource devices. KD generates lighter models and typically performs with slightly less accuracy than the heavier teacher model (model accuracy by the teacher model on CIFAR 10, CIFAR 100, and TinyImageNet is 95.04%, 76.03%, and 63.39%; model accuracy by KD is 91.78%, 69.7%, and 60.49%). Although the distillation process makes models deployable on low-resource devices, they were found to consume an exorbitant amount of energy and have a substantial carbon footprint (15.8, 17.9, and 13.5 times more carbon compared to the corresponding teacher model). The enormous environmental cost is primarily attributable to the tuning of the hyperparameter, Temperature (τ). In this article, we propose measuring the environmental costs of deep learning work (in terms of GFLOPS in millions, energy consumption in kWh, and CO2 equivalent in grams). In order to create lightweight models with low environmental costs, we propose a straightforward yet effective method for selecting a hyperparameter (τ) using a stochastic approach for each training batch fed into the models. We applied knowledge distillation (including its data-free variant) to problems involving image classification and object detection. To evaluate the robustness of our method, we ran experiments on various datasets (CIFAR 10, CIFAR 100, Tiny ImageNet, and PASCAL VOC) and models (ResNet18, MobileNetV2, Wrn-40-2). Our novel approach reduces the environmental costs by a large margin by eliminating the requirement of expensive hyperparameter tuning without sacrificing performance. Empirical results on the CIFAR 10 dataset show that the stochastic technique achieves an accuracy of 91.67%, whereas tuning achieves an accuracy of 91.78%-however, the stochastic approach reduces the energy consumption and CO2 equivalent each by a factor of 19. Similar results have been obtained with CIFAR 100 and TinyImageNet dataset. This pattern is also observed in object detection classification on the PASCAL VOC dataset, where the tuning technique performs similarly to the stochastic technique, with a difference of 0.03% mAP favoring the stochastic technique while reducing the energy consumptions and CO2 emission each by a factor of 18.5., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Rafat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study examines the impact of several important macroeconomic variables such as quality of education, infrastructure development, foreign direct investment inflow, and green energy transitions on economic growth. We analyzed annual time series data sample for estimation of the above macroeconomic indicators during 1990 to 2020. We use nonlinear auto-regressive distributive lag model (NARDL) approach to detect the short-term and long-term effects of undermentioned macroeconomic variables on economic growth of Pakistan. The results primarily reveal that the quality education, foreign direct investment inflow, and infrastructure development are playing a significant positive role in the economic growth of Pakistan. Similarly, in short term the foreign direct investment inflow, infrastructure, and green energy transition coefficients are significantly positive related to sustainable development goals. However, the education found as unsubstantial as contributive as other variables. Moreover, the Granger causality and structural break estimations are employed to estimate the causal association between the selected parameters and unexpected change over the economy. The estimated outcomes find the unidirectional causality from education and green energy transition towards economic growth, where education is found within relation to infrastructure. Additionally, bidirectional causal relationship is found between FDI and infrastructure towards economic growth which shows that the increase in foreign investment has the potential to boost the economic growth. Finally, all the estimated indexes are considered as important sources towards the economic growth., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

It is well acknowledged that achieving sustainable development goals without negatively impacting a country's economic activity is complicated. The question of whether foreign or domestic capital can be used to address the financial demands of the nations who lack the financial resources for a green transformation should now be resolved. Based on this, the main goal of this research is to analyze the impacts of domestic and foreign capital on carbon emissions for a heterogeneous panel of 42 countries for the period from 1990 to 2017. Aside from capital accumulation, the environmental impact of elements such as economic growth, urbanization, trade openness, and energy usage is also studied. The newly developed quantile via moment approach is utilized to isolate the impacts according to the countries' emission levels. Finally, the impact of these variables on the recently constructed sustainable development index is investigated in order to ensure its robustness. The findings of the study reveal that the environmental efficiency of domestic capital accumulation in countries with low emission levels is higher than in countries with high emission levels. Foreign capital, on the other hand, has no substantial effect on emission levels in all quantiles., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper measures the effects on carbon emissions of production scale, composition, technological use, and environmental policy to elucidate the environmental Kuznets curve (EKC) hypothesis, synthesising the propositions of Grossman and Krueger, Q J Econ, 110(2), 353-377 (1995) and Brock and Taylor (2005) for the case of seventy-seven regions of the Russian Federation from 1999 to 2015. To this end, we apply dynamic threshold regression due to its robustness in addressing non-linear asymmetry and unobserved individual heterogeneity issues. Our empirical investigation demonstrates that the gross regional products (GRP) per capita play a threshold role in supporting the EKC hypothesis and non-monotonic scale effect. Modern technologies used in production appear to be the driving factor in explaining the declining portion of EKC and corroborating the technology effect proposition. Our finding also affirms the effect of composition, as the impacts of energy and technology vary with regional economic growth orientations (e.g. among manufacturing, agricultural, mining, and trade zones). Environmental policy stringency is found to be effective in curbing regional carbon emissions. We provide several policy implications based on the results., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Within a framework that includes economic activity, real interest rate, grants, and subsidies, we aim to explore the role of renewable energy, technological innovation, and particularly the environmentally damaging militarization in driving green growth, which fosters sustainable economic growth by ensuring the values of natural assets, considering OECD countries. Our examination affirms a positive proposition between the development of renewable energy, technological innovation, and green growth in the long run by implementing the cross-sectional dependency panel autoregressive-distributed lags (CS-ARDL) framework in a dynamic heterogeneous panel setting. The findings also suggest that militarization is antagonistic to green growth. Our decomposed analysis is compatible with our premier analysis, indicating a conducive impact of both biomass and non-biomass types of renewable energy on green growth. We also document a negative association between the real interest rate (RIR) and green growth, while income muddles the results. The robustness tests confirm the sensitivity of our main findings to the magnitude of the subsidies and grants provided to renewable energy. The paper concludes with several policy recommendations., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This article revisits the nexus between financial development and environmental degradation by incorporating economic growth, electricity consumption and economic globalization in the CO 2 emissions function for the period 1975Q I -2014Q IV in the United Arab Emirates. We apply structural break and cointegration tests to examine unit root and cointegration between the variables. Further, the article also uses the Toda-Yamamoto causality test to investigate the causal relationship between the variables and tests the linkages of the robustness of causality by following the innovative accounting approach. Our empirical analysis shows cointegration between the series. Financial development increases CO 2 emissions. Economic growth is positively linked with environmental degradation. Electricity consumption improves environmental quality. Economic globalization affects CO 2 emissions negatively. The relationship between financial development and CO 2 emissions is U-shaped and inverted N-shaped. Further, financial development leads to environmental degradation, and environmental degradation in turn leads to financial development in the Granger sense.

This study assesses environmental Kuznets curve (EKC) hypothesis corroborating the role of scale, composite, and technology effects in OECD countries. To this end, we analyze the panel time series data from 1980 to 2017 using cross-sectional-autoregressive distributed lags (CS-ARDL). We document that economic growth and carbon emissions follow a U-shaped relationship, contrary to the EKC hypothesis, which our analysis attributes to the substantial contributions of the industrial, manufacturing, and service sectors to GDP. Technological progress has a somewhat marginal impact in reducing carbon emissions through energy efficiency but is unable to validate the existence of EKC hypothesis.

Middle-income countries are currently undergoing massive structural changes towards more industrialized economies. In this paper, we carefully examine the impact of these transformations on the environmental quality of middle-income countries. Specifically, we examine the role of sector value addition to GDP on CO 2 emission nexus for middle-income economies controlling for the effects of population growth, energy use, and trade openness. Using recently developed panel methods that consider cross-sectional dependence and allow for heterogeneous slope coefficients, we show that energy use and growth of industrial and service sectors positively explain CO 2 emissions in middle-income economies. We also find that population growth is insignificantly associated with CO 2 emission. Hence, our paper provides a solid ground for developing a sustainable and pro-growth policy for middle-income countries.

Background: Endoscopic submucosal dissection (ESD) has typically been performed using air insufflation. Recently, however, insufflation of CO2 has been increasingly used to avoid complications. This prospective study was designed to compare the CO2 concentration, intestinal volume, and acid-base balance using the duodenal balloon procedure., Methods: From June 2010 to February 2011, we enrolled 44 patients with esophageal or gastric cancer and randomly allocated them into two groups. We compared 22 patients undergoing CO2-insufflated ESD with a balloon placed into the duodenal bulb (duodenal balloon group) and 22 patients undergoing regular CO2-insufflated ESD (regular group). Three-dimensional computed tomography was performed before and after the procedure to measure intestinal volume. CO2 concentrations were measured every 10 minutes. The visual analogue system (VAS) scores for postoperative symptoms were recorded, and pH was measured immediately after the procedure. This was a prospective case control study randomized by the sealed envelope method., Results: Intestinal CO2 gas volume before and after ESD was lower in the duodenal balloon group than in the regular group (P = 0.00027). The end-tidal CO2 level was significantly lower in the duodenal balloon group than in the regular group (P = 0.0001). No significant differences in blood ΔpH were found between the two groups. The VAS score for the occurrence of nausea due to abdominal distension after ESD indicated a significant difference (P = 0.031)., Conclusions: ESD using the duodenal balloon occlusion method is effective for reduction of post-ESD intestinal CO2 gas volume, resulting in a lower total amount of CO2 insufflation during ESD and reducing harmful influences on the human body to some extent.