Environmental Progress & Sustainable Energy最新文献

There are positive effects on climate, water availability, and temperature that may be attributed to the presence of mangrove forests. In addition, mangroves provide natural infrastructure and protection to human communities that are located nearby by preventing erosion and absorbing the effects of storm surges caused by large weather events such as hurricanes. Regrettably, the disappearance of mangroves harms the ecology and the environment's viability. Using secondary sources and a descriptive approach to study, this article investigates the impact that the destruction of mangrove forests in India has had on the surrounding ecosystem. The research findings indicate that India's most significant contributors to environmental destruction are development activities and other reasons. Deforestation may lead to many negative outcomes, including changes in climate, a reduction in biodiversity, the destruction of habitat, and armed conflict.

Environmental protection in the twenty-first century is more important than ever. Despite this, urbanization has led to an increase in the worldwide push for environmental sustainability and reducing pollution from human activities in the air, water, soil, etc. Pursuing a better quality of life has led humanity to use more of the planet's finite resources. This analysis focused on demographics such as age, gender, occupation, degree of education, and socio-economic status. This study focuses on the people of Andhra Pradesh, India, to gauge their familiarity with and outlook on climate change and other worldwide environmental concerns. People's judgments of the efficacy of individual action in coping with the adverse effects of environmental and climatic changes and the most visible changes in these areas are assessed. The results of this research will be used to shape future communication and public awareness campaigns, whether they are aimed at spreading specific ideas or policies. Thus, it is seen as more vital to examine the possibility of strengthening communication capabilities and public outreach than to determine whether or not common knowledge is accurate.

Biochar loaded with metal ions has been widely used to activate persulfate to degrade organic pollutants. However, the preparation conditions of catalysts are always controversial. In this study, the co-heating conditions of soybean straw with high carbon and low nitrogen and nano-iron oxide were systematically discussed by response surface method, and an efficient catalyst (Fe@BC) was obtained. Fe@BC can effectively activate sodium persulfate (PS) and degrade three synthetic azo dyes (Methyl orange (MO), Amino black 10B (AB10B), and Orange II) and rhodamine B (RhB). Under the optimum conditions, the removal rate of total organic carbon of these four dyes reached 41.1%–89.8% and the final degradation rate could reach 100%. The physicochemical properties of Fe@BC were studied by SEM, BET, XRD, XPS and TGA. The results revealed that the specific surface area of Fe@BC was 195.6m²/g. Further, reducing iron oxide by C generated C_0.09Fe_1.91 and zero-valent iron. Free radical scavenging experiments and electron paramagnetic resonance (EPR) measurements showed that the main reactive oxide species (ROS) in the Fe@BC/PS system were hydroxyl radicals (•OH) and singlet oxygen (¹O₂). Finally, Several Fe@BC modification systems were explored, the optimum pH of Fe@BC/PS system was analyzed, and the effects of Fe@BC and PS dosage on degradation performance were also studied. This study provides a scientific basis for the production of efficient catalysts and biochar energy, and an effective treatment scheme for printing and dyeing wastewater.

The high volatility and uncertainty of carbon price have always been two major challenges in carbon price forecasting. To solve these two challenges, an adaptive lower-and upper-bound estimation (LUBE) model with improved variational mode decomposition (VMD) and PSO-based interval optimization strategy is proposed for interval prediction of carbon price. To validate effectiveness and superiority, the adaptive LUBE model and several competitive models, including the bootstrap model, delta model, and Bayesian model, were utilized for interval prediction of carbon prices of Beijing and Shanghai. Compared with other models, the adaptive LUBE model not only has excellent coverage but also has the narrowest interval width in both training set and test set. Therefore, the excellent comparison results show that the proposed model can obtain a more reliable and higher-quality prediction interval, which can be a novel and effective carbon prices forecasting tool for governments and enterprises.

An electric utility bill often includes a fuel adjustment clause (FAC) charge that can be changed up or down to recoup the utility's fuel costs. The fuel adjustment clause was first introduced after fluctuations in coal prices during World War 1. The continued use of the FAC, which shifts the risk of fuel price spikes to the customer, is particularly concerning where energy burdens for vulnerable residents are high. To estimate the impact of an increase in natural gas prices on residential electricity bills because of the rise in the use of gas to generate energy in recent years. In 2020, electricity production accounted for around 33% of gas usage. One approach to address the high-energy burden is providing clean and efficient energy for households, reducing energy bills, and freeing up household resources for other necessities. To determine this; first, a few utilities were selected, then the residential rate schedules were examined so a model could be built to calculate the bill based on a range of usage levels. The selected utilities, Florida Power & Light, Duke Energy Florida, Duke Energy Progress, and Duke Energy Carolinas, all rely on different fuel mixes to serve customers. Our findings suggest that with a heavy reliance on gas and a direct pass-through of gas prices to customers, customers bear the risks of the volatile gas market. A slight increase in electric bills can have an outsized impact on customers, particularly low-income households and individuals on fixed incomes.