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Fiscal policy is a crucial government tool for influencing and managing the national economy and creating a strong incentive for low carbon investment. Previous literature has reputable evidence that improving fiscal policy enhances environmental quality. However, the literature fails to classify the exact turning level (threshold point) below/above which the association may be negative or positive. In this regard, this research investigates the nexus between fiscal policy, foreign direct investment, financial development, trade openness, urban population, gross capital formation, labour force, and CO2 emissions in the era of globalization. The panel data set contained 105 countries over the period from 1990 to 2016. The empirical findings are estimated through linear and nonlinear panel data approaches such as fully modified ordinary least square and panel threshold regression. The subsequent findings are established: first, fiscal policy and globalization significantly increase environmental pollution. Second, the empirical results confirm the existence of the pollution haven hypothesis (PHV). Third, financial development and gross fixed capital formation are also considered some of the most crucial indicators to increase pollution levels. Fourth, trade openness, urban population, and labour force improve environmental quality. Fifth, panel threshold regression discovers that countries maintain a minimum level of fiscal policy at −1.2889. Based on these empirical findings, this study suggests that policymakers and governments of these countries should take steps to restructure their industrial sector and design macroeconomic-level carbon-free policies to support the implementation of low-energy-intensive and lower carbon production technologies.

The majority of countries rely largely on agriculture for employment. Irrigation accounts for a sizable amount of water use. Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. It is critical to safeguard the field’s water supply. The shortage of fresh water is a major challenge for the world, and the situation will deteriorate further in the next years. As a result of the aforementioned challenges, smart irrigation and precision farming are the only viable solutions. Only with the emergence of the Internet of Things and machine learning have smart irrigation and precision agriculture become economically viable. Increased efficiency, expense optimization, energy maximization, forecasting, and general public convenience are all benefits of the Internet of Things (IoT). As systems and data processing become more diversified, security issues arise. Security and privacy concerns are impeding the growth of the Internet of Things. This article establishes a framework for detecting and classifying intrusions into IoT networks used in agriculture. Security and privacy are major concerns not only in agriculture-related IoT networks but in all applications of the Internet of Things as well. In this framework, the NSL KDD data set is used as an input data set. In the preprocessing of the NSL-KDD data set, first all symbolic features are converted to numeric features. Feature extraction is performed using principal component analysis. Then, machine learning algorithms such as support vector machine, linear regression, and random forest are used to classify preprocessed data set. Performance comparisons of machine learning algorithms are evaluated on the basis of accuracy, precision, and recall parameters.

Global warming and climate change have become one of the most embarrassing and explosive problems/challenges all over the world, especially in third-world countries. It is due to a rapid increase in industrialization and urbanization process that has given the boost to the volume of greenhouse gases (GHGs) emissions. In this regard, carbon dioxide (CO 2 ) is considered a significant driver of GHGs and is the major contributing factor for global warming. Considering the goal of mitigating environmental pollution, this research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression, and Holt’s methods for forecasting CO 2 emission. It attempts to forecast the CO 2 emission of Bahrain. These methods are evaluated for performance. The neural network model has the root mean square errors (RMSE) of merely 0.206, while the Gaussian Process Regression Rational Quadratic (GPR-RQ) Model has RMSE of 1.0171, and Holt’s method has RMSE of 1.4096. Therefore, it can be concluded that the neural network time series nonlinear autoregressive model has performed better for forecasting the CO 2 emission in the case of Bahrain.

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