INTEGRATED MAINS AND PRELIMS MENTORSHIP (IMPM) 2025 Daily KEY
| Exclusive for Subscribers Daily: Inflation for the UPSC Exam? Why are topics like Marine heatwave and Migrant Worker important for both preliminary and main exams? Discover more insights in the UPSC Exam Notes for December 30, 2024 |
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Critical Topics and Their Significance for the UPSC CSE Examination on December 30, 2024
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Why inflation will matter more in 2025?
For Preliminary Examination: Consumer Price Index (CPI), Wholesale Price Index (WPI), Inflation
For Mains Examination: GS III - Economy
Context:
It has been what one may call, a bittersweet year for the economy, with a sweet first half, when growth surprised everyone, including the government. And a difficult latter half by the end of which the Reserve Bank of India (RBI) and Finance Ministry mandarins are sparring, as diplomatically as possible, over who is to be blamed for sputtering growth, a persistently resurgent inflation trajectory and the flux this creates for policy making
Read about:
Wholesale Price Index (WPI)
Consumer Price Index (CPI)
Key takeaways:
- Since late November, senior government officials have been vocal about the need for the RBI to reduce interest rates, emphasizing growth and investment over volatile food prices. This urgency became evident after Q2 GDP figures revealed a 7-quarter low growth rate of 5.4%, accompanied by weakened urban demand and lackluster corporate earnings that affected market sentiment.
- Despite these challenges, India's stock markets hit record highs, and GDP for the first half of 2023-24 rose to 7.7%, up from 7.2% in the previous year.
- The Finance Ministry anticipated full-year growth exceeding 6.5% while preparing an interim Budget ahead of the general elections. The RBI was expected to initiate rate cuts in late 2024, projecting inflation to average its target of 4% by July-September.
- The NSO's growth estimates for 2023-24 exceeded expectations, bolstered by revised earlier data. The first half grew by 8.1%, with an 8.6% surge in Q3 (October-December 2023) and 7.8% in Q1 2024, culminating in an impressive 8.2% annual growth.
- However, the 2024-25 Q1 period was dominated by election activities, during which the BJP secured 240 seats in the Lok Sabha, allowing the NDA to retain power with support from allies. Key economic portfolios saw continuity in leadership.
- In July, Finance Minister Nirmala Sitharaman presented the Union Budget for 2024-25, focusing on employment, the middle class, skilling, and job creation. Modest tax reliefs aimed to mitigate inflation's impact and boost consumption.
- While private investment recovery depended on sustained consumption, public infrastructure spending remained crucial, with a capex plan of ₹11.11 lakh crore announced. The government remained optimistic about achieving a fourth consecutive year of 7%+ growth.
- Initial growth data for 2024-25 showed a five-quarter low GDP increase of 6.7%, attributed to delays in capital spending during the prolonged election period. While optimism persisted, concerns about high interest rates dampening urban demand emerged.
- By September, S&P Global Ratings forecast 6.8% growth for the year, below the RBI's 7.2% projection. Following a disappointing Q2, various entities, including the Finance Ministry, revised growth projections to around 6.5%, raising fears of a cyclical slowdown.
- Although inflation stayed below 6% for most of the year, it reached a five-year low of 3.5% in July before rebounding due to rising food prices in September, pushing headline inflation closer to 6%.
- Consequently, the RBI refrained from cutting rates in October or later, citing persistent inflation risks. Its 2024-25 growth projection was reduced to 6.6% from 7.2%.
- High interest rates and inflation continued to strain consumption and investments, prompting tensions between the government and the RBI over economic priorities.
- As 2025 approaches, the delicate balancing act between growth and inflation remains a critical focus, alongside potential global uncertainties stemming from U.S. political developments
Inflation refers to the rate at which the general level of prices for goods and services rises over a period, leading to a decrease in purchasing power. It is a key economic indicator that affects both consumers and policymakers. Moderate inflation is considered normal in a growing economy, but excessive inflation (hyperinflation) or its opposite, deflation, can signal economic instability.
Causes of Inflation
- Demand-Pull Inflation: When demand for goods and services exceeds supply, prices rise.
- Cost-Push Inflation: Higher production costs, such as wages and raw materials, drive up prices.
- Built-In Inflation: When businesses increase prices to compensate for higher wage demands, creating a feedback loop.
- Monetary Factors: An increase in the money supply without a corresponding increase in goods and services can devalue currency, causing inflation.
Measuring Inflation:
- Consumer Price Index (CPI): Tracks the price changes of a basket of consumer goods and services over time.
- Wholesale Price Index (WPI): Measures the price changes at the wholesale level.
- Producer Price Index (PPI): Examines price changes from the producer's perspective
- Bank rate
- Open Market Operations
- Public debt
- Public revenue
Which of the above is/are component(s) of Monetary Policy?
(a) 1 only
(b) 2, 3 and 4
(c) 1 and 2
(d) 1, 3 and 4
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Answer (c)
Monetary policy refers to the tools and actions undertaken by a country's central bank (in India, the Reserve Bank of India) to regulate the money supply, control inflation, stabilize the currency, and achieve economic growth. Components of Monetary Policy:
Not Part of Monetary Policy:
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- A heatwave refers to an extended period of abnormally high temperatures, often significantly above the average for a particular region and season. These events are typically driven by atmospheric conditions, such as high-pressure systems that trap warm air near the surface.
- The absence of cloud cover allows the sun’s heat to accumulate, further intensifying the temperature. Heatwaves can last several days to weeks and have significant implications for human health, agriculture, and infrastructure.
- For instance, prolonged exposure to extreme heat can lead to dehydration, heatstroke, and other health issues, especially for vulnerable populations such as the elderly and children. Additionally, heatwaves can exacerbate drought conditions, harm crops, and strain energy systems due to increased demand for cooling.
- On the other hand, a marine heatwave occurs when ocean water temperatures rise significantly above the seasonal norm for an extended period, often for days, weeks, or even months.
- These anomalies can occur due to changes in ocean currents, reduced mixing of cooler deep waters with surface waters, or prolonged atmospheric warming. Marine heatwaves have profound ecological impacts, disrupting marine ecosystems and food chains.
- For example, corals can experience bleaching, a stress response to elevated temperatures, while fish and other marine species may migrate to cooler waters, impacting local fisheries. Additionally, marine heatwaves can influence weather patterns, intensify cyclones, and contribute to rising sea levels due to thermal expansion.
- Both heatwaves and marine heatwaves are becoming more frequent and severe due to global warming. As the atmosphere and oceans absorb more heat from greenhouse gas emissions, these extreme events pose increasing risks to ecosystems, economies, and societies worldwide
1. Appropriate alternate technologies are not sufficiently available.
2. India cannot invest huge funds in research and development.
3. Many developed countries have already set up their polluting industries in India.
Which of the statements given above is/are correct?
(a) 1 and 2 only
(b) 2 only
(c) 1 and 3 only
(d) 1, 2 and 3
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Answer (a) India faces challenges in addressing Global Warming: Developing and underdeveloped nations lack access to advanced technologies, resulting in a scarcity of viable alternatives for combating climate change. Being a developing nation, India relies partially or entirely on developed countries for technology. Moreover, a significant portion of the annual budget in these nations is allocated to development and poverty alleviation programs, leaving limited funds for research and development of alternative technologies compared to developed nations. Analyzing the statements provided: Statements 1 and 2 hold true based on the aforementioned factors. However, Statement 3 is inaccurate as the establishment of polluting industries by developed countries within India is not feasible due to regulations governing industrial setup |
- Tropical cyclones are among the most destructive natural disasters, capable of causing immense damage and loss of life. While the North Indian Ocean basin experiences fewer cyclones compared to other regions, its densely populated coastal areas make it highly vulnerable to their impacts.
- This vulnerability was starkly demonstrated by the Bhola cyclone of 1970, the deadliest on record. Observations suggest shifts in the behavior, strength, and frequency of tropical cyclones, emphasizing the need for adaptive strategies in at-risk areas.
- The Bay of Bengal sees a higher number of tropical cyclones than the Arabian Sea. However, the Arabian Sea has witnessed a 52% increase in cyclonic storm frequency in recent years, along with a threefold rise in the duration of very severe cyclonic storms.
- Cyclonic storms are now more likely to escalate into severe cyclones, and satellite-era data reveal an upward trend in accumulated cyclone energy over the North Indian Ocean. These changes are influenced by environmental factors, including rising ocean heat content and reduced vertical wind shear.
- Climate change scenarios suggest that anthropogenic warming will intensify tropical cyclones in the future. Precipitation rates during cyclones are expected to rise due to increased atmospheric moisture, while ocean basins may experience more frequent rapid intensification events, a poleward shift in peak intensity latitudes, and slower cyclone movements.
- Cyclone Fengal serves as a significant example of unusual cyclone behavior and its severe consequences. Emerging as a low-pressure system over the Bay of Bengal on November 23, it struck Tamil Nadu's coast near Puducherry on November 30.
- Remarkably, it stalled after landfall due to rare balanced steering winds, sustaining its intensity until December 1, fueled by moisture from saturated soils. This stalling led to unprecedented rainfall, with areas in Puducherry and Villupuram districts recording 40-50 cm in a single day, while neighboring districts such as Cuddalore and Tiruvannamalai experienced over 20 cm in 24 hours. The flooding caused widespread damage to farmland, resulting in catastrophic losses for farmers and local communities.
- The India Meteorological Department (IMD) has made significant advancements in predicting cyclone tracks and landfalls. However, Fengal's unpredictable behavior posed substantial challenges.
- While the IMD accurately forecast its landfall near Puducherry three days in advance, some aspects, such as its northeastward movement on November 27 and its stalling near the coast, were difficult to predict.
- Additionally, weather models struggled to anticipate the exceptionally heavy rainfall, with some areas exceeding 40 cm in 24 hours. These forecasting limitations are partly due to insufficient oceanic observational data and the complex cloud dynamics within cyclones.
- Improving predictions of tropical cyclone intensity, especially rapid intensification, and accurately forecasting heavy rainfall during landfall remain critical research priorities. As IPCC climate models indicate stronger cyclones with heavier precipitation and slower movement, addressing these gaps is urgent.
- The post-monsoon cyclone activity of 2024 underscores the necessity of sustained investments in advanced forecasting technologies and research to overcome existing challenges.
- Despite notable progress, achieving precise cyclone predictions continues to be a formidable task, requiring prioritization of measures to protect lives, livelihoods, and ecosystems from their devastating impacts
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Answer (c)
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Uttar Pradesh, Maharashtra, and Madhya Pradesh also rank among the top five states. An analysis of Indian Railways data on unreserved tickets for non-suburban passengers traveling over 150 kilometers indicates that while most states saw a decline in inbound passengers, likely due to reduced migration overall, West Bengal, Rajasthan, and Karnataka experienced the highest growth in the percentage share of arriving passengers.
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Compared to 2012, Andhra Pradesh and Bihar have dropped a rank. The report, titled "400 Million Dreams!" and published by the Prime Minister's advisory council, highlights an 11.78% decline in the overall migrant population since the 2011 Census.
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The report suggests that better economic opportunities in smaller cities may be contributing to the slowdown in migration.
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Data from the Indian Railways Unreserved Ticketing System (UTS) for 2012 and 2023, combined with mobile phone roaming data and district-level banking remittance records, was used by the EAC-PM to estimate that India had 402 million migrants in 2023, down from 455.7 million recorded in the 2011 Census.
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The analysis shows that Gujarat, Uttar Pradesh, Bihar, Karnataka, and Telangana are the primary source states for migrants traveling to Maharashtra. Similarly, Uttar Pradesh, Bihar, Rajasthan, Haryana, and Madhya Pradesh are the major origins of migrants heading to Delhi.
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Migration serves as a critical livelihood strategy for rural households in developing nations, including India. Internal migration, defined as the movement of people within the country's borders, significantly impacts both urban and rural economies.
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Internal migration can be categorized as intra-district (within the same district), intra-state (within the same state), or inter-state (across states). Migration streams can include rural-to-rural, rural-to-urban, urban-to-rural, and urban-to-urban movements.
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Accurately estimating migration volumes is challenging due to the informal nature of India's economy. The Census provides data to understand permanent and semi-permanent migration patterns. It identifies two types of migrants: those defined by their place of birth (enumerated in a location different from where they were born) and those defined by their last residence (living in a location other than their place of enumeration for at least six months before the Census)
1.Consider the following statements regarding migrant labor in India:
- The Census of India categorizes migrants based on their place of birth and last residence.
- Rural-to-urban migration is the most prominent type of migration in India.
- Improved economic opportunities in smaller cities have contributed to a decline in overall migration in recent years.
Which of the above statements is/are correct?
a) 1 and 2 only
b) 1 and 3 only
c) 2 and 3 only
d) 1, 2, and 3
|
Answer (b)
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As tech firms look to translate the promise of AI in 2025, 3 clear templates emerge
For Preliminary Examination: Current events of national and international importance
For Mains Examination: GS III - Science & Technology
Context:
If the year 2024 started with the promise of generational AI, 2025 could be more about testing out the use cases of some of these AI tools. And finding answers to the quintessential question of monetising AI. Going forward, there could be three board templates for the most compelling use cases yet
Read about:
What is artificial intelligence (AI)?
What are the areas of AI application?
Key takeaways:
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First, there is a concentrated emphasis on AI agents — artificial intelligence systems capable of managing multi-step tasks such as client onboarding, expense approvals, and not only directing but also independently addressing customer service inquiries, all with minimal human involvement.
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Second, regarding the challenge of monetizing AI, San Francisco-based Perplexity AI offers potential solutions. In November, Perplexity, a conversational "answer engine" powered by large language models (LLMs) that generate responses from web-sourced information with embedded citations, introduced a user-centric shopping hub to strengthen its platform and compete with Google's dominance in the search engine industry.
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Third, key players in the AI sector, including OpenAI, Google, Meta, xAI, and Anthropic, are intensifying efforts to enhance AI interfaces, with these companies leading advancements in the current generation of LLM technology.
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Google has also positioned itself in this space by announcing the launch of Gemini 2.0, which it claims to be its most advanced model designed for the "new agentic era." With innovations in multimodality — such as native support for image and audio outputs, as well as tool integration — Google envisions the development of AI agents that bring humanity closer to a universal assistant concept.
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This progress comes at a time when leading companies acknowledge that AI advancements are becoming increasingly challenging, as the initial easier breakthroughs have been achieved, and the path ahead is more demanding, particularly by 2025. According to Google CEO Sundar Pichai, foundational models are expected to improve their reasoning capabilities and ability to assess sequences of actions more reliably, making them more autonomous.
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Deep learning (DL), a subset of machine learning (ML), employs multi-layered neural networks to extract features automatically from extensive datasets. While ML is effective with smaller datasets, DL depends on vast quantities of data and significant computational resources
- Artificial Intelligence (AI) has become a transformative force, revolutionizing a variety of fields by automating complex tasks, improving efficiency, and unlocking new possibilities. Here’s an in-depth look at the key areas of AI application:
- In healthcare, AI is reshaping how medical services are delivered. It aids in diagnosing diseases with high accuracy, personalizing treatment plans based on patient data, and analyzing medical images like X-rays and MRIs. Furthermore, AI-powered tools are accelerating drug discovery and enabling virtual health assistants to provide immediate support to patients. Remote patient monitoring systems are also leveraging AI to track health conditions in real-time, improving access to care for those in remote areas.
- The education sector is another area experiencing significant impact. AI is enabling personalized learning experiences by adapting content and teaching methods to individual student needs. Automated grading systems reduce the workload for educators, while virtual tutors provide on-demand assistance to students. AI also plays a critical role in predictive analytics, helping educators identify students at risk of falling behind. Moreover, accessibility tools are enhancing learning opportunities for students with disabilities.
- In finance and banking, AI is critical for ensuring security and efficiency. It detects and prevents fraud by analyzing transactional patterns, assesses credit risk more accurately, and facilitates algorithmic trading for faster decision-making. Robo-advisors provide personalized financial advice, while AI chatbots enhance customer support by responding to queries instantly.
- The retail and e-commerce industry relies heavily on AI for personalization and efficiency. Recommendation systems suggest products based on customer preferences, while AI tools optimize inventory management by forecasting demand. Dynamic pricing strategies ensure competitive pricing, and visual search capabilities allow users to find products more intuitively. AI-driven customer service further enhances the shopping experience by addressing inquiries in real-time.
- Transportation and logistics benefit significantly from AI advancements. Autonomous vehicles, such as self-driving cars, are revolutionizing mobility, while AI optimizes traffic management and route planning. Fleet management becomes more streamlined with predictive maintenance, ensuring minimal downtime for vehicles. In logistics, AI enhances supply chain efficiency by predicting demand and optimizing resources.
- In manufacturing, AI improves quality control by detecting defects in products and machinery. Predictive maintenance helps in avoiding costly breakdowns, and supply chains become more efficient with AI-driven optimization tools. Robotics and automation streamline processes, while AI is also used for designing and prototyping products with greater precision.
- AI’s role in entertainment and media is growing rapidly. Streaming platforms like Netflix and Spotify use AI to recommend content tailored to user preferences. AI-generated content, such as videos, music, and articles, is becoming commonplace. It also automates video and image editing processes and enhances gaming experiences by creating adaptive and immersive environments.
- Agriculture is leveraging AI to improve efficiency and sustainability. Precision farming techniques, supported by AI, analyze soil health and monitor crop conditions to optimize yields. Automated systems for irrigation and pest control reduce resource wastage, while AI tools predict weather patterns and assist in planning. Robotics further streamline tasks like planting, harvesting, and sorting crops.
- Public safety and security are bolstered by AI’s ability to monitor and analyze data. Surveillance systems use AI for threat detection, while emergency response is improved through predictive systems. AI also plays a vital role in cybersecurity, identifying and mitigating potential threats. In disaster management, AI tools analyze data to predict and respond to emergencies effectively.
- In addressing environmental challenges, AI helps monitor climate change and supports wildlife conservation efforts through tracking and pattern analysis. Smart grids use AI to optimize energy consumption, while waste management systems leverage AI for sorting and recycling processes.
- Governments and public services also benefit from AI in managing urban environments. Smart city initiatives use AI to optimize traffic flow, manage utilities, and improve waste management. Policy-making is enhanced through data-driven analysis, and AI-powered chatbots provide citizens with efficient access to services. Public health monitoring systems also use AI to track and respond to epidemics.
- In the human resources domain, AI automates candidate screening, making recruitment processes more efficient. Workforce analytics help in planning and improving employee engagement, while personalized training programs support professional development.
- AI plays a crucial role in space exploration as well. Autonomous spacecraft and rovers utilize AI for navigation and data collection. Satellites rely on AI to interpret vast amounts of imagery, while anomaly detection systems ensure mission success.
- Lastly, deep learning (DL), a subset of machine learning (ML), underpins many of these applications. By using neural networks with multiple layers, DL can analyze large datasets to identify patterns and features. While ML works well with smaller datasets, DL requires significant computational resources and vast amounts of data to deliver its transformative capabilities.
- These applications illustrate how AI is revolutionizing industries, driving innovation, and addressing challenges across various domains.
1.Consider the following statements regarding Artificial Intelligence (AI):
- AI is solely dependent on large datasets for its functioning.
- Machine Learning (ML) is a subset of AI that enables systems to learn and improve from experience without explicit programming.
- Deep Learning (DL) is a type of ML that uses multi-layered neural networks to analyze vast amounts of data.
Which of the above statements is/are correct?
a) 1 and 2 only
b) 2 and 3 only
c) 1 and 3 only
d) 1, 2, and 3
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Answer (b)
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| Subject | Topic | Description |
| History | Modern Indian History | Important Personalities |
| History | Modern Indian History | Independence and Partition |
| History | Modern Indian History | Constitutional Development in India |
| History | Modern Indian History | Peasants, Tribal and other movements |
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