Hi Friends,

Even as I launch this today ( my 80th Birthday ), I realize that there is yet so much to say and do. There is just no time to look back, no time to wonder,"Will anyone read these pages?"

With regards,
Hemen Parekh
27 June 2013

Now as I approach my 90th birthday ( 27 June 2023 ) , I invite you to visit my Digital Avatar ( www.hemenparekh.ai ) – and continue chatting with me , even when I am no more here physically

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Friday, 13 February 2026

Goodbye Diesel, Hello Clean Tech

Goodbye Diesel, Hello Clean Tech

Why Niti Aayog’s proposal matters

I read the recent policy note from Niti Aayog with the mixture of hope and pragmatism I bring to most conversations about large-system change. The think tank has proposed a phased elimination of polluting diesel vehicles and an accelerated shift to cleaner options — CNG, hybrids, biofuels and, ultimately, battery and zero‑emission vehicles — as part of a long-term pathway to net‑zero transport emissions by 2070 (Times of India).

This is not simply environmental rhetoric. It is a recognition that road transport still depends largely on fossil fuels (petrol and diesel make up the majority of energy use) and that meeting climate and air‑quality goals will require deliberate policy sequencing.


What the proposal actually recommends

  • A gradual phase‑out of diesel vehicles beginning with the most polluting segments and moving toward stricter controls on new diesel sales.
  • Near‑term emphasis on lower‑emission interim technologies (CNG, hybrids) and scaling biofuels via flex‑fuel vehicles and Bio‑CNG blending.
  • Continued push for electrification (EVs) with supporting investments in charging infrastructure and cleaner power generation.
  • Stronger enforcement of Corporate Average Fuel Efficiency (CAFE) norms and demand management via better public transport and urban planning.

These are sensible building blocks: a staged approach that mixes pragmatism (interim technologies) with ambition (EVs and zero‑emission targets).


Why phase‑out is being suggested: clear drivers

  • Air quality and public health: Diesel engines are substantial contributors to NOx and particulate pollution in our cities.
  • Climate commitments: Transport emissions must fall if India is to meet its long‑term targets.
  • Energy security: Reduced dependence on oil imports aligns with national economic priorities.
  • Technological trajectory: Battery costs continue to fall and automakers are investing heavily in cleaner platforms.

These drivers are familiar; I’ve written about them before and argued that financing and infrastructure are as critical as vehicle technology in making transitions real (High-level Discussions to Phase Out Petrol & Diesel Vehicles Underway).


Implications for industry, consumers and the environment

Industry

  • Manufacturers will need to accelerate R&D, retool factories and manage legacy diesel supply chains and dealer networks.
  • Component suppliers (especially for diesel-related parts) face disruption but can pivot to EV components, hydrogen systems or biofuel-compatible technologies.
  • Fleet operators and logistics companies may see high short‑term capex to upgrade vehicles but long‑term operational savings from electrified fleets.

Consumers

  • Buyers may face higher upfront costs for cleaner vehicles initially, but lower running costs (especially for EVs) and better air quality benefits.
  • Used‑vehicle markets will need clear rules (scrappage schemes, life-cycle protections) so current owners are not unfairly penalised.

Environment

  • Urban air quality should improve as the oldest and dirtiest diesel units leave the road, assuming the electricity that powers EVs becomes cleaner.
  • Lifecycle emissions depend on how we decarbonise power and build batteries and biofuels sustainably.

Possible timeline and sequencing (practical view)

A rapid, nationwide ban overnight is neither politically nor technically feasible. A realistic sequence would be:

  1. Near term (0–5 years): Restrictions on older, high‑emission diesel vehicles; stronger scrappage incentives; push for CNG and hybrids in commercial fleets.
  2. Medium term (5–15 years): Rapid EV adoption supported by charging networks, local battery manufacturing, and demand incentives; phase‑out of new diesel sales in urban and peri‑urban areas.
  3. Long term (15+ years): Predominant zero‑emission fleet with use of hydrogen/biofuels in hard‑to‑electrify heavy transport.

This staggered path reduces social shocks and gives industry and consumers time to adapt.


Challenges and trade‑offs

  • Charging infrastructure and grid readiness: EVs will only be as clean as the power that charges them. Rapid renewables deployment and grid upgrades must go hand‑in‑hand.
  • Finance and affordability: Even with lower lifetime costs, high upfront prices and limited financing remain barriers.
  • Employment and supply chains: Thousands of jobs in the diesel value chain will be affected; planned transitions, reskilling and industrial policy can soften the blow.
  • Political economy: States, tax regimes and fuel pricing create complex incentives that must be aligned.

Policy recommendations (my view)

  • Design phased bans tied to clear milestones (air‑quality targets, charging density) rather than hard dates alone.
  • Create targeted financing windows and tax incentives for EVs, batteries and charging infrastructure, including low‑cost loans for fleets.
  • Invest aggressively in renewables and local battery manufacturing to address lifecycle emissions and supply security.
  • Implement transparent scrappage and buy‑back schemes for older diesel vehicles to protect vulnerable owners and stimulate demand for cleaner alternatives.
  • Strengthen public transport and urban planning to reduce vehicle‑km travelled—technology alone won’t solve congestion and pollution.

A balanced conclusion

I welcome Niti Aayog’s roadmap because it frames the end goal while recognising intermediate realities. The success of any diesel phase‑out will depend less on a headline date and more on carefully sequenced policy, financing, industrial strategy and honest urban planning. India can and should aim for cleaner mobility — but the journey must be just, affordable and powered increasingly by clean electricity.


Regards,
Hemen Parekh


Any questions / doubts / clarifications regarding this blog? Just ask (by typing or talking) my Virtual Avatar on the website embedded below. Then "Share" that to your friend on WhatsApp.

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Hello Candidates :

  • For UPSC – IAS – IPS – IFS etc., exams, you must prepare to answer, essay type questions which test your General Knowledge / Sensitivity of current events
  • If you have read this blog carefully , you should be able to answer the following question:
"What are the main policy tools a government can use to ensure a fair and affordable transition away from diesel vehicles?"
  • Need help ? No problem . Following are two AI AGENTS where we have PRE-LOADED this question in their respective Question Boxes . All that you have to do is just click SUBMIT
    1. www.HemenParekh.ai { a SLM , powered by my own Digital Content of more than 50,000 + documents, written by me over past 60 years of my professional career }
    2. www.IndiaAGI.ai { a consortium of 3 LLMs which debate and deliver a CONSENSUS answer – and each gives its own answer as well ! }
  • It is up to you to decide which answer is more comprehensive / nuanced ( For sheer amazement, click both SUBMIT buttons quickly, one after another ) Then share any answer with yourself / your friends ( using WhatsApp / Email ). Nothing stops you from submitting ( just copy / paste from your resource ), all those questions from last year’s UPSC exam paper as well !
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When Schools Race to AI

When Schools Race to AI

When Schools Race to AI

Introduction

There is a new national and global push to bring more AI and IT into classrooms — faster devices, AI tutors, administrative automation, and digital curricula. As someone who has watched technology reshape education for years, I welcome the promise of better personalization and teacher support. But I also worry that the rush to equip schools with AI can outpace our safeguards, deepen inequities, and change what it means to teach and learn.

In this post I’ll lay out the benefits and the risks, unpack equity and privacy issues, consider effects on teacher roles and skills, and end with pragmatic policy recommendations and real-world examples. I’ve written about AI’s educational potential before From interview to personalised learning, and this feels like the moment we balance enthusiasm with responsibility.

The benefits — why the push is happening

  • Personalized learning at scale: AI can adapt content and pacing to each learner’s needs, helping close gaps in mixed-ability classrooms.
  • Efficiency for teachers: Automated grading of routine tasks, lesson-generation aids, and administrative workflows can free teacher time for higher-value interactions.
  • Accessibility and differentiation: Tools can provide language supports, alternate formats, and scaffolded practice for students with special needs.
  • Data-informed interventions: Early-warning analytics can help schools identify students at risk and target supports earlier.

These opportunities are well-documented in policy and research reviews — they are why ministries and districts are investing now (OECD, 2024).

The concerns — what keeps me awake

  • Privacy and surveillance: Many AI services require student-level data. Without strict protections, schools risk invasive monitoring and data misuse (U.S. Dept. of Education report).
  • Algorithmic bias: AI reflects the data it was trained on. Left unchecked, systems can reproduce or amplify racial, linguistic, or socioeconomic bias in assessment and recommendations (Frontiers review).
  • Erosion of human judgment: Overreliance on automated suggestions risks sidelining teacher expertise and weakening critical thinking instruction.
  • Commercial influence: Rapid procurement without pedagogical oversight can let private vendors shape curriculum, assessment, and learning priorities.
  • Unequal access: Devices, broadband, paid subscriptions, and AI literacy are unevenly distributed — the digital divide can become an AI divide (US Commission on Civil Rights, 2024).

Equity, privacy, teachers and skills — a closer look

Equity: Technology only narrows gaps when accompanied by intentional programs (devices, connectivity, local language content, and teacher coaching). Absent that, the best tools go to those already advantaged. International reviews urge equity-first deployment and inclusive design processes (OECD, 2024).

Privacy: Student data governance has to be explicit. We need clear limits on what is collected, how long it’s stored, who can see it, and rules preventing commercial repurposing. Consent, transparency, and local control are non-negotiable.

Teacher roles: AI should augment — not replace — teachers. That means training teachers in AI literacy so they can interpret tool outputs, correct errors, and make professional judgments. Professional learning must be sustained, practical, and tied to classroom practice.

Skills for students: Alongside subject knowledge, we must teach critical evaluation of AI outputs, digital ethics, and data literacy so students learn to use AI responsibly and creatively.

Policy recommendations (practical and actionable)

  1. Establish baseline safeguards before procurement
  • Require privacy, security, and data-minimization standards for any product used in schools.
  1. Promote human-in-the-loop decision-making
  • Ban automated decisions that materially affect students without teacher review and contestability.
  1. Fund equitable access packages
  • Devices, connectivity, and paid tool licenses for under-resourced schools should be part of any rollout.
  1. Invest in teacher capacity, not just hardware
  • Long-term professional learning, exemplar lesson plans, and local coaching are essential.
  1. Audit and monitor algorithmic impact
  • Regular independent audits for bias and disparate impact, with public reports and remediation plans.
  1. Limit commercial influence in curriculum design
  • Procurement should prioritize pedagogical fit and open interoperability over vendor lock-in.

These are consistent with recommendations from national and international reviews and civil-rights inquiries (U.S. Dept. of Education; USCCR, 2024).

Practical examples — small, responsible ways to start

  • Start with pilots, not mandatory rollouts: Try AI lesson-planning aids in a defined set of classes, evaluate outcomes, and scale based on evidence.
  • Use AI for low-stakes, high-return tasks: Automated transcript generation, language translation supports, and practice quizzes that give formative feedback.
  • Co-design with communities: Include parents, students, and marginalized groups in tool selection and evaluation to surface local risks.
  • District-level procurement consortia: Pool buying power to negotiate stronger privacy and interoperability terms with vendors.

Conclusion

I believe AI and IT can strengthen learning — but only if we decouple enthusiasm from inevitability. The current big push gives us a narrow window to set rules, center equity, and protect privacy. If we act thoughtfully — funding access, training teachers, auditing algorithms, and keeping humans in the loop — we can harvest real benefits while limiting harm. If we fail to do so, we risk amplifying the very inequities schools should be correcting.

Short sign-off: Let’s welcome innovation, but not at the cost of our values.


Regards,
Hemen Parekh


Any questions / doubts / clarifications regarding this blog? Just ask (by typing or talking) my Virtual Avatar on the website embedded below. Then "Share" that to your friend on WhatsApp.

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Hello Candidates :

  • For UPSC – IAS – IPS – IFS etc., exams, you must prepare to answer, essay type questions which test your General Knowledge / Sensitivity of current events
  • If you have read this blog carefully , you should be able to answer the following question:
"What are the top three policy safeguards schools should implement before adopting AI tools?"
  • Need help ? No problem . Following are two AI AGENTS where we have PRE-LOADED this question in their respective Question Boxes . All that you have to do is just click SUBMIT
    1. www.HemenParekh.ai { a SLM , powered by my own Digital Content of more than 50,000 + documents, written by me over past 60 years of my professional career }
    2. www.IndiaAGI.ai { a consortium of 3 LLMs which debate and deliver a CONSENSUS answer – and each gives its own answer as well ! }
  • It is up to you to decide which answer is more comprehensive / nuanced ( For sheer amazement, click both SUBMIT buttons quickly, one after another ) Then share any answer with yourself / your friends ( using WhatsApp / Email ). Nothing stops you from submitting ( just copy / paste from your resource ), all those questions from last year’s UPSC exam paper as well !
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On-Screen Marking Arrives

On-Screen Marking Arrives

I read the announcement with a mix of relief and scrutiny: the Central Board of Secondary Education will introduce On-Screen Marking (OSM) for Class 12 answer books starting with the 2026 exams CBSE announces on screen marking system for 2026 Class 12 exams — The Tribune.

Why this matters to me

As someone who has long argued that technology must be harnessed to bring fairness and scale to education, this feels like the logical next step. The change is targeted: keep pen-and-paper exams, but shift the evaluation to a digital platform. The expected benefits are clear in the board’s communication — fewer totalling errors, faster processing, broader teacher participation and reduced logistical overhead.

What CBSE expects schools to do

The move isn't just a software flip. Schools will need to meet concrete technical requirements before they can participate in evaluation:

  • A computer lab with a public static IP.
  • Machines running Windows 8 or above, with minimum 4 GB RAM and 1 GB free on C: drive.
  • Updated web browsers and Adobe Reader.
  • Reliable internet (minimum 2 Mbps) and uninterrupted power supply.

CBSE plans training, dry runs, instructional videos and a call centre to smooth the transition — sensible, but not sufficient on its own.

The immediate upsides

  • Elimination of arithmetic/totalling errors via automated calculation.
  • Faster turnaround and less physical transport of answer books (time and carbon savings).
  • Wider pool of examiners: teachers can evaluate from their own schools, increasing flexibility.
  • Potentially reduced need for manpower in post-result verification procedures.

These are practical wins. But the policy's success will depend on equitable access and rigorous implementation.

My concerns (and what I’d watch for)

  • Digital divide: many schools — especially smaller or resource-constrained ones — will struggle to meet the stated infrastructure requirements. Without targeted funding or phased concessions, the change could widen inequities.
  • Security and integrity: scanned answer scripts, access controls, audit logs and encryption must be bulletproof. Dry runs and call centres help, but robust, independently audited security is essential.
  • Training and calibration: evaluation uniformity depends on well-calibrated rubrics and sustained training. A screen-based interface can standardise some elements, but human judgment still dominates.
  • Change management: the logistics of assigning scripts, monitoring evaluations, and handling exceptions need operational playbooks that account for connectivity outages, hardware failures and local constraints.

Where this fits in a longer story

This is not an isolated reform. Over the last couple of years I’ve written about CBSE’s push toward surveillance and tech-assisted exam administration — for example, when the board mandated CCTV in exam rooms and we discussed how technology can reduce malpractices and improve transparency CBSE mandates CCTVs in class for 10 & 12 board exams in 2025. That conversation anticipated many of the operational and ethical trade-offs we now face with OSM.

The pattern is familiar: incremental, back-end digitalisation aimed at improving fairness while keeping the visible experience for students unchanged. That’s a pragmatic approach — but it obliges policymakers to address access gaps and privacy safeguards proactively.

Practical advice for schools, teachers and parents

  • Schools: audit your infrastructure now. If you lack a public static IP or reliable bandwidth, start conversations with local ISPs and education authorities about subsidies or pooled evaluation centres.
  • Teachers: request hands-on training and insist on calibration sessions so marking standards remain consistent across evaluators.
  • Parents and students: ask schools how OSM will affect re-check processes, timelines for results, and data privacy policies.

A note on governance and equity

Digital reforms succeed when accompanied by governance: clear SLAs for the tech platform, external audits for security and privacy, compensation or support for schools that lack readiness, and transparent escalation routes for students who believe their answers were mishandled. Without these, a well-intentioned reform risks leaving the most vulnerable behind.

In closing

I welcome the move toward on-screen marking because it addresses concrete pain points — arithmetic errors, slow processing, and logistical cost. But we must be vigilant: scale the rollout with fairness, secure the systems, and invest in training. Done well, OSM can be a building block toward an examination system that is faster, fairer and more sustainable. Done poorly, it will be another layer of digital bureaucracy that deepens existing divides.


Regards,
Hemen Parekh


Any questions / doubts / clarifications regarding this blog? Just ask (by typing or talking) my Virtual Avatar on the website embedded below. Then "Share" that to your friend on WhatsApp.

Get correct answer to any question asked by Shri Amitabh Bachchan on Kaun Banega Crorepati, faster than any contestant


Hello Candidates :

  • For UPSC – IAS – IPS – IFS etc., exams, you must prepare to answer, essay type questions which test your General Knowledge / Sensitivity of current events
  • If you have read this blog carefully , you should be able to answer the following question:
"What are the minimum technical requirements CBSE has asked schools to meet for the 2026 On-Screen Marking rollout?"
  • Need help ? No problem . Following are two AI AGENTS where we have PRE-LOADED this question in their respective Question Boxes . All that you have to do is just click SUBMIT
    1. www.HemenParekh.ai { a SLM , powered by my own Digital Content of more than 50,000 + documents, written by me over past 60 years of my professional career }
    2. www.IndiaAGI.ai { a consortium of 3 LLMs which debate and deliver a CONSENSUS answer – and each gives its own answer as well ! }
  • It is up to you to decide which answer is more comprehensive / nuanced ( For sheer amazement, click both SUBMIT buttons quickly, one after another ) Then share any answer with yourself / your friends ( using WhatsApp / Email ). Nothing stops you from submitting ( just copy / paste from your resource ), all those questions from last year’s UPSC exam paper as well !
  • May be there are other online resources which too provide you answers to UPSC “ General Knowledge “ questions but only I provide you in 26 languages !




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Milking Data

Milking Data

Why I Called "Sarlaben"

This morning I found myself thinking about a simple act I had watched since childhood: a woman gently milking a cow, feeling the animal’s warmth and rhythm. That quiet intimacy between human and animal has always felt far removed from the cold dashboards of data science — until now.

Amul’s new AI assistant, "Sarlaben", is changing that distance. By turning decades of cooperative milk records, veterinary logs, and even satellite fodder data into an always-on advisory service, the dairy world is quietly moving from cowshed intuition to cloud-powered decisions From cowshed to cloud: Amul Diary launches AI assistant 'Sarlaben' .

I wrote about digital farming some time ago — about how data could nudge traditional practices toward higher productivity and inclusion — and Sarlaben feels like that idea arriving in the market, in voice and Gujarati, at scale Digital Farming .

What "Milking Data" Really Means

Milking data is not a metaphor — it is a sequence of practical steps:

  • Collect: every litre at the village collection point, each veterinary visit, the insemination log; decades of this live in cooperative systems.
  • Clean & integrate: make procurement, health, and fodder signals talk to one another.
  • Analyse: surface early-warning signs for mastitis, nutritional gaps, or breeding windows.
  • Advise: deliver personalised, actionable guidance back to the farmer — by app or by phone.

Sarlaben demonstrates each link of that chain. Importantly, it makes the guidance personal: not generic best practices, but recommendations shaped by a farmer’s herd history and cooperative records.

Why Voice + Local Language Matters

Technology often stalls at the last mile. What Amul is doing right is coupling high-fidelity data with accessibility:

  • Voice calls for feature-phone users.
  • Gujarati as the first language of interaction.

These design choices recognise a truth I’ve returned to in my writing: adoption is not just about accuracy; it’s about language, trust, and habit. A farmer who can call and ask about a bloated rumen at 4 a.m. — and get an immediate answer — is more likely to act than one who must read a long advisory in English on an app.

The Upsides I Celebrate

  • Productivity gains: timely feeding and vaccination reminders can raise yields and reduce loss.
  • Inclusion: women producers, smallholders and feature-phone users can join an information loop they were historically excluded from.
  • Systemic intelligence: connecting AMCS-style procurement data with veterinary logs enables insights that no single veterinarian or extension worker could infer alone.

These are the wins I had hoped digital farming could deliver. Today, those hopes look less like theory and more like operational reality.

What Still Keeps Me Awake

Data-led care is promising — but not unproblematic. A few caveats I worry about:

  • Data governance: who owns the herd-level data? Cooperatives, farmers, platform providers, or the AI team? Clear rules are essential.
  • Algorithmic bias: models trained on cooperative data from one region may not generalise to very different geographies, breeds, or feed patterns.
  • Over-reliance: advice should augment, not replace, local vets and indigenous knowledge.
  • Incentives: an always-on assistant can become a channel for nudges that prioritise procurement or inputs. Transparency is non-negotiable.

If milk becomes a source of recurring, monetisable signals, we must ensure farmers truly benefit from the value they produce.

How I’d Design the Next Phase

If I were advising the cooperative and its partners, I would push for three priorities:

  1. Farmer-first data contracts: opt-in models with clear revenue- or service-sharing for insights derived from herd data.
  2. Local vet integration: build workflows so that AI flags are triaged by local vets, not only by call-centre scripts.
  3. Explainability & recourse: every recommendation should come with a plain-language rationale and a path for a farmer to contest or seek second opinion.

These are not technical luxuries — they are what turn a novelty into a durable public good.

A Small Thought to End On

Watching a woman milk a cow used to be an act I associated with patience and craft. Today, that same act is also a stream of signals: yield, health, cycles, economics. If we steward that stream well — with consent, transparency and local partnership — we can amplify livelihoods without diminishing dignity.

Sarlaben is an early, important step. I’ll be following how farmers use the voice channel, how vets respond to AI alerts, and whether cooperative governance evolves fast enough to keep the benefits where they belong: in the hands of the producers.


Regards,
Hemen Parekh


Any questions / doubts / clarifications regarding this blog? Just ask (by typing or talking) my Virtual Avatar on the website embedded below. Then "Share" that to your friend on WhatsApp.

Get correct answer to any question asked by Shri Amitabh Bachchan on Kaun Banega Crorepati, faster than any contestant


Hello Candidates :

  • For UPSC – IAS – IPS – IFS etc., exams, you must prepare to answer, essay type questions which test your General Knowledge / Sensitivity of current events
  • If you have read this blog carefully , you should be able to answer the following question:
"How can cooperative-owned AI assistants balance personalized herd-level advice with farmer data privacy and benefit-sharing?"
  • Need help ? No problem . Following are two AI AGENTS where we have PRE-LOADED this question in their respective Question Boxes . All that you have to do is just click SUBMIT
    1. www.HemenParekh.ai { a SLM , powered by my own Digital Content of more than 50,000 + documents, written by me over past 60 years of my professional career }
    2. www.IndiaAGI.ai { a consortium of 3 LLMs which debate and deliver a CONSENSUS answer – and each gives its own answer as well ! }
  • It is up to you to decide which answer is more comprehensive / nuanced ( For sheer amazement, click both SUBMIT buttons quickly, one after another ) Then share any answer with yourself / your friends ( using WhatsApp / Email ). Nothing stops you from submitting ( just copy / paste from your resource ), all those questions from last year’s UPSC exam paper as well !
  • May be there are other online resources which too provide you answers to UPSC “ General Knowledge “ questions but only I provide you in 26 languages !




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Small Cars, Big Policy Tradeoffs

Small Cars, Big Policy Tradeoffs

I watched the latest NITI Aayog recommendation on Corporate Average Fuel Efficiency (CAFE) norms and felt a familiar tug — policy trying to balance climate ambition, industrial competitiveness, and everyday affordability. The think‑tank’s push to incentivise lightweight, fuel‑efficient small cars under CAFE is a deliberate attempt to nudge first‑time buyers toward lower‑emission choices while keeping mobility affordable for millions source.

What the recommendation says (in short)

  • NITI Aayog’s transport scenarios emphasise that a near‑term increase in zero‑emission vehicles is necessary for net‑zero pathways, but also that India’s current car ownership is low (~33 cars per 1,000 people). That means many households view a car as aspirational; affordability matters.
  • The report calls for CAFE to incentivise smaller, lighter entry‑level cars because they offer higher fuel efficiency, lower tailpipe emissions, and reduced pressure on congestion and parking — provided lifecycle emissions and sustainable biofuel benefits are considered.
  • Crucially, the study suggests a time‑bound sunset for any relaxations so that incentives don’t become permanent loopholes and the industry is prepared for global low‑emission export standards source.

The practical contours already being debated

Regulators and agencies have been working through draft language for CAFE‑3 (effective 2027–32). The key operational idea under discussion is a limited emission credit for qualifying small petrol cars — the concrete design (weight, length, engine capacity thresholds; and the magnitude of CO₂ deduction) matters a great deal. Past drafts and industry responses show how contentious this can be: relaxations can protect affordable mobility but also shift competitive advantage and risk undermining safety or electrification incentives [analysis in media commentary].

Why I welcome the intent — and where I worry

I welcome the intent. Policy should be rooted in social context: if a first‑time rural or peri‑urban buyer upgrades from a two‑wheeler to an ultra‑efficient small car, we gain real societal value. Smaller cars can deliver immediate emissions reductions per km and make personal mobility inclusive.

But design details will determine whether incentives amplify the public good or create distortions. My concerns are:

  • Safety: Lightweight, ultra‑cheap cars sometimes score poorly on crash tests. Incentives must be conditional on minimum safety standards.
  • Competitive fairness: A narrowly calibrated concession can advantage a very small set of models or manufacturers and distort the market.
  • Lock‑in risk: If incentives persist indefinitely, they may delay the transition to EVs and lifecycle‑cleaner solutions.
  • Lifecycle accounting: Tailpipe emissions aren’t the whole story — fuel production, use of biofuels, and end‑of‑life impacts must factor into policy.

Practical principles I’d press for (policy design checklist)

  • Time‑bound relief: Any concession for small cars should have a clear sunset clause tied to measurable milestones (e.g., EV market share thresholds, tightened WLTP targets).
  • Safety gating: Eligibility must require minimum crash‑safety and occupant protection scores.
  • Lifecycle credits: Recognise sustainable biofuel blends and well‑measured lifecycle CO₂ benefits rather than reward only lab‑test tailpipe numbers.
  • Anti‑gaming caps: Cap cumulative per‑manufacturer benefits so a single model or OEM cannot disproportionately capture credits.
  • Complementary support for EVs/hybrids: Maintain or strengthen multipliers/credits for genuine zero‑emission technologies so the pathway to electrification remains attractive.
  • Transparent data & periodic review: Require public disclosure of fleet compositions, credits claimed, and a scheduled policy review every 2–3 years.

A few second‑order effects policy must anticipate

  • Urban planning and parking: Cheaper small cars may increase vehicle kilometres traveled if public transport and last‑mile options aren’t improved.
  • Used‑car markets: Price shifts can affect the second‑hand market and long‑term fleet emissions — plan for end‑of‑life and scrappage incentives.
  • Industrial strategy: Clear sunset notices help OEMs plan investments in EVs, hybrids, and safer platforms rather than chasing temporary advantages.

Where this fits in the arc of my past thinking

I’ve written about tighter fuel efficiency rules and the need to pair regulation with long‑term action plans before — the question of sequencing (tightening norms, EV incentives, charging infrastructure, and safety) has been a recurring theme in my reflections see one of my earlier posts on CAFE and electrification. What’s different now is the intensity of the industry debate and the clarity that tradeoffs are unavoidable.

My bottom line

Policy that deliberately nudges affordability toward cleaner, lighter cars can be part of a just transition — but only if crafted with discipline. Time limits, safety gates, lifecycle accounting and complementary EV incentives will determine whether the NITI Aayog suggestion becomes a smart bridge or a policy detour.

I’m hopeful because India’s regulatory institutions are capable of pragmatic design — but they must resist either moralizing about affordability or bowing to narrow industry rent‑seeking. Thoughtful, data‑driven CAFE rules can keep mobility affordable, protect citizens on our roads, and accelerate the shift to lifecycle‑clean transport.


Regards, Hemen Parekh


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