Assessing the long- and short-run asymmetrical effects of climate change on rice production: empirical evidence from India

Environmental Science and Pollution Research - In recent years, environmental change has arisen as a ubiquitous problem and gained environmentalist’s attention across the globe due to its...     

People’s attitude towards willingness-to-pay for environmental protection in Pakistan

Environmental Science and Pollution Research - Recent environmental research has found that people with higher incomes and in more developed countries are more willing to pay (WTP) to protect their...     

Spillovers from global economic policy uncertainty and oil price volatility to the volatility of stock markets of oil importers and exporters

Economic policy uncertainty generally tends to induce a pessimistic view of future market behaviour. Furthermore, instabilities in global oil prices have serious implications for the economies of oil exporters and importers, due to their over-dependence on crude oil for revenue and production activities, respectively, and thereby on stock market indices. Against limited empirical evidence, this study examines the spillover effects from global economic policy uncertainty (GEPU) and oil price volatility to the volatility of the stock market indices of oil exporters and importers in both developed and emerging economies. The results show that the spillover effect from GEPU to oil exporters is relatively smaller than to oil importers, for both developed and emerging countries. Conversely, the volatility spillovers from oil prices to oil exporters are relatively larger than to oil importers, for both developed and emerging countries. Specifically, the volatility spillovers from oil prices to oil exporters (importers) in emerging countries are relatively stronger compared to oil exporters (importers) in developed countries. The findings indicate that the volatility of the stock markets of emerging countries is more sensitive to global factors such as GEPU and oil price volatility, and that oil exporters and importers in emerging economies are more sensitive to oil price volatility than oil exporters and importers in developed economies, which is in line with previous studies.

     

Evaluating the groundwater potential of Wadi Al-Jizi,Sultanate of Oman,by integrating remote sensing and GIS techniques

Environmental Science and Pollution Research - Groundwater resources are highly stressed due to their overuse, especially in the arid region. This study is aimed at discovering potential...     

The impacts of organizational green culture and corporate social responsibility on employees’ responsible behaviour towards the society

Environmental Science and Pollution Research - Corporate social responsibility (CSR) initiatives and organizational green culture (OGC) play a significant role in developing organizations and...     

Climate change and major crop production: evidence from Pakistan

Environmental Science and Pollution Research - Climatic changes are posing serious threats to crop production and food insecurity across the globe. This study explores the dynamic relationship...     

Land cover mapping based on random forest classification of multitemporal spectral and thermal images

Thematic mapping of complex landscapes, with various phenological patterns from satellite imagery, is a particularly challenging task. However, supplementary information, such as multitemporal data and/or land surface temperature (LST), has the potential to improve the land cover classification accuracy and efficiency. In this paper, in order to map land covers, we evaluated the potential of multitemporal Landsat 8’s spectral and thermal imageries using a random forest (RF) classifier. We used a grid search approach based on the out-of-bag (OOB) estimate of error to optimize the RF parameters. Four different scenarios were considered in this research: (1) RF classification of multitemporal spectral images, (2) RF classification of multitemporal LST images, (3) RF classification of all multitemporal LST and spectral images, and (4) RF classification of selected important or optimum features. The study area in this research was Naghadeh city and its surrounding region, located in West Azerbaijan Province, northwest of Iran. The overall accuracies of first, second, third, and fourth scenarios were equal to 86.48, 82.26, 90.63, and 91.82 %, respectively. The quantitative assessments of the results demonstrated that the most important or optimum features increase the class separability, while the spectral and thermal features produced a more moderate increase in the land cover mapping accuracy. In addition, the contribution of the multitemporal thermal information led to a considerable increase in the user and producer accuracies of classes with a rapid temporal change behavior, such as crops and vegetation.