Global warming and its consequences have heightened the urgency of reducing emissions of carbon dioxide globally. The concern arises from countries’ relentless efforts to achieve economic development at the expense of the environment. In this context, the paper examines the Environmental Kuznets Curve (EKC) hypothesis at the world level using carbon emission as an indicator of environmental degradation. The EKC hypothesis postulates an inverted U-shaped curve between economic development and environmental degradation; degrading environmental quality at the initial stages of development and, after a threshold level, environmental degradation lowers. The study investigates the validity of the EKC hypothesis for carbon emission with an analysis of 158 countries in the world, with population, urbanization, forest cover, and tourist inflow as the control variables. The study is based on secondary data collected from the World Bank. A regression analysis is used for the study. To ensure environmental sustainability, it is important to identify the determinants of carbon emissions across countries with varying levels of economic development. The findings of the study support the hypothesized inverse U-shaped association between Gross Domestic Product per capita and carbon emission per capita at the world level. Out of the four control variables, urbanization and tourist inflow were found statistically significant. Urbanization was positively correlated with carbon emission per capita while forest area was negatively correlated. Carbon emission per capita initially increases with rising GDP per capita and declines after GDP per capita reaches a certain level. The estimated turning point of GDP per capita occurs at a high level and therefore, most of the countries are anticipated to emit carbon dioxide.

Cryptocurrency has seen an increased popularity with the introduction of Bitcoins. It has been adapted in several countries and has become an alternate solution to conventional currency. Despite its benefits, some controversies surround the manufacturing of bitcoins. While all the countries are moving to sustainability development and global warming control, Bitcoin production has raised several concerns about environmental pollution and sustainability. The increased carbon emissions and high electrical consumption have accompanied the popularity of cryptocurrency. Hence, there is an immediate need to reduce the carbon footprint and electricity consumption caused by human cryptocurrency for a sustainable future. This study presents the current scenario and trends of worldwide cryptocurrency growth and discusses the environmental impact of cryptocurrency mining. It explores crypto mining worldwide and provides a qualitative review. Further, this article highlights the need to take necessary measures to control cryptocurrency circulation.

Watershed Development is a very common phenomenon in the river basins in India due to its dynamic and continuously changing nature, which are interconnected via. Land use/land cover (LULC) change and water poverty scenario over time. In the present study, the samples were chosen from seven sampled villages for the Water Poverty Index (WPI) in the upper Tons River Basin. Among them, Ghunwara and Maihar Village exhibit the highest and lowest WPI, i.e., 98.1 and 62.91 out of 100, respectively. This indicates that villages with a high WPI face challenges in their water requirements, regardless of the seasonal river serving the basin area. Conversely, villages with a low WPI can satisfy their water needs solely from the basin. The present analysis of the Upper Tons River Basin suggests that Land Use and Land Cover (LULC) will undergo influences or adjustments at various stages, ultimately affecting agricultural land in the impact region. It also becomes evident that areas with limited land use and land cover (LULC) extensions exhibit lower Water Productivity Index (WPI), primarily due to their reliance on agricultural land. It is observed that alterations, reductions, or modifications in LULC lead to changes in multiple aspects of agricultural land, resulting in noticeable variations in various metrics. The present paper not only evaluates the land use in the Upper Tons River Basin spanning from 2001 to 2021 but also highlights the changing patterns that impact water resources and their utilization capacity. Furthermore, the study estimates the influence of reducing specific features on the distribution of WPI and other LULC parameters. The Upper Tons River Basin faces challenges such as unfavorable rainfall patterns and inadequate planning for irrigation at the fundamental and local levels. Additionally, its geographical location in a rainfed area negatively affects the WPI.

Natural ecosystems have been facing a major threat due to deforestation and forest fires for the past decade. These environmental challenges have led to significant biodiversity loss, disruption of natural habitats, and adverse effects on climate change. The integration of Artificial Intelligence (AI) and Optimization techniques has made a revolutionary impact in disaster management, offering new avenues for early detection and prevention strategies. Therefore, to prevent the outbreak of a forest fire, an efficient forest fire diagnosis and aversion system is needed. To address this problem, an IoT-based Artificial Intelligence (AI) technique for forest fire detection has been proposed. This system leverages the Internet of Things (IoT) to collect real-time data from various sensors deployed in forest areas, providing continuous monitoring and early warning capabilities. Several researchers have contributed different techniques to predict forest fires at various remote locations, highlighting the importance of innovative approaches in this field. The proposed work involves object detection, which is facilitated by EfficientDet, a state-of-the-art object detection model known for its accuracy and efficiency. EfficientDet enables the system to accurately identify potential fire outbreaks by analyzing visual data from the sensors. To facilitate efficient detection at the outbreak of forest fires, a bi-directional gated recurrent neural network (Bi-GRU-NN) is needed. This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. CSO is inspired by the intelligent foraging behavior of crows, and when combined with fractional calculus, it provides a robust optimization framework that improves the accuracy and efficiency of the AI model. Experimental analysis shows that the proposed technique outperformed the other existing traditional approaches with an accuracy of 99.32% and an error rate of 0.12%. These results demonstrate the effectiveness of the integrated AI and optimization techniques in enhancing forest fire detection and prevention. The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.

Climate change can impact farmers’ incomes as agricultural production still depends on the weather. Currently, the majority of the impoverished rely primarily on agriculture for their income. The connection between poverty and climate change has been extensively studied, but further research is needed in this area. This research was conducted to provide empirical evidence regarding the impact of climate change on poverty using time series data, which has never been done. This research wants to examine the impact of socio-economics (economic growth, agricultural sector growth, inequality, inflation) and climate change on poverty. This research uses time series data from 2007 to 2022. The Central Bureau of Statistics and Climate Change Performance Index (CCPI) reports are the sources of research data. The study results suggest that the government’s performance index in combating inflation, agricultural sector growth, and climate change has a positive impact on poverty. Poverty is negatively affected by the Gini index and economic growth. Government efforts to adaptively address climate change are necessary to prevent worsening impacts on poverty rates. To reduce the risk of crop failure, farmers must also practice practical agricultural management.

Agriculture is the crucial pillar and basic building block of our nation. Agriculture plays a key role as the major source of revenue for our nation. Farming is the primary financial source of India. Abrupt environmental changes affect crop yield prediction. Unpredictable climate changes, lack of water resources, deficiency of nutrients, depletion of soil fertility, unbalanced irrigation systems, and conventional farming techniques are the major causes of crop yield prediction. Today, AI, the use of machine learning, and deep learning techniques provide an achievable solution to improve crop yields. The key intent of the survey is to accurately predict and improve crop yield by combining agricultural statistics with machine learning and deep learning models. To accomplish this, we have surveyed the optimization algorithms implemented in conjunction with the Random Forest and Cat Boost models. A survey made across multiple databases to determine the effectiveness of crop yield prediction and analysis was performed on the included articles. The survey results show that a hybrid CNN DNN and RNN model with optimization algorithms outperforms the other existing traditional models.

Tannery wastewater contains a significant amount of chemical compounds, including toxic substances. Due to the toxicity and negative environmental effects of these tannery effluents, mandatory treatment is necessary. The main objective of this study was to treat effluent from an artisanal tannery in the city of Fez (Morocco) using the adsorption process with activated charcoal derived from olive pomace. The physicochemical characterization of tanning water included several parameters, such as chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), suspended solids (SS), sulfate ions (SO42-), nitrate, and chromium Cr(VI). The analyses show that the adsorption process reduced nitrate by 57.54%, sulfate by 94.08%, TKN by 74.84%, COD by 68.18%, Cr by 91.27%, and Cr (VI) by 89.78%. The activated charcoal was characterized before and after tannery effluent treatment using various techniques, including FT-IR, SEM, and EDX. From the above, it can be inferred that using activated carbon made from olive pomace has the potential to reduce tannery effluent pollution parameters. This innovative approach demonstrates that competitive results can be achieved without sacrificing economic viability, thereby promoting sustainable practices in the treatment of industrial liquid waste and wastewater treatment plants.

The two biggest environmental issues the world is currently dealing with are global warming and climate change. Minimizing energy consumption will help to cut down on greenhouse gas emissions, which is our responsibility. Companies choose ‘Carbon Footprint’ as a tool to calculate greenhouse gas emissions to show the impact of their activities on the environment. The techniques and procedures used in the analysis of carbon footprints are the primary focus of this study. Several criteria for evaluating carbon footprints were compared to one another to uncover parallels, variances, and deficiencies. Carbon footprints of companies and items were analyzed, and their objectives, ideas, topics of inquiry, calculation techniques, data choices, and additional elements were investigated. Standards for both organizations (ISO14064 and the GHG protocol) and products were compared and contrasted to arrive at accurate carbon footprint estimates. The most important aspects of a carbon footprint and assessment criterion are the research of GHG, system settings, measurement and carbon footprint, date, and treatment of individual emissions. Especially true for commercial enterprises and consumer goods. Guidelines have been produced for these challenges based on valuation criteria that have been used up to this point; nonetheless, they should be enhanced. This study highlights the need to formulate policies to reduce greenhouse gas emissions.

Climate change is a worldwide phenomenon that significantly impacts the area, production, and yield of crops. Changes in climate conditions have diverse effects on farming globally. For instance, an increase in temperature can make specific crops more vulnerable to pests. Similarly, a decrease in rainfall reduces water availability, affecting both irrigated and rainfed farming practices. This study aims to investigate climate change effects on crop area dynamics in the Cachar district of Assam, India, for a period spanning from 1981 to 2017. The time series ARDL (Autoregressive Distributed Lag) model is employed to analyze the relationship between climate factors and areas under different crops. As a pre-requisite condition for ARDL, the Augmented Dickey-Fuller (ADF) test is employed to check the order of integration of area under selected crops. The research reveals that the annual average temperature negatively affects the area dedicated to chickpeas, while annual average rainfall negatively impacts the areas allocated to rice and chickpeas. Conversely, annual average relative humidity has a significant positive impact on the area of these crops in the study region. Policymakers may consider strategies and policies for agriculture by encouraging the cultivation of crop varieties that are more resilient to climate change.

Supermarket applications are significant contributors to greenhouse gas emissions, necessitating efforts to reduce carbon footprints in the food retail sector. Carbon dioxide (R744) is recognized as a viable long-term refrigerant choice due to its favorable properties, including low Global Warming Potential, non-toxicity, non-flammability, affordability, and widespread availability. However, enhancing the energy efficiency of pure CO2 systems in basic architecture units, particularly in warm regions like India, remains a challenge. To address this, modern refrigeration systems must prioritize low energy consumption and high coefficient of performance (COP) while meeting environmental standards. This study investigates different operating conditions to determine the optimal parameter range for maximizing COP and improving the efficiency of conventional CO2 refrigeration configurations. It examines both subcritical and transcritical refrigeration cycles under varying parameters, emphasizing the importance of understanding COP’s relationship with factors such as subcooling, superheating, ambient temperature, and evaporator temperature. The study advises against superheating in CO2 systems but highlights the substantial COP increase with higher degrees of subcooling, leading to enhanced system performance. Additionally, it provides a comprehensive theoretical comparison between advanced pure CO2 supermarket applications and commonly used hydrofluorocarbons-based systems, offering insights into energy efficiency and environmental impacts for informed decision-making in the industry.