Improving energy efficiency and minimizing environmental concerns through environmental laws and green taxes are regarded as the primary motivating factors of climate change policy. This analysis clarifies the significance of green taxes in lowering energy use and intensity from 1994 to 2020. As part of our contribution to the literature on energy economics, this study examines how green taxes interact with energy intensity and consumption in four Nordic nations. Environmental policies and sustainable development goals (SDGs) are driving new research into the effects of green taxes on energy consumption and intensity. According to the outcomes of fully modified ordinary least square (FMOLS), panel dynamic ordinary least square (PDOLS), and panel quantile regression, a green tax helps to reduce total energy consumption. It increases energy efficiency by motivating governments, companies, and citizens to encourage innovation in environment-related technology. When it comes to creating a more sustainable environment, the study argues that regulations that ensure the displacement of non-renewable resources while increasing energy efficacy should be implemented.
Over the years, the economies of Mexico, Indonesia, Nigeria, and Turkey (the MINT countries) have had significant levels of economic growth. However, these countries have not been able to protect the quality of their environments simultaneously. As a result, the rising environmental indices in these nations cast a gloomy shadow over their capacity to continue their economic development. It has been shown that a more educated workforce may boost an economy’s absorption capacity and enhance the efficiency of green technology, both of which contribute to lower emissions of greenhouse gases. This article reports on research that examines the link between educating the labor force and environmental sustainability in the MINT economies. In order to conduct an empirical analysis of the data spanning the years 1995–2020, panel ARDL-PMG and NARDL-PMG techniques were used. First, the results of the ARDL-PMG demonstrate that a more highly educated workforce plays a vital role in mitigating CO2 emissions. Moreover, the NARDL-PMG’s results demonstrate that a positive component of a highly educated workforce is a large negative influence on CO2 emissions, whereas in the long run, the negative component of a highly educated workforce has a positive impact on CO2 emissions over time. This article recommends that the MINT nations’ authorities boost education and training for their workforces in order to keep CO2 emissions down.
Several economies have acknowledged that environmental degradation poses a serious danger to worldwide sustainable production and consumption. Policy makers concur that the increased use and production of carbon-intensive technologies has intensified the detrimental consequences of carbon dioxide emissions. In response, a number of nations have reacted by enacting stringent regulations and encouraging green technology innovations across corporate and governmental organizations. Evidence that already exists suggests that research and development is a cyclical process; nevertheless, the non-linear influence of shocks in research and development and innovation in green technologies on CO2 emissions in the Nordic nations has not been well investigated. Using panel data from 1995 to 2019, this research explores the asymmetric link between innovation in green technologies and CO2 emissions. The cointegration link between the chosen variables was validated using the Westerlund cointegration test and the Johansen–Fisher panel cointegration test. The findings of both tests confirm the presence of cointegration association between dependent and independent variables. The outcomes of CS-ARDL revealed that negative shocks in creating green technologies contribute to carbon dioxide emissions during recessions. Second, the findings supported the notion that innovation in green technology may reduce carbon dioxide emissions during times of economic expansion. Thirdly, the GDP increases the CO2 emissions, but the usage of renewable energy decreases CO2 emissions. In addition, the robustness analysis validated the consistency and precision of the existing findings. In summary, the findings suggest that the link between advances in environmentally friendly technologies and levels of carbon dioxide emissions were inversely proportional.
In the modern day, protecting data against tampering is a significant task. One of the most common forms of information display has been digital photographs. Images may be exploited in a variety of contexts, including the military, security applications, intelligence areas, legal evidence, social media, and journalism. Digital picture forgeries involve altering the original images with strange patterns, which result in variability in the image's characteristics. Among the most challenging forms of image forgeries to identify is Copy Move Forgery (CMF). It occurs by copying a portion or piece of the picture and then inserting it again, but in a different place. When the actual content is unavailable, techniques for detecting fake content have been utilised in image security. This study presents a novel method for Copy Move Forgery Recognition (CMFR), which is mostly based on deep learning (DL) and hybrid optimization. The hybrid Grey Wolf Optimization and African Buffalo Optimization (GWO-ABO) using Convolution Neural Network (CNN) technique i.e., GWO-ABO-CNN is the foundation of the suggested model. The developed model extracts the features of images by convolution layers, and pooling layers; hereafter, the features are matched and detect CMF. The MICC-F220, SATs-130, and MICC-F600 datasets were three publicly accessible datasets to which this methodology has been implemented. To assess the model's efficacy, the outcomes of implementing the GWO-ABO-CNN model were contrasted with those of other approaches.
El objetivo de la investigación fue determinar la competitividad de la tara peruana en el comercio internacional en el período 2010-2018, considerando las teorías de: World Economic Forum – WEF (2018), Cann 2016), Gómez (2017), Cordero (2005), Porter 2008), Daniels, Radebaugh, y Sullivan (2013), Lerma y Márquez (2010), con un proceso metodológico que definió un estudio exploratorio por los datos fragmentados existentes que se recolectaron y seleccionaron de instituciones gubernamentales: Ministerio de Agricultura y Riego-MINAGRI (2009, 2010, 2017 a, 2017 b), Ministerio de Agricultura y Riego-MINAGRI y Servicio Nacional Forestal y de Fauna Silvestre-SERFOR (2016, 2017, 2018, 2019), Sistema Integrado de Información de Comercio Exterior- SIICEX (2019), Superintendencia Nacional de Aduanas y de Administración Tributaria-SUNAT (2019) y particulares: Sociedad Nacional de Industrias-SIN (2018), así como, por los escasos estudios acerca de la unidad de análisis, complementado con métodos de análisis de contenido y frecuencia. Los resultados concluyen que la tara peruana no es competitiva debido que mientras su producción fue descendente desde el año 2010 (32736 TM) hasta el 2018 (25866 TM) su valor FOB tuvo un alza permanente desde el año 2010 (27075.9) a 39228. 49 el año 2018, que el cálculo de Costo de los Recursos Domésticos (CRD) analizo por el factor precio indicando que sólo el año 2016 manifestó competitividad porque el precio internacional por Kg. fue de cerca de $3.00 dólares americanos en relación al precio doméstico de S/. 1.51 nuevos soles, además, por no cumplir satisfactoriamente los 12 pilares del enfoque de competitividad del World Economic Forum-WEF (2018); que permiten reflexionar acerca del tránsito de la estructura de exportación basada en ventajas comparativas a su transformación sustentada en ventajas competitivas que el país andino tramita.
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