Highlights of the Interim Union Budget 2024-25 (GS Paper 3, Economy)
Why in news?
With the ‘mantra’ of ‘Sabka Saath, Sabka Vikas, and Sabka Vishwas’ and the whole of nation approach of “Sabka Prayas”, the Union Minister for Finance and Corporate Affairs presented the Interim Union Budget 2024-25 in Parliament, today. The key highlights of the Budget are as follows
Part A
Social Justice
- Prime Minister to focus on upliftment of four major castes, that is, ‘Garib’ (Poor), ‘Mahilayen’ (Women), ‘Yuva’ (Youth) and ‘Annadata’(Farmer).
‘Garib Kalyan, Desh ka Kalyan’
- Government assisted 25 crore people out of multi-dimensional poverty in last 10 years.
- DBT of Rs. 34 lakh crore using PM-Jan Dhan accounts led to savings of Rs. 2.7 lakh crore for the Government.
- PM-SVANidhi provided credit assistance to 78 lakh street vendors. 2.3 lakh have received credit for the third time.
- PM-JANMAN Yojana to aid the development of particularly vulnerable tribal groups (PVTG).
- PM-Vishwakarma Yojana provides end-to-end support to artisans and crafts people engaged in 18 trades.
Welfare of ‘Annadata’
- PM-KISAN SAMMAN Yojana provided financial assistance to 11.8 crore farmers.
- Under PM Fasal BimaYojana, crop insurance is given to 4 crore farmers
- Electronic National Agriculture Market (e-NAM) integrated 1361 mandis, providing services to 1.8 crore farmers with trading volume of Rs. 3 lakh crore.
Momentum for Nari Shakti
- 30 crore Mudra Yojana loans given to women entrepreneurs.
- Female enrolment in higher education gone up by 28%.
- In STEM courses, girls and women constitute 43% of enrolment, one of the highest in the world.
- Over 70% houses under PM Awas Yojana given to women from rural areas.
PM Awas Yojana (Grameen)
- Despite COVID challenges, the target of three crore houses under PM Awas Yojana (Grameen) will be achieved soon.
- Two crore more houses to be taken up in the next five years.
Rooftop solarization and muft bijli
- 1 crore households to obtain 300 units free electricity every month through rooftop solarization.
- Each household is expected to save Rs.15000 to Rs.18000 annually.
Ayushman Bharat
- Healthcare cover under Ayushman Bharat scheme to be extended to all ASHA workers, Anganwadi Workers and Helpers.
Agriculture and food processing
- Pradhan Mantri Kisan Sampada Yojana has benefitted 38 lakh farmers and generated 10 lakh employment.
- Pradhan Mantri Formalisation of Micro Food Processing Enterprises Yojana has assisted 2.4 lakh SHGs and 60000 individuals with credit linkages.
Research and Innovation for catalyzing growth, employment and development
- A corpus of Rs.1 lakh crore to be established with fifty-year interest free loan to provide long-term financing or refinancing with long tenors and low or nil interest rates.
- A new scheme to be launched for strengthening deep-tech technologies for defence purposes and expediting ‘atmanirbharta’.
Infrastructure
- Capital expenditure outlay for Infrastructure development and employment generation to be increased by 11.1 per cent to Rs.11,11,111 crore, that will be 3.4 per cent of the GDP.
Railways
- 3 major economic railway corridor programmes identified under the PM Gati Shakti to be implemented to improve logistics efficiency and reduce cost
- Energy, mineral and cement corridors
- Port connectivity corridors
- High traffic density corridors
- Forty thousand normal rail bogies to be converted to Vande Bharat standards.
Aviation Sector
- Number of airports in the country doubled to 149.
- Five hundred and seventeen new routes are carrying 1.3 crore passengers.
- Indian carriers have placed orders for over 1000 new aircrafts.
Green Energy
- Coal gasification and liquefaction capacity of 100 MT to be set up by 2030.
- Phased mandatory blending of compressed biogas (CBG) in compressed natural gas (CNG) for transport and piped natural gas (PNG) for domestic purposes to be mandated.
Tourism sector
- States to be encouraged to take up comprehensive development of iconic tourist centres including their branding and marketing at global scale.
- Framework for rating of the tourist centres based on quality of facilities and services to be established.
- Long-term interest free loans to be provided to States for financing such development on matching basis.
Investments
- FDI inflow during 2014-23 of USD 596 billion was twice of the inflow during 2005-14.
Reforms in the States for ‘Viksit Bharat’
- A provision of Rs.75,000 crore rupees as fifty-year interest free loan is proposed to support milestone-linked reforms by the State Governments.
Revised Estimates (RE) 2023-24
- RE of the total receipts other than borrowings is Rs.27.56 lakh crore, of which the tax receipts are Rs.23.24 lakh crore.
- RE of the total expenditure is Rs.44.90 lakh crore.
- Revenue receipts at Rs.30.03 lakh crore are expected to be higher than the Budget Estimate, reflecting strong growth momentum and formalization in the economy.
- RE of the fiscal deficit is 5.8 per cent of GDP for 2023-24.
Budget Estimates 2024-25
- Total receipts other than borrowings and the total expenditure are estimated at Rs.30.80 and Rs.47.66 lakh crore respectively.
- Tax receipts are estimated at Rs.26.02 lakh crore.
- Scheme of fifty-year interest free loan for capital expenditure to states to be continued this year with total outlay of Rs.1.3 lakh crore.
- Fiscal deficit in 2024-25 is estimated to be 5.1 per cent of GDP
- Gross and net market borrowings through dated securities during 2024-25 are estimated at Rs.14.13 and Rs.11.75 lakh crore respectively.
Part B
Direct taxes
- FM proposes to retain same tax rates for direct taxes
- Direct tax collection tripled, return filers increased to 2.4 times, in the last 10 years
- Government to improve tax payer services
- Outstanding direct tax demands upto Rs 25000 pertaining to the period upto FY 2009-10 withdrawn
- Outstanding direct tax demands upto Rs 10000 for financial years 2010-11 to 2014-15 withdrawn
- This will benefit one crore tax payers
- Tax benefits to Start-Ups, investments made by Sovereign wealth funds or pension funds extended to 31.03.2025
- Tax exemption on certain income of IFSC units extended by a year to 31.03.2025 from 31.03.2024
Indirect taxes
- FM proposes to retain same tax rates for indirect taxes and import duties
- GST unified the highly fragmented indirect tax regime in India
- Average monthly gross GST collection doubled to Rs 1.66 lakh crore this year
- GST tax base has doubled
- State SGST revenue buoyancy (including compensation released to states) increased to 1.22 in post-GST period(2017-18 to 2022-23) from 0.72 in the pre-GST period (2012-13 to 2015-16)
- 94% of industry leaders view transition to GST as largely positive
- GST led to supply chain optimization
- GST reduced the compliance burden on trade and industry
- Lower logistics cost and taxes helped reduce prices of goods and services, benefiting the consumers
Tax rationalization efforts over the years
- No tax liability for income upto Rs 7 lakh, up from Rs 2.2 lakh in FY 2013-14
- Presumptive taxation threshold for retail businesses increased to Rs 3 crore from Rs 2 crore
- Presumptive taxation threshold for professionals increased to Rs 75 lakh from Rs 50 lakh
- Corporate income tax decreased to 22% from 30% for existing domestic companies
- Corporate income tax rate at 15% for new manufacturing companies
Achievements in tax-payer services
- Average processing time of tax returns has reduced to 10 days from 93 days in 2013-14
- Faceless Assessment and Appeal introduced for greater efficiency
- Updated income tax returns, new form 26AS and prefilled tax returns for simplified return filing
- Reforms in customs leading to reduced Import release time
- Reduction by 47% to 71 hours at Inland Container Depots
- Reduction by 28% to 44 hours at Air Cargo complexes
- Reduction by 27% to 85 hours at Sea Ports
Scientists fuse brain-like tissue with electronics to make computer
(GS Paper 3, Science and Technology)
Why in news?
- Scientists have fused brain-like tissue with electronics to make an ‘organoid neural network’ that can recognise voices and solve a complex mathematical problem.
- Their invention extends neuromorphic computing, the practice of modelling computers after the human brain to a new level by directly including brain tissue in a computer.
Details:
- The system was developed by a team of researchers from Indiana University, Bloomington; the University of Cincinnati and Cincinnati Children’s Hospital Medical Centre, Cincinnati; and the University of Florida, Gainesville.
- The study marks a significant advance in multiple areas of science and engineering. It opens possibilities at the intersection of tissue engineering, electrophysiology, and neural computationd.
- The work comes against the backdrop of the staggering rise of artificial intelligence (AI), itself founded on the development of artificial neural networks that can process large datasets that conventional computers struggle with.
Challenges:
- The hardware on which these neural networks run has a problem, however: the memory units and the data processing units are separate. When a neural network operates, the network will have to access the data in the memory unit, bring it over to the processing unit, and work on it and it needs to do this many times over.
- If the problem is more complex, the time and energy demands increase further because the system will have to go back and forth between these units even more.
- Scientists have tried to build more efficient neuromorphic chips that include some short-term memory, so they can avoid going back and forth just a bit. These chips have been used for applications like computer vision and speech recognition.
Organoid neural network:
- This new area of research, called biocomputing, uses biological components to perform computational processes.
- In 2023, a group of researchers from Australia cultured brain cells and trained them to play pong (a table-tennis-like videogame), in the process demonstrating the initial steps of long-term training.
- In the new study, the U.S. researchers used actual brain organoids to make an ‘organoid neural network’ and tested it to recognise speech and solve a complex mathematical problem.
- Brain organoids are three-dimensional aggregates of brain cells. The scientists made them by extracting human pluripotent stem cells, which are cells that can develop to become almost any kind of cell within the human body, and made them into brain cells.
- Brain organoids that are aggregates of such cells have a mix of the different types of cells in the brain. In this case, they were neuron progenitor cells, early-stage neurons, mature neurons, and astrocytes (cells that maintain and protect neurons).
A three-layered computer:
- The team connected the brain organoid to an array of microelectrodes to form an organoid neural network, a type of artificial neural network containing a live organoid. The team then built this network into a system called a reservoir computer.
- This machine contains three ‘layers’: input, reservoir, and output.
- The input signals are routed to a reservoir, which is a black box whose purpose is to convert the signals into mathematical entities that the computer can ‘work on’ to find solutions. The output is a simple readout from the reservoir.
- In this system, which the team calls ‘Brainoware’, the reservoir was the organoid neural network. It received inputs from the input layer in the form of electrical stimulation.
- The output layer was ‘normal’ computer hardware that had been modified to recognise Brainoware’s neural activity.
Performance:
- The researchers demonstrated Brainoware’s abilities by predicting a Henon map, a mathematical function that draws a curve on a graph that can be chaotic or not depending on the values of two variables.
- Brainoware could also tell which Japanese vowel an individual was voicing after ‘learning’ from 240 audio clips from eight speakers, over just two days. Its accuracy in the latter task was 78%, and without any external feedback on whether its inferences while learning were right or wrong.
- Brainoware was more accurate than artificial neural networks that lacked a short-term memory unit but slightly less accurate than those with one. But it achieved this comparable accuracy with less than a tenth of the training the artificial neural networks required.
Way Forward:
- Their system still has some limitations that the researchers have also acknowledged, but it’s a start.
- Running Brainoware requires technical expertise and infrastructure to maintain a biological neural network. Organoids form a relatively heterogeneous mix of cell types, so not all organoids function the same way. But efforts are underway to achieve more uniform cell mixes.
