In News:

India and Germany have launched a €20 million (approximately ?180 crore) Large Grant project under Germany’s International Climate Initiative (IKI). The project focuses on strengthening climate resilience in India’s most vulnerable ecosystems through nature-based and sustainable adaptation strategies.

About the International Climate Initiative (IKI)

• Established in 2008, IKI is Germany’s principal funding instrument for international climate action.
• Supports projects in:
  • Climate change mitigation
  • Adaptation
  • Biodiversity conservation
• Operates in over 150 partner countries, with 14 priority countries, including India, Brazil, China, South Africa, Indonesia, and Mexico.
• Aligns with global commitments under the Paris Agreement and the Convention on Biological Diversity (CBD).

IKI represents Germany’s climate diplomacy approach, combining financial assistance, technology cooperation, and capacity building.

Scope of the New India–Germany Project

The newly launched €20 million initiative targets high-risk and ecologically sensitive regions in India, promoting long-term resilience through ecosystem-based adaptation.

Priority Regions

• Himalayas
  • Challenges: Glacier melt, glacial lake outburst floods (GLOFs), landslides.
  • Significance: Water security for major river systems.
• Western Ghats
  • Biodiversity hotspot facing deforestation and habitat fragmentation.
  • Vulnerable to extreme rainfall events and ecological degradation.
• North-East India
  • Fragile hill ecosystems prone to soil erosion and flooding.
  • Rich in biodiversity but ecologically sensitive.
• Island Ecosystems (e.g., Andaman & Nicobar)
  • Threatened by sea-level rise and coastal erosion.
  • High vulnerability to cyclones and marine ecosystem disruption.

Focus Areas of Intervention

• Promotion of Nature-Based Solutions (NbS)
• Ecosystem restoration and conservation
• Climate-resilient livelihoods
• Capacity building at local and state levels
• Strengthening institutional frameworks for adaptation

Nature-based solutions integrate environmental restoration with socio-economic resilience, ensuring sustainability and community participation.

Strategic Significance

1. Strengthening India’s Climate Resilience

India faces:

• Rising temperatures
• Erratic monsoons
• Increased frequency of extreme weather events
• Biodiversity loss

This initiative enhances adaptive capacity in vulnerable geographies.

2. Alignment with India’s National Commitments

The project supports:

• India’s Nationally Determined Contributions (NDCs)
  • Target of 50% cumulative electric power installed capacity from non-fossil fuel sources by 2030.
• Net-Zero Commitment (2070)
• National Action Plan on Climate Change (NAPCC) missions, particularly:
  • National Mission for Sustaining the Himalayan Ecosystem
  • National Mission on Sustainable Habitat

3. Global Climate Governance

• Reinforces North–South cooperation in climate finance.
• Demonstrates operationalisation of climate finance commitments under the Paris Agreement.
• Promotes biodiversity conservation alongside climate mitigation and adaptation.

4. Indo-German Strategic Partnership

Climate cooperation is a key pillar of the India–Germany Strategic Partnership, complementing collaboration in:

• Renewable energy
• Green hydrogen
• Sustainable urbanisation
• Technology and innovation

In News:

On the sidelines of the India AI Impact Summit 2026, the IndiaAI Mission and Business Sweden signed a Statement of Intent (SoI) to deepen bilateral cooperation in Artificial Intelligence (AI) and digital technologies. The agreement marks a significant step in institutionalising India–Sweden collaboration in emerging technologies and innovation-driven growth.

Nature and Objectives of the Statement of Intent (SoI)

The SoI establishes a structured framework for collaboration in:

• Development, application, and deployment of AI solutions
• Promotion of trade and investment linkages
• Advancement of responsible and scalable digital innovation

The partnership emphasises real-world industrial and societal outcomes, reflecting a shared commitment to using AI for economic growth, sustainability, and technological transformation while managing associated risks.

Sweden–India Technology and Artificial Intelligence Corridor (SITAC)

Both countries will jointly develop a dedicated programme titled the Sweden–India Technology and Artificial Intelligence Corridor (SITAC).

Key Features:

• Flagship institutional platform for AI cooperation
• Structured engagement between:
  • Government agencies
  • Industry stakeholders
  • Startups
  • Academic and research institutions

SITAC aims to serve as a long-term innovation bridge linking the AI ecosystems of both countries.

Areas of Cooperation under SITAC

The framework proposes:

1. Conferences, seminars, and thematic workshops
2. Ecosystem exchanges between Indian and Swedish AI communities
3. Field visits to innovation hubs and Centres of Excellence
4. Engagement among companies, investors, researchers, and policymakers
5. Joint innovation platforms and investment corridors
6. Deployment of AI solutions across priority sectors

This multi-level engagement seeks to translate policy vision into industry-level collaboration.

Strategic Alignment of National Priorities

India’s Objectives (IndiaAI Mission)

• Build a comprehensive AI ecosystem
• Expand access to compute infrastructure, data, and skilled talent
• Promote sovereign and inclusive AI development
• Encourage startup-led innovation

Sweden’s Strengths

• Industrial innovation and advanced R&D
• Strong manufacturing and clean-tech ecosystem
• Leadership in responsible and ethical AI implementation
• Experience in digital governance frameworks

The partnership integrates India’s scale and digital capacity with Sweden’s research depth and industrial expertise.

In News:

Recent discussions on India’s climate strategy have highlighted the growing importance of Carbon Capture and Utilisation (CCU) technologies, particularly for hard-to-abate sectors such as cement, steel, refineries, and chemicals. With India committing to net-zero emissions by 2070, CCU is emerging as a necessary complement to renewable energy expansion.

What is Carbon Capture and Utilisation (CCU)?

Carbon Capture and Utilisation (CCU) refers to a set of technologies that:

• Capture carbon dioxide (CO?) from industrial sources or directly from the atmosphere.
• Convert the captured CO? into useful products such as fuels, chemicals, building materials, or polymers.

Unlike Carbon Capture and Storage (CCS), where CO? is permanently stored underground, CCU reintegrates carbon into the economy, contributing to a circular carbon economy.

Why CCU is Necessary for India

1. High Emissions Profile

India is the world’s third-largest CO? emitter, with emissions primarily arising from:

• Power generation
• Cement production
• Steel manufacturing
• Chemicals and refineries

2. Hard-to-Abate Sectors

In industries like cement and steel:

• A significant portion of emissions comes from industrial processes themselves, not just fuel combustion.
• Renewable energy alone cannot fully eliminate these emissions.

3. Alignment with Net-Zero 2070

CCU supports:

• Deep industrial decarbonisation
• Circular economy goals
• Low-carbon industrial competitiveness

Thus, CCU acts as a bridge technology during the transition to a fully decarbonised economy.

Global Developments

• European Union: The EU Bioeconomy Strategy and Circular Economy Action Plan promote CCU for converting CO? into feedstocks for fuels and chemicals.
• Belgium: ArcelorMittal and Mitsubishi Heavy Industries are piloting technology to convert captured CO? into carbon monoxide for steel and chemical production.
• United States: Combines tax credits and public funding to scale CO?-derived fuels and chemicals.
• UAE: The Al Reyadah project integrates CCU with green hydrogen for CO?-to-chemicals hubs.

These initiatives indicate that CCU is becoming part of mainstream climate-industrial policy globally.

India’s Progress and Policy Push

1. Research and Roadmaps

• The Department of Science and Technology (DST) has prepared a dedicated R&D roadmap for CCU.
• The Ministry of Petroleum and Natural Gas has proposed a draft 2030 CCUS roadmap identifying potential projects.

2. Budgetary Support

• The Union Budget 2026–27 announced a ?20,000 crore scheme to scale up Carbon Capture, Utilisation and Storage (CCUS).
• Focus sectors: Power, Steel, Cement, Refineries, and Chemicals.
• Marks a shift from pilot projects to structured, policy-backed deployment.

3. Private Sector Initiatives

• Ambuja Cements (Adani Group) with IIT Bombay: Indo-Swedish CCU pilot converting CO? into fuels and materials.
• JK Cement: Developing CCU applications for lightweight concrete blocks and olefins.
• Organic Recycling Systems Limited (ORSL): Leading India’s first pilot-scale Bio-CCU platform, converting CO? from biogas into bio-alcohols and specialty chemicals.

In News:

At the India-AI Impact Summit 2026, Bengaluru-based startup Sarvam AI unveiled two indigenous Large Language Models (LLMs)** trained on 35 billion and 105 billion parameters. These models are designed to be less power- and compute-intensive, while demonstrating improved performance in Indian languages. The development marks a significant milestone in India’s quest for sovereign and cost-efficient AI systems aligned with domestic needs.

Understanding Large Language Models (LLMs)

A Large Language Model (LLM) is an AI system built using transformer-based neural networks trained on massive text datasets to understand and generate human language.

They contain billions of parameters—internal variables learned during training—that help the model predict the next word in a sequence and generate coherent text.

How LLMs Work

1. Tokenisation: Text is broken into smaller units called tokens (word pieces or characters).
2. Embeddings & Transformer Architecture: Tokens are converted into numerical vectors. The self-attention mechanism helps the model determine which words in a sentence are contextually important, even if they are far apart.
3. Next-Token Prediction: The model generates language by predicting one token at a time based on probability distributions.
4. Layered Learning: Multiple transformer layers refine linguistic and semantic understanding—from grammar to reasoning patterns.

Stages of Training LLMs

1. Data Collection & Pre-processing

• Massive datasets sourced from books, websites, code repositories, etc.
• Cleaning to remove bias, spam, duplicates, and harmful content.
• Quality of data directly influences performance.

2. Pre-training (Self-Supervised Learning)

• Model learns via next-token prediction.
• Produces a base model capable of understanding grammar, facts, and reasoning.

3. Supervised Fine-Tuning

• Trained on curated prompt–response pairs.
• Enhances instruction-following ability and task performance (summarization, translation, Q&A).

4. Alignment via RLHF

• Reinforcement Learning from Human Feedback (RLHF).
• Humans rank outputs based on safety and quality.
• A reward model optimises responses to align with human values.

Challenges in Training LLMs in India

• Data Scarcity in Indian Languages
  • English and East Asian languages dominate internet data.
  • Indian languages remain underrepresented.
  • Many models rely on translation into English, increasing token consumption and cost.
• High Capital and Compute Costs
  • Requires clusters of Graphics Processing Units (GPUs).
  • Training costs run into millions of dollars.
  • Limited domestic venture capital for foundational AI research.
• Limited Immediate Commercial Use Cases: Training large models without clear monetisation pathways deters investment.
• Infrastructure Constraints
  • Dependence on imported high-end chips.
  • Energy-intensive training processes.

Innovation: Mixture of Experts (MoE) Architecture

Earlier LLMs activated all parameters during inference, making them computationally expensive.

The Mixture of Experts (MoE) architecture activates only a subset of parameters (“experts”) for each query.

Advantages:

• Reduced computational load
• Faster inference
• Lower electricity consumption
• Cost-efficient deployment in resource-constrained settings

Sarvam’s 105B parameter model leverages MoE to balance performance with efficiency, focusing on accuracy and Indian context alignment rather than sheer scale.

IndiaAI Mission: Government Support for Domestic AI

Launched in March 2024 with an outlay of ?10,372 crore, the IndiaAI Mission aims to build a comprehensive AI ecosystem.

Key Components:

• Compute Infrastructure
  • Over 36,000 GPUs commissioned in Indian data centres.
  • Additional 20,000 GPUs being added.
  • Target: 100,000 GPUs by end of 2026.
• Subsidised Access
  • Sarvam AI granted 4,096 GPUs from a common compute cluster.
  • Subsidy estimated at nearly ?100 crore.
  • Cluster cost approximately ?246 crore.
• Support for Innovation
  • Promotion of sovereign foundational models trained on Indian datasets.
  • Financial support covering compute and training costs.
  • Encouragement of open-source innovation.
• Talent Development
  • Training support for over 13,500 students.
  • Establishment of India Data and AI Labs.

Other Indian Efforts

• BharatGen (IIT Bombay-incubated): Multilingual 17B parameter model, targeted at sectors like education and healthcare.
• Gnani.ai: Small text-to-speech model.
• Indigenous focus on domain-specific and language-specific AI models.

In News:

The Ministry of Micro, Small and Medium Enterprises (MoMSME) recently convened the 5th meeting of the National MSME Council in New Delhi to review the progress of the World Bank–assisted Raising and Accelerating MSME Performance (RAMP) Programme. The review gains significance in the context of MSMEs being the backbone of India’s economy and central to achieving inclusive growth, Atmanirbhar Bharat, and the $5 trillion economy vision.

About the RAMP Programme

• It was launched in 2022 and is being implemented over a five-year period (2022–23 to 2026–27) by the Ministry of MSME with World Bank support.
• It seeks to address structural challenges faced by MSMEs through systemic reforms and capacity building at both the Central and State levels.

Objectives of RAMP

The programme focuses on:

• Improving Access to Market and Credit
  • Enhancing financial inclusion.
  • Promoting integration into domestic and global value chains.
• Strengthening Institutions and Governance
  • Capacity building of MSME institutions at Central and State levels.
  • Improving policy design and implementation mechanisms.
• Enhancing Centre–State Coordination
  • Encouraging cooperative federalism through structured partnerships.
  • Providing financial assistance to States for preparing Strategic Investment Plans (SIPs).
• Addressing Delayed Payments: Tackling liquidity stress among Micro and Small Enterprises (MSEs).
• Greening of MSMEs: Supporting climate-resilient and sustainable business practices in alignment with India’s Net Zero target of 2070.

Institutional Framework

National MSME Council

• Established by MoMSME as the administrative and functional body under RAMP.
• Provides strategic direction, monitors progress, and facilitates coordination among stakeholders.

State-Level Role

• States receive grants to prepare Strategic Investment Plans (SIPs).
• SIPs align state-specific reforms with national MSME objectives.
• Promotes decentralised planning and context-specific solutions.

Key Sub-Schemes under RAMP

1. MSME GIFT Scheme

(MSME Green Investment and Financing for Transformation)

• Promotes adoption of green technologies.
• Provides interest subvention and credit guarantee support.
• Encourages energy efficiency, cleaner production, and sustainability.

2. MSE SPICE Scheme

(Scheme for Promotion and Investment in Circular Economy)

• Supports circular economy initiatives among MSEs.
• Offers credit-linked capital subsidy.
• Contributes toward the long-term objective of MSMEs achieving net-zero emissions by 2070.

3. MSE ODR Scheme

(Online Dispute Resolution for Delayed Payments)

• First-of-its-kind initiative integrating legal support with IT tools and Artificial Intelligence.
• Addresses the chronic issue of delayed payments to Micro and Small Enterprises.
• Strengthens ease of doing business and improves working capital cycles.

Significance for the Indian Economy

• MSMEs contribute significantly to GDP, exports, and employment generation.
• RAMP supports:
  • Formalisation and competitiveness.
  • Digital transformation.
  • Climate-aligned industrial growth.
  • Improved credit flow and risk mitigation.
• It operationalises cooperative and competitive federalism through structured Centre–State collaboration.