DAILY CURRENT AFFAIRS IAS | UPSC Prelims and Mains Exam –18th June 2024
Archives (PRELIMS & MAINS Focus) A NEW WAY TO PREDICT EL NIÑO, LA NIÑA Syllabus Prelims – GEOGRAPHY Context: Hyderabad-based Indian National Centre for Ocean Information Services (INCOIS) has developed a new product to predict the emergence of El Niño and La Niña conditions — they are different phases of El Niño Southern Oscillation (ENSO) — up to 15 months in advance. Background:- In India, while El Niño conditions usually lead to a weak monsoon and intense heatwaves, La Niña conditions result in a strong monsoon. About ENSO ENSO is a climate phenomenon that involves changes in the temperature of waters in the central and eastern tropical Pacific Ocean, coupled with fluctuations in the overlying atmosphere. It can alter the global atmospheric circulation, which, in turn, influences weather across the world. ENSO occurs in irregular cycles of 2-7 years and has three different phases — warm (El Niño), cool (La Niña), and neutral. In the neutral phase, the eastern side of the Pacific Ocean (near the northwestern coast of South America) is cooler than the western side (near the Philippines and Indonesia). This is due to the prevailing wind systems that move from east to west, sweeping the warmer surface waters towards the Indonesian coast. The relatively cooler waters from below come up to replace the displaced water. In the El Niño phase, however, wind systems weaken, leading to lesser displacement of warmer waters. As a result, the eastern side of the Pacific becomes warmer than usual. In contrast, a La Niña event is characterized by a period of colder than average sea surface temperatures in the central and eastern Pacific Ocean and an intensification of the prevailing east to west surface winds. What is the new product? Known as Bayesian Convolutional Neural Network (BCNN), the new product uses the latest technologies such as Artificial Intelligence (AI), deep learning, and machine learning (ML) to improve forecasts related to the ENSO phases. The model’s prediction relies on the fact that El Niño or La Niña are connected to the slow oceanic variations and their atmospheric coupling, which gives sufficient lead time to issue early forecasts. How does BCNN compare with existing models? There are largely two kinds of weather models used for forecasting. One is the statistical model, which generates forecasts based on various information sets received from different countries and regions. The other is the dynamic model, which involves a 3D mathematical simulation of the atmosphere done using High Performance Computers (HPC). The dynamic model is much more accurate than the statistical model. The BCNN, however, is a combination of the dynamic model with AI. This helps it forecast the emergence of El Niño and La Niña conditions with a 15-month lead time — unlike other models which can give a prediction up to six to nine months in advance. Source: Indian Express GOVT LOOKS AT INCOME TAX RATE CUT TO BOOST DEMAND, TRIGGER PRIVATE INVESTMENT Syllabus Prelims & Mains – ECONOMY Context: As the Indian economy grapples with the problem of flagging consumption, policymakers in the government are in favour of rationalising the existing income tax structure, especially at lower income levels. Background: In the most recent GDP data release for the January-March quarter on May 31, Private Final Consumption Expenditure (PFCE), an indicator of consumption demand, dropped as a share of GDP to 52.9 per cent — the lowest level in the 2011-12 base year series. Key Takeaways According to senior government officials, it is likely that tax rate cuts for those earning less may likely take precedence over freebies or excessive welfare spending given the focus on fiscal consolidation. The tax cuts may be a more efficient measure to enhance disposable income, which in turn would result in higher consumption, and give a fillip to economic activities. A boost to consumption is being seen as crucial for reviving demand, which in turn is central to restarting the investment cycle, especially rekindling private capital expenditure in consumer-focused sectors. The revenue loss from any such measure requires a dynamic analysis, officials said. “Since it is expected to spur demand, it requires a general equilibrium analysis to assess the net effect. There will be more money in the hands of the people, leading to better consumption and more direct and indirect tax revenues. So even though revenue loss might be there, the net effect will be positive,” the official said. In the most recent GDP data release for the January-March quarter on May 31, Private Final Consumption Expenditure (PFCE), an indicator of consumption demand, dropped as a share of GDP to 52.9 per cent — the lowest level in the 2011-12 base year series. The government has been focusing on fiscal consolidation over the last few years, with an aim to bring down the fiscal deficit to 5.1 per cent of the GDP in 2024-25 and reduce it further to below 4.5 per cent in 2025-26. The discussions have taken note that the rise in marginal income tax is “too steep” in the existing tax structure. Right now, in the new tax system, first slab of 5 per cent starts at Rs 3 lakh. By the time it goes to Rs 15 lakh, which is five times, the marginal tax rate jumps from 5 per cent to 30 per cent — a six fold jump. So while income goes up five times, the marginal tax rate goes up six times, which is quite steep. Tax simplification is also being seen as a better tool than overt spending on welfare schemes, which may see possible leakages. Source: Indian Express TARAKNATH DAS Syllabus Prelims – HISTORY Context: Birth anniversary of Shri Taraknath Das was celebrated recently. Background: He was a pioneering immigrant in the west coast of North America and discussed his plans with Tolstoy, while organising the Asian Indian immigrants in favour of the Indian independence movement. About TARAKNATH DAS Taraknath Das (15 June 1884 – 22 December 1958) was an Indian revolutionary and internationalist scholar. Tarak was born at Majupara, in the 24 Parganas district
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