Why is it in the News?

In a study published recently, researchers analyzed more than 500 future emissions scenarios the UN Intergovernmental Panel on Climate Change (IPCC) assessed in its latest reports.

About the Intergovernmental Panel on Climate Change (IPCC):

• The IPCC was created in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP).
• It is the leading international body for the assessment of climate change.
• It is a key source of scientific information and technical guidance to the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement.
• The IPCC provides governments with scientific information for use in developing climate policies.
• The IPCC currently has 195 members.
• The IPCC does not undertake new research. Instead, it synthesizes published and peer-reviewed literature to develop a comprehensive assessment of scientific understanding.
  • These assessments are published in IPCC reports.
  • They are subject to multiple drafting and review processes to promote an objective, comprehensive, and transparent assessment of current knowledge.
• The IPCC’s work is guided by principles and procedures that govern all main activities of the organization.
• IPCC member governments and observer organizations nominate experts and the IPCC's scientific governing body, the IPCC Bureau, selects authors and editors with expertise in a range of scientific, technical, and socio-economic fields.

What are IPCC Assessment Reports?

• Typically, IPCC reports comprise three Working Group reports:
  • One on physical science
  • One on climate adaptation, and
  • One on mitigation action.
• One synthesis report consolidates findings from the three Working Group reports.
• Then there are thematic special reports.
• Each report assesses climate-related scientific literature to capture the state of scientific, technical, and socio-economic knowledge on climate change.
• The IPCC is currently in its Seventh Assessment cycle (AR7).

How Does it Assess Future Scenarios?

• The IPCC uses ‘modelled pathways’ to estimate what it will take to limit the warming of the earth’s surface.
• These pathways are drawn using Integrated Assessment Models (IAMs) that describe human and earth systems.
• IAMs are complex models that examine possible futures of the energy and climate systems and economies.
• Its macroeconomic models can point to future growth levels in terms of GDP;
  • Its energy models can project future consumption
  • Vegetation models can examine land-use changes; and
  • Earth-system models use the laws of physics to understand how climate evolves.
• With such integration across disciplines, IAMs are meant to provide policy-relevant guidelines on climate action.
• However, these models also have shortcomings. They prioritize least-cost assessments — for example, the absolute cost of setting up a solar plant or undertaking afforestation in India is lower than in the U.S.
• However, experts have said they could exercise the option of enabling countries to equitably share the burden of action, where the richest undertake more drastic mitigation action more immediately.

About the Latest Study:

• Conducted by a team of specialists from Bengaluru and Chennai, the study scrutinized 556 scenarios outlined in the IPCC's AR6 report.
• Their findings indicate that by 2050, per-capita GDP in Sub-Saharan Africa, South Asia, West Asia, and other parts of Asia will remain below the global average.
• Collectively, these regions account for 60% of the global population.
• Additionally, the study highlighted disparities in the consumption of goods and services, as well as energy and fossil fuel consumption, between the Global North and the Global South.

Why Does Equity Matter?

• Equity is crucial in climate action as per the UNFCCC, which mandates developed nations to lead in combating climate change.
• However, current modeling approaches often overlook equity, burdening poorer nations disproportionately.
• Researchers highlight the need for modeling techniques that prioritize climate justice and equitable distribution of responsibilities.
• They argue that mitigation pathways should ensure developed regions accelerate towards net negative emissions and allocate carbon budgets to less developed regions.
• Addressing this gap requires a paradigm shift in scenario building, emphasizing both equity and environmental sustainability.
• This approach is vital for fostering global cooperation and achieving meaningful climate action.

Why is it in the News?

Children of the Lisu and Singpho communities in Arunachal Pradesh and Assam are named according to the order they are born in the family, incorporating numbers into their names.

News Summary:

• In the Lisu or Yobin community of Arunachal Pradesh, names reflect the birth order of children, a tradition emphasizing familial hierarchy and cultural heritage.
• This practice underscores the community's deep-rooted connection to family and tradition.
• Recently, Birdwatchers discovered a new species of wren babblers in remote northeastern Arunachal Pradesh, aptly named the Lisu wren babbler.

Lisu and Singpho communities:

• The Lisu and Singpho communities, belonging to the Tibeto-Burman ethnic family, share a unique tradition of employing numbered names to denote birth order within their families, serving as a testament to their ethnic cohesion and rich cultural legacy.
• This naming tradition is prevalent among the Lisus, spanning regions such as Arunachal Pradesh, China, Myanmar, and Thailand, as well as the Singphos, who are prominent in Arunachal Pradesh and Assam in India.
  • The Singphos, an ethnic community believed to have originated from the Kachin peoples, migrated from regions including upper Myanmar, Southwestern China, and Northern Thailand to settle in the eastern areas of Arunachal Pradesh.
• Both communities adhere to specific naming sequences for boys and girls, supplemented by strategies to prevent confusion in cases of similar name counts within families, such as the use of prefixes or suffixes.
• Furthermore, names may incorporate clan or ancestral references, adding layers of cultural and familial significance to the naming tradition, which underscores the profound connection to tradition and the enduring importance of family and clan identities within these communities.

About Wren Babblers:

• Belonging to the babbler family Timaliidae, Wren Babblers encompass approximately 20 small Asian bird species.
• Characteristics: These birds typically measure between 10 to 15 centimeters (4 to 6 inches) in length, featuring short tails and straight bills.
• Natural Habitat: Primarily found in southern Asia, Wren Babblers inhabit various ecological niches.
• Grey-bellied Wren Babblers: A closely resembling species to this newly discovered one, predominantly inhabit regions of Myanmar, with smaller populations also found in China and Thailand.

Why is it in the News?

Revenue-based financing (RBF) is increasingly popular among startups and digital SMEs due to a lack of venture capital and limited access to traditional credit options.

What Is Revenue-Based Financing?

• Revenue-based financing is a method of raising capital for a business from investors who receive a percentage of the enterprise's ongoing gross revenues in exchange for the money they invested.
• In a revenue-based financing investment, investors receive a regular share of the business's income until a predetermined amount has been paid.
• Typically, this predetermined amount is a multiple of the principal investment and usually ranges between three to five times the original amount invested.

How Revenue-Based Financing Works?

• Capital investment: An investor or a group of investors provides capital to a company (but not as a traditional loan nor in exchange for equity in the company).
• Revenue percentage agreement: In return for the capital, the company agrees to give the investor a fixed percentage of its gross revenues each month.
• Repayment structure: The company repays the invested capital through payments based on monthly or annual revenue.
  • The amount paid each month varies as it is directly tied to the company’s revenue for that month.
• Repayment cap or term: There is usually a cap on the total amount to be repaid, often set as a multiple of the original investment (e.g., 1.5x or 2x the initial amount).
  • Alternatively, the repayment might continue until a specific term is reached, such as a number of years.

Comparing Revenue-based Financing to Debt and Equity-based Models:

• While revenue-based financing shares similarities with debt financing in terms of regular investor repayments, it differs notably as it doesn't involve interest payments.
• Instead, repayments are based on a predetermined multiple, yielding returns higher than the initial investment.
• Moreover, unlike traditional debt arrangements, revenue-based financing doesn't necessitate collateral.
• Additionally, unlike equity-based models, it doesn't entail transferring ownership stakes in the company to investors.

Why is it in the News?

Characterized by its marble pillars and intricate latticework, Chausath Khamba (64 pillars) stands adjacent to the Nizamuddin dargah, a 14th-century shrine erected in honor of the revered Sufi saint Hazrat Nizamuddin Auliya.

About the Chausath Khamba:

• Chausath Khamba was built in AD 1623 - 24 to serve as a tomb for Mirza Aziz Koka, the foster brother of Mughal Emperor Akbar.
  • It is so called on account of the 64 (chausath) monolithic marble pillars (khamba) and stands close to his father, Atgah Khan’s tomb, at the edge of the Dargah of Hazrat Nizamuddin Auliya.
• The tomb enclosure is entered through a lofty arched gateway and has a large sunken forecourt.
  • The mausoleum is unique on account of it being built entirely of marble, with 25 marble domes supporting the flat roof of the structure.
  • The plan for Chausath Khamba could have been inspired by the wooden garden pavilions from Persia - such as the Chihil Sutun, and in turn, the Chausath Khamba seems to have inspired the architectural design for Emperor Shahjahan’s Diwan-i-Aam, Hall of Audience.
• Each facade of the square structure has five marble arches inset with marble jaallis or lattice screens and a doorway in the central arch providing access to the tomb.
  • The column capitals are intricately carved with simple yet striking pendentives bridging the square floor plan to the circular dome above.
• The structure also finds mention in Sir Gordon Risley Hearn’s book The Seven Cities of Delhi.
• As per author and historian Sam Dalrymple, the edifice embodies the architectural style of Gujarat and Ahmedabad within Delhi, serving as the Urs Mahal for hosting festivities during the commemoration of Nizamuddin's passing.
  • This illustrates the historical dissemination of regional architectural influences across India over centuries.

Why is it in the News?

Predictive AI is revolutionizing data analysis, decision-making, and industry leadership, offering businesses unprecedented insights and strategic advantages.

What is Predictive Artificial Intelligence (AI)?

• Predictive artificial intelligence (AI) utilizes machine learning techniques to analyze historical data and forecast future events, distinguishing it from traditional AI focused solely on retrospective analysis.
• This cutting-edge technology employs advanced algorithms and machine learning models to sift through extensive datasets, identifying subtle patterns and trends.
• Unlike conventional approaches, Predictive AI doesn't just analyze data; it transforms it into actionable insights, enabling organizations to:
  • Anticipate future outcomes,
  • Predict market shifts, and
  • Make strategic decisions with unprecedented foresight.
• By continuously learning from past data and adapting to changing trends, Predictive AI becomes an invaluable tool, guiding businesses through uncertain landscapes.

How Predictive AI Work?

• Leveraging Big Data: Predictive AI relies on access to extensive datasets, often referred to as "big data," as larger datasets typically lead to more accurate analyses.
• Utilizing Machine Learning (ML): As a subset of AI, ML involves training computer programs to analyze data autonomously, without human intervention.
  • In the realm of predictive AI, ML algorithms are applied to vast datasets to extract valuable insights.
• Autonomous Processing: Predictive AI models are capable of autonomously processing massive datasets, eliminating the need for human oversight.
• Pattern Recognition: Through ML techniques, predictive AI learns to recognize patterns within datasets, associating specific data points or occurrences with potential future events.
  • By examining numerous factors, predictive AI can identify intricate patterns indicative of recurring events, enabling organizations to anticipate future outcomes effectively.

Difference Between Predictive AI and Generative AI:

• Predictive AI and generative AI both employ machine learning techniques and leverage extensive datasets to generate their outputs.
• However, while predictive AI utilizes machine learning to forecast future outcomes, generative AI employs machine learning to produce original content.
• For instance, a predictive AI model may inform fishermen about impending storms, whereas a generative AI model may craft a fictional narrative depicting various scenarios involving weather and fishing expeditions.
• While both types of AI rely on statistical analysis to discern patterns, their objectives, machine learning methodologies, and applications differ significantly.

Various Applications of Predictive AI:

• Assessing the Impact of Natural Disasters: With the recent eruption of a volcano in Iceland, the potential repercussions on air travel echo concerns from a similar event in 2010, which disrupted flights across Europe.
  • Predictive AI leverages data analysis to identify patterns and anticipate the impact of such extreme weather events on air travel. Platforms like Yandex offer interactive maps for real-time monitoring of ash clouds post-eruption.
• Enhancing Oil and Gas Exploration: In the realm of oil and gas exploration, companies possess extensive historical geological data that can inform predictive AI systems.
  • By analyzing past drilling successes, these systems can predict optimal locations for new oil wells.
    • For instance, Saudi Aramco utilizes its meta-brain generative AI to optimize drilling plans, analyze geological data, and forecast drilling outcomes accurately.
• Inventory and Supply chain management: Predictive AI aids in inventory and supply chain management by identifying peak consumer demand periods, facilitating proactive stock adjustments, and optimizing resource allocation to address fluctuations in road congestion and meet increased user demands.
• Marketing campaigns: Just as predictive AI can anticipate user or customer behavior, it can help prognosticate what kinds of content or products prospective customers may be interested in.
• Advancing Medical Research: Predictive AI plays a pivotal role in drug discovery, a cornerstone of contemporary medical research.
  • Pharmaceutical companies are increasingly collaborating to leverage predictive AI models for analyzing vast datasets and identifying potential drug candidates. Initiatives like the 'MELLODDY Project', supported by the EU Innovative
  • Medicines Initiative and multiple pharmaceutical firms, exemplify this collaborative effort in pooling data and leveraging predictive AI for drug discovery.