Plastic pollution is a global concern affecting water, soil, and air quality. Urgent action is needed to address this issue. This study aims to identify factors influencing the use of biodegradable plastic to reduce its negative impacts. Data were collected from 269 households-129 in Punggawan and 140 in Mojosongo, Surakarta, and analyzed using multiple regression analysis to study the determinants of WTP (Willingness to Pay) for biodegradable plastic with STATA software. The results show that the average WTP for biodegradable plastic is IDR 2,214. Most people in Surakarta are already environmentally conscious. Age, knowledge, occupation, interaction of sex and location, education, and marital status influence WTP for biodegradable plastic. It is hoped that the implications of the research will be used as a recommendation for government policies to reduce the amount of plastic waste generation, which is a danger to human beings and the environment.

This study investigates the use of thermal indexes, specifically Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI), to determine outdoor comfort in the Bogor Botanical Gardens (KRB). This park is centrally located in Bogor city, with elevations ranging from 215-260 m above sea level. The thermal sensation was determined using seven references: PET in Europe, Taiwan, Tianjin, Tel Aviv, and UTCI in the Mediterranean, Tianjin, and general contexts. The study involved 284 visitors surveyed for their thermal comfort perceptions. Findings indicate that, based on thermal sensation criteria from the seven references, KRB is generally not within the comfort zone throughout the year, except for the PET in Taiwan, which is comfortable year-round. In-situ measurements show an average daily PET of 33.8°C and UTCI of 34.4°C. According to the Taiwan PET range, the thermal sensation is categorized as somewhat warm to warm (uncomfortable). However, 69.4% of visitors reported feeling comfortable, likely due to the environmental conditions, with 70.3% tree coverage in the 54.7 ha park area.

Agriculture has been a vital sector for the majority of people, especially in countries like India. However, the increasing need for food production has led to intensive farming practices that have resulted in the deterioration of soil quality. This deterioration in soil quality poses significant challenges to both agricultural productivity and environmental sustainability. To address these challenges, advanced soil nutrient prediction systems that utilize machine learning and deep learning techniques are being developed. These advanced soil nutrient prediction systems utilize various sources of data, such as soil parameters, plant diseases, pests, fertilizer usage, and changes in weather patterns. By mapping and analyzing these data sources, machine learning algorithms can accurately predict the distribution of soil nutrients and other properties essential for precise agricultural practices. A previous study compared machine learning algorithms like SVM and Random Forest with deep learning algorithms CNN and LSTM for predicting crop yields. The most appropriate model is a significant challenge, but several studies have evaluated recommendation system models using deep machine learning techniques. Deep learning models attain accuracy above 90%, while many ML models achieve rates between 90% and 93%. Furthermore, the research seeks to propose specific crop suggestions grounded in soil nutrients for precision agriculture to enhance crop productivity.

Sandy soils are not suitable for agriculture because they do not retain nutrients, and water drains quickly. The biochar applied to these soils provides nutrients, improves their fertility, and favors crop yields. Thus, this work aimed to evaluate the effect of the application of pine biochar and the pruning of green areas obtained by slow pyrolysis on the physicochemical attributes of sandy soil. For this purpose, a greenhouse experiment was conducted in fifteen pots randomly divided into three groups (five replicas) of treatment depending on the dose of biochar: 0% (0 g/pot, T1 control treatment), 10% (100 g/pot, T2), and 25% (250 g/pot, T3) calculated according to the volume of the soil. Likewise, 05 seeds of turnip (Brassica rapa subsp. rapa) were placed in each pot, where their germination and growth were monitored. Application of biochar reported an increase in organic matter, porosity, pH, electrical conductivity, cation exchange capacity, NO3-, K, and Mg (without significant differences) and a reduction in bulk density, P, and Ca (without significant differences). These behaviors were higher in T3, followed by T2, compared to T1. Similarly, T3 (68%, 7.5 ± 0.9 cm) showed a higher number of turnip germinations and growth compared to T2 (48%, 7 ± 0.6 cm) and T1 (28% 6 ± 0.4 cm). The biochar applied improved the attributes of the sandy soil, strengthening it against possible erosion and promoting the preservation of terrestrial ecosystems.

This research aimed to identify the most appropriate probability distribution for modeling average monthly rainfall in the agro-climatic zones of West Bengal and to detect any trends in this data. The study utilized historical rainfall data spanning 51 years (1970-2020) obtained from the IMD in Pune. To determine the best-fitting distribution and assess trends, 23 different probability distributions were employed, with the Mann-Kendall test and Sen’s slope estimator used for trend analysis. Goodness-of-fit tests, including the Kolmogorov-Smirnov, Anderson-Darling, and Chi-square tests, were employed to determine the most suitable distribution. The findings indicated that the Generalized Extreme Value, Gamma, and Lognormal (3-parameter) distributions were the best fits for two specific districts. The monthly rainfall distributions can be effectively used for predicting future monthly rainfall events in the region. The Mann-Kendall test revealed an increasing trend in rainfall for Kalimpong and Nadia Districts and a decreasing trend for Malda District.

To increase productivity and avoid waste, the construction industry has started implementing Lean ideas and methodologies in construction projects. Due to a lack of awareness of lean practices in the preparation, design, and execution of building and infrastructure projects, lean practices are not very familiar among construction projects, which are most commonly used in the manufacturing industry. Hence, an effort has been made in this paper to provide a comprehensive review of the literature and case studies to analyze the suitability of lean practice in sustainable waste management, increased productivity, and on-time project delivery. It aims to explore the effect of improving communication and fostering collaboration among stakeholders on time, costs, and resource management. The review identified the most commonly applied lean practices, Just in Time (JIT) and Last Planner System (LPS), and linked the adoption of lean techniques within the construction sector to a total of sixteen distinct benefits for the economy, society, and the environment. According to this study, lean techniques have a strong chance of boosting productivity in the construction industry and developing a sustainable built environment, but they also need to be used widely and continuously to achieve these goals.

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.

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.

Prevention of the occurrence and development of emergencies of a natural and man-made nature is one of the basic fundamental foundations of ensuring the national security of any state. The most important mechanism for preventing emergencies is an effective system of monitoring and forecasting emergencies established at the state level. In the process of functioning such a system, one of the main urgent problems requiring constant attention, continuous research, system analysis, and the search for solutions by scientific methods and methods is to increase the reliability of emergency forecasts. In this format, special attention is currently being paid worldwide to a comprehensive assessment of the adverse consequences of emergency situations, primarily related to the safety of the population, environmental conservation, and environmental safety. From the standpoint of solving this significant scientific and practical problem, the purpose of this work was to develop and justify a more advanced method for calculating the feasibility of forecasts of emergencies with environmental consequences as a tool for a reasonable detailed assessment of the quality, optimality of emergency forecasting processes and the reliability of the forecasts themselves.

Indonesia must establish a policy on the application of technology for mitigating greenhouse gas emissions because it is the nation that produces the most palm oil. When evaluating different technologies, policymakers should consider how much the technology will cost compared to the potential emissions abated, in terms of marginal abatement cost (MAC), which reflects priorities in the form of marginal abatement cost curves (MACC). The objective of this research is to evaluate and estimate the ranking of MAC from eight mitigation technologies used in Indonesia’s palm oil sector between 2020 and 2030. The least MAC is given as technology ranked first, namely the high-capacity boiler, with a value of $-19.61/tonne CO2eq followed by the high-efficiency steam turbine with $-7.2/tonne CO2eq, and the POME-to-biogas technology with $-0.1/tonne CO2eq. Additionally, the MAC of five additional technologies is positive, suggesting that implementation expenses were incurred. Subsequently, a sensitivity analysis is performed to see which technology ranks are impacted by interest rate fluctuations. Biogas upgrading technology is therefore liable to changes in the discount rate, which occur at different values. Other mitigation technologies, however, are also increasing their parameters, although less significantly than biogas upgrading, therefore this has no bearing on mitigation technology ranking.