Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2024/r/gumushane/14184
  A different look at the environmental Kuznets curve from the perspective of environmental deterioration and economic policy uncertainty: evidence from fragile countries

  Anwar, Ahsan; Barut, Abdulkadir; Pala, Fahrettin; Kilinc-Ata, Nurcan; Kaya, Emine; Lien, Duong Thi Quynh

  Environmental degradation is one of the most significant issues that developing nations confront and needs to be resolved right away in order for them to achieve sustainable development. Government policies are crucial in this situation since emerging nations frequently struggle with the issue of policy ambiguity, which can result in environmental deterioration. In this context, this study investigates how policy uncertainty affects environmental degradation in the five fragile emerging economies known as the Fragile Five—Brazil, India, Indonesia, South Africa, and Turkey. Using data from 1996 to 2019, we estimate a Panel Quantile Regression analysis. The empirical findings indicate that economic policy uncertainty and technology innovation increases the environmental degradation whereas environmental degradation is slowed down by financial development and renewable energy consumption. Empirical evidence also confirms the presence of EKC hypothesis in fragile economies. Based on the findings, we suggest both a policy and an environmental framework for achieving sustainable development in fragile economies. Graphical Abstract: (Figure presented.)

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• MakaleAçık Erişim2023/r/gumushane/12823
  Does ecological footprint in ECCAS and ECOWAS converge? Empirical evidence from a panel unit root test with sharp and smooth breaks

  Çelik, Orkun; Adali, Zafer; Bari, Bilgin

  The costs due to climate change have been increasing day by day. In addition to the risk of losing our planet’s natural assets due to the increasing destructiveness of climate-related natural disasters and extreme climate events, we are also faced with grave economic risks. For this reason, researchers have recently focused on environmental issues. Nevertheless, they generally have investigated developed countries and ignored developing and least developed countries such as African countries. The United Nations (2015) report highlights that African countries should be specially investigated. Hence, the paper analyzes whether the ecological footprint in the Economic Community of Central African States (ECCAS) and Economic Community of West African States (ECOWAS) converges for the period from 1961 to 2017. We employ Bahmani-Oskooee et al.’s (2014) panel unit root test with sharp and smooth breaks. The empirical findings demonstrate that the ecological footprint is stationary in ECCAS and ECOWAS. Stated in other words, the ecological footprint in these countries converges. Therefore, policymakers could implement similar policies to reduce the ecological footprint in these countries. This policy framework paves the way for an effective sustainable development.

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• MakaleYalnız künye2022/r/gumushane/14870
  Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset

  Günen, Mehmet Akif

  Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, mapping, and conservation of wetlands is a challenging and time-consuming process. Because they produce temporal and geographical information, remote sensing and photogrammetric approaches are useful tools for analyzing and managing wetlands. In this study, the water areas of five different wetlands obtained with Sentinel-2 images in Turkey were classified. Although obtaining large amounts of high-dimensional dataset labeled for various land types is costly, it is a significant advantage to use it after model training in a wide range of applications. In this paper, the EuroSAT dataset was used in the validation process. Proposed deep learning–based 1D convolutional neural networks (CNN) and traditional machine learning methods (i.e., support vector machine, linear discriminant analysis, K-nearest neighborhood, canonical correlation forests, and AdaBoost.M1) were compared quantitatively (i.e., accuracy, recall, precision, specificity, F-score, and image quality assessment metrics) and qualitatively. Finally, pairwise comparison was made with chi-square-based McNemar’s test. There is a statistical difference between 1D CNN and machine learning method (except the support vector machine vs linear discriminant analysis in Test 1 area). CNN models outperform machine learning algorithms in terms of non-linear function approximation and the ability to extract and articulate data features. Since 1D CNNs can process data in a highly complex and unique feature space, they are very successful in segmenting strongly related and highly correlated discrete signals. It also has advantages over machine learning methods for water body extraction in that it can be integrated with sophisticated image pre-processing and standardization tools, is less susceptible to low-level random noise, and provides shift in variations and contrast-invariant image local transforms.

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• Derleme MakaleYalnız künye2021/r/gumushane/11313
  Assessment of the relationship between renewable energy and employment of the United States of America: Empirical evidence from spectral Granger causality

  Çelik, Orkun

  In this study, the spectral Granger causality nexus between renewable energy generation and employment for the period February 1973–September 2019 in the USA is examined. The results obtained from the study expresses that there is no spectral Granger causality relationship between renewable energy generation and employment for aforementioned period in the USA. Furthermore, these findings support neutrality hypothesis, which means that there is no causality relation between the variables. In other words, renewable energy generation does not influence employment and employment does not have an effect on renewable energy generation.

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• MakaleYalnız künye2015/r/gumushane/15583
  Assessment of metal pollution associated with an alteration area: Old Gümüşhane, NE Black Sea

  Vural, Alaaddin

  The objective of this study was to determine the potential environmental risks associated with the Kirkpavli (Old Gümüşhane in northern Turkey) alteration area by quantifying pollution in soil. The Kirkpavli (Old Gümüşhane) alteration area is situated at the south of the deposit with the same name of gold-silver-bearing lead, zinc, and copper in the southern part of the Black Sea Tectonic Unit (Eastern Pontides). In this study, 28 soil samples acquired from the alteration area were analyzed for contents of some main elements including lead (Pb), copper (Cu), zinc (Zn), cadmium (Cd), cobalt (Co), manganese (Mn), nickel (Ni), and arsenic (As). Contents of the elements in the area were determined, and high values were obtained such as 1,171 mg/kg for As, 12.4 mg/kg for Cd, 77.3 mg/kg for Co, 341 mg/kg for Cu, 1,172 mg/kg for Mn, 51.9 mg/kg for Ni, 3,725 mg/kg for Pb, and 880 mg/kg for Zn. Soil contamination was appraised on the basis of Geoaccumulation Index (Igeo), Enrichment Factor (EF), Pollution Index, and Integrated Pollution Index. The calculated results of Igeo, EF, and PI of the elements can be shown in descending order of parameters as As > Pb > Cd > Zn > Cu > Co > Mn. The parameters for some of these elements indicated extremely high contamination (Igeo > 5), extremely high enrichment (EF > 40), and high pollution (PI > 3). High Igeo, EF, and PI values of As, Pb, and Cd in the soil samples mean that soil pollution is typically associated with alteration area. Considering its location and the results of this study, the Kirkpavli alteration area is a significant source of pollution and may have ecotoxicological effects on terrestrial, groundwater, and aquatic ecosystems in the region.

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