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24 Jul 2026

The latest Environmental Performance Index reveals global leaders and laggards, while raising important questions about wealth, policy and environmental progress.   Imagine two countries. One enjoys clean rivers, healthy forests, and fresh air. The other struggles with polluted cities, shrinking biodiversity, and rising climate risks. On the surface, the difference looks financial - richer nations simply have more means to safeguard the environment.  But is it so? The 2026 Environmental Performance Index (EPI) has once again highlighted on how nations rank on nature conservation and public health.Estonia claimed the top position this year, while European countries continued to dominate the rankings.Many lower-income countries continued to rank near the bottom, but the results raise a bigger question: Is environmental performance simply a reflection of economic wealth? It’s not a simple yes-or-no answer! Developed by Yale researchers, the Environmental Performance Index ranks countries based on dozens of environmental indicators. It evaluates factors such as air quality, sanitation, waste, biodiversity, climate policy, and ecosystem protection. The index goes beyond a single environmental measure, evaluating how effectively countries pursue economic growth while safeguarding the environment. The rankings matter every year because they tell a bigger story: not just who’s ahead or behind, but what path each country chose for development.Estonia's climb to the top is the result of long-term planning. Over the years, it has strengthened environmental policies while investing in clean energy, efficient waste management, and digital systems that support better management of natural resources.Across Europe, many countries have shown that economic growth and strong environmental standards can advance together. These results raise a further issue: if the European model is so effective, why has it not been applied globally?For many developing countries, the issue is less about ambition and more about competing priorities. With limited resources, governments must balance environmental action alongside poverty reduction, healthcare, housing, employment, and infrastructure.Many countries lack the financial resources needed to invest in clean technology or to restore degraded ecosystems. Rapid urbanisation makes it worse. Unchecked expansion of roads, housing, and industry often leads to higher pollution, shrinking green spaces, and increasing pressure on natural resources.The challenge is compounded by climate change. Countries with the lowest emissions are often among the most vulnerable to extreme weather, forcing governments to spend scarce resources on recovery instead of long-term environmental improvements. Comparing countries at different stages of economic development can therefore be misleading. A lower ranking does not necessarily indicate weak environmental commitment. It often reflects differences in income, governance, access to technology, and historical development. Similarly, a higher ranking does not mean every environmental challenge has been resolved.Experts say the bigger story lies beyond the rankings. Instead of focusing on who tops the list, they encourage a closer look at how countries are improving and where further action is needed. Ultimately, sustained progress is a better measure of success than rank alone. Countries that steadily improve air quality, expand renewable energy, strengthen waste management, or protect biodiversity are making meaningful progress, even if their rankings remain low. At the same time, top-performing countries cannot afford to be complacent, as environmental and climate challenges continue to evolve. The 2026 EPI also highlights that environmental protection cannot rest solely with governments. Businesses can reduce their environmental impact by adopting cleaner production methods and cutting emissions. Researchers help shape better policies through scientific evidence. Communities protect local ecosystems, while individuals contribute by conserving water, reducing waste, and choosing more sustainable products. Perhaps the biggest takeaway from this year's rankings is that wealth alone does not define environmental success. Lasting progress depends just as much on effective policies, strong institutions, and sustained action. Experts say lasting environmental progress is built on strong institutions, effective policies, public participation, and long-term planning. Countries that treat sustainability as a continuous priority rather than a short-term initiative are often the ones that achieve enduring results. The real value of the 2026 Environmental Performance Index lies beyond the rankings. Instead of debating who stands at the top or bottom, it should prompt every country to focus on a more meaningful challenge: What practical actions can we take today to create a cleaner, healthier, and more resilient future? At the end of the day, environmental progress is measured not by a country's position on a global index but by the difference it makes on the ground-cleaner air, healthier ecosystems, and better lives for the people who rely on them.   Sources:  Centre for Integrated Earth System InformationYale Centre for Geospatial Solutions   ...Read more

24 Jul 2026

When coastal communities get the right support, the journey from the sea to the market can become a story of resilience, livelihoods and sustainable growth.   Kolkata |24 July, 2026:   For thousands of families along India's coastline, fishing is more than a livelihood- it is a way of life.But rising sea levels, shifting weather patterns, and declining fish stocks are making it harder for coastal communities to sustain their livelihoods. As climate threats increase, communities are exploring new approaches to protect their incomes and natural resources. On July 9, 2026, three women's self-help groups (SHGs) from Maharashtra brought value-added seafood products to a national exhibition under the Enhancing Climate Resilience of India's Coastal Communities (ECRICC) project, highlighting new livelihood opportunities for coastal communities.The initiative proves climate adaptation isn’t just about resilience - it’s about new jobs and income. By backing women entrepreneurs, sustainable fisheries and better market access, it shifts climate action from cost to opportunity. Instead of selling fresh fish at modest prices, the women are creating value-added seafood products through processing, packaging, and branding, helping them earn more from every catch.According to experts, this approach boosts household incomes, raises profit margins, cuts post-harvest losses, and generates new jobs in coastal communities.It also promotes improved food safety standards and gives producers access to wider markets and new customers beyond their local communities. The process begins with seafood sourced responsibly from local fishermen, followed by cleaning, processing, packaging and labelling prior to distribution through exhibitions, retailers and local markets. This coast-to-consumer value chain generates employment at every step - from procurement and processing to packaging, branding and marketing. Experts say models like this help communities earn more from existing resources rather than adding pressure on fish stocks.  Local Fishermen         ↓ Sustainable Fish Harvest         ↓ Cleaning & Processing         ↓ Packaging & Branding         ↓ Food Safety & Licensing         ↓ Exhibitions / Retail Markets         ↓ Consumers   The initiative is supported by the Mangrove Cell, the United Nations Development Programme (UNDP), and the Green Climate Fund under the ECRICC project.The programme equipped women with skills across the entire business chain; including food processing, quality control, branding, packaging, licensing, and enterprise management, while providing financial and business support too. These skills are helping them build businesses that can withstand climate and economic shocks. Experts say the real challenge begins after the exhibition. Long-term success will depend on building reliable supply chains, maintaining food safety standards, strengthening branding, improving logistics, and expanding access to stable markets. Quality products alone are not enough. Without strong support system, community enterprises may find it difficult to compete in larger markets.   Growing coastal businesses is only a part of the solution. Experts say long-term success will depend on balancing economic opportunities with healthy marine ecosystems through sustainable fishing, responsible sourcing, and stronger mangrove conservation. Sustainable management of local fisheries will be crucial to ensuring marine resources remain available for future generations.Experts believe wider access to finance, digital sales platforms, and organised retail networks can help women's self-help groups scale their businesses. Continued institutional support will be equally important to ensure growth is environmentally sustainable and community-driven. ProductValue AdditionCommunity BenefitDried FishHygienic processing & packagingLonger shelf life and higher incomeFish PickleReady-to-eat productBetter profit marginsFish PowderNutrient-rich food ingredientReduced fish wastePrawn PicklePremium branded productAccess to urban marketsDry Fish SnacksRetail-ready packagingEmployment for women The Maharashtra initiative suggests that climate resilience is built not only by protecting the environment but also by strengthening livelihoods. Experts say supporting women-led enterprises, improving seafood value chains, and conserving coastal ecosystemscan create a future where economic development and environmental sustainability reinforce one another.   Document Support:Press Information Bureau (9 July 2026), Mangrove Cell, Government of Maharashtra, Enhancing Climate Resilience of India's Coastal Communities (ECRICC), United Nations Development Programme (UNDP), Green Climate Fund (GCF), Food Safety and Standards Authority of India (FSSAI) – Food processing and licensing guidelines (background reference) Sources: Press Information Bureau (PIB) – 9 July 2026, Mangrove Cell, Government of Maharashtra, Enhancing Climate Resilience of India's Coastal Communities (ECRICC), United Nations Development Programme (UNDP), Green Climate Fund (GCF) ...Read more

23 Jul 2026

India's green hydrogen ambitions are entering a phase where proving claims may matter as much as making them. Kolkata | 23 July, 2026: India is betting big on green hydrogen, with plans to establish itself as a global leader in the emerging clean fuel sector. As policies take shape and investments grow, the focus is shifting from ambition to execution. Can India's green hydrogen meet global expectations and earn international trust? On 2 July 2026, the Ministry of New and Renewable Energy (MNRE) highlighted India's progress under the National Green Hydrogen Mission, with the focus shifting towards certification, quality assurance, and global market preparedness.Experts say it’s time to move beyond promises. The real test begins now: can producers meet global benchmarks and win the confidence of international buyers? Renewable electricity is used to split water into hydrogen and oxygen to produce green hydrogen. It is seen as critical for decarbonising industries that cannot easily switch to electricity, such as steel, fertilisers, refineries and shipping. Experts argue that making green hydrogen is only half of the task. Proving that it is produced with renewable electricity is equally important, keeping emissions low throughout the process, and meeting certification standards expected by both domestic and global markets. The launch of the certification portal in June marked a significant step in that direction. It aims to help producers track renewable power, assess emissions intensity, and demonstrate alignment with global sustainability benchmarks. Renewable Energy       ↓  Electrolyser       ↓Green Hydrogen       ↓Certification & Testing       ↓Storage & Transport       ↓Industrial Users(Steel • Fertiliser • Refineries • Shipping • Heavy Mobility) Industry experts believe such transparency will make Indian hydrogen more competitive, especially with export markets raising their environmental bar high. There’s a gap between promise and plant. Many firms have unveiled green hydrogen plans, but commercial production remains the exception.According to analysts, distinguishing between announced projects and commissioned plants will provide investors, policymakers and buyers with a clearer view of India’s real progress.Certification and testing are becoming essential to building trust and credibility in the green hydrogen sector.Experts argue credibility hinges on four things: certified labs, independent checks, digital certification, and clear rules.In global markets, claims are not enough. Without trusted certification, Indian producers may struggle to earn buyers' confidence. The transition is expected to begin with industries that use the most energy and produce the highest emissions. Experts expect fertiliser manufacturers, oil refineries, steel producers, shipping firms and heavy mobility operators to emerge as the first major users of green hydrogen. They offer the greatest potential for emission reductions while also ensuring steady demand for producers. Experts stress that smaller technology firms should not be overlooked.Numerous start-ups are developing electrolysers, storage systems, sensors, monitoring tools and safety technologies to support India’s hydrogen sector. They argue that better access to funding, testing infrastructure, certification support and government-led pilot programmes would enable these companies to engage more effectively in the expanding market. Industry leaders also emphasize that sustained investment in renewable power, transmission infrastructure and hydrogen storage is essential for scaling production sustainably. They argue that certification should cover more than just renewable electricity - it should also track emissions intensity and additionality to confirm that new clean power is being added, not just existing sources. Smaller tech companies also need improved access to testing facilities, certification support, funding and pilot initiatives to integrate into the growing national green hydrogen sector. According to experts, greater collaboration between the public sector, research bodies and industry will drive down costs, accelerate tech development and create a more resilient supply chain. Current ProgressWhat Still Needs AttentionRenewable energy expansion Internationally accepted certificationGreen hydrogen pilot projects   More commissioned commercial plantsCertification portal launchedAccredited testing laboratoriesGovernment policy supportStrong domestic demandGrowing investmentsFaster support for technology start-ups The National Green Hydrogen Mission offers India a key opportunity to enhance energy security and cut industrial emissions. But experts say the real challenge begins now! India’s green hydrogen ambitions will be measured by credible certification, clear emissions reporting, robust testing and rising demand - not just by targets.    Sources: Ministry of New and Renewable Energy (MNRE), National Green Hydrogen Mission, Green Hydrogen Certification Scheme of India, Bureau of Energy Efficiency (BEE), Press Information Bureau (PIB), Down To Earth (Background), The Hindu BusinessLine (Background)  ...Read more

21 Jul 2026

India's latest Environmental Performance Index ranking has reignited a debate that goes far beyond the final score.     Kolkata | 21 July 2026:   Another year, another low rank. India placed 176th out of 177 in the 2026 Environmental Performance Index, reigniting questions about what’s working, what isn’t, and how we measure success.  Out in July from Yale, the EPI scores 177 countries on 47 measures of health, nature, and climate. The numbers have sparked arguments, but the experts are saying not to read it as a report card but as a trend line Environmental Performance Index (2024)IndiaGlobal Rank176 / 180Overall EPI Score27.6 / 100Environmental Health Rank177Ecosystem Vitality Rank171Climate Change Rank133   The EPI measures performance across air quality, water, sanitation, waste, biodiversity, forests, emissions and more. Experts say this approach captures environmental health more broadly than climate goals or renewable capacity by themselves. Despite strong progress on renewable energy, India still lags in air pollution, waste management, water quality and biodiversity conservation. Experts note that clean power does not automatically address problems such as contaminated water bodies, waste mismanagement, depleting habitats, and urban air pollution. Since 2024, very little has changed. Until India tackles air pollution and gets serious on waste management, water management and ecosystem, the rankings won’t budge – no matter how fast renewable energy grows.The Ministry of Environment, Forest and Climate Change says environmental protection remains a priority, with programmes centred on renewable energy, afforestation, pollution control, and ecosystem conservation. Experts agree the direction is right, but real progress will depend on stronger implementation, consistent monitoring, and better coordination between the Centre and the states. Comparing rankings is only part of the picture. Each country begins its climate journey under different circumstances.Experts say that we need to look at emissions per person, total emissions, and where policy is headed. India is among the world's largest emitters largely because of its population. But on a per-person basis, its emissions remain well below those of many developed countries. India is investing in clean energy, electric mobility, green hydrogen, and forest restoration. But experts say the real measure of success lies elsewhere: cleaner air, safer water, healthier ecosystems, and less pollution. Without visible improvements on the ground, neither environmental outcomes nor EPI rankings are likely to improve. The EPI is more than a ranking- it is a reminder of where improvement is still needed. Experts say the real goal should not be a higher position on a global index, but cleaner air, healthier rivers, stronger ecosystems, and a better quality of life for millions. Source:  Yale Centre for Environmental Law & Policy, Environmental Performance Index 2024 (in partnership with Columbia University Centre for International Earth Science Information Network) ...Read more

21 Jul 2026

The new EV Policy promises cleaner transport, but charging, battery recycling and public mobility will decide whether it delivers.   Kolkata | July 21, 2026:   Buying an electric vehicle is getting easier. But is owning one that easy?  MeasurePurposePurchase incentives Encourage EV adoption Charging stations Improve accessibility Battery swapping Reduce charging time Support for commercial EVs Faster transition for high-mileage vehicles Battery recycling Reduce environmental impact  That’s the central question behind Delhi’s new EV Policy 2026. The plan is to accelerate the shift to cleaner transport through EV incentives, more charging and battery-swapping stations and lower air pollution. But experts say the real test starts once the vehicle is bought. Delhi has struggled with poor air quality for years, as transport continues to be a major source of pollution. The policy goes beyond promoting electric vehicles. It covers two-wheelers, three-wheelers, commercial vehicles and private cars.Policies can boost EV sales. But infrastructure will determine whether the transition succeeds. The real challenge is whether charging networks, battery-swapping services, and the power grid can grow as quickly as per demand.   Experts say the policy's biggest impact will come from commercial vehicles, which spend most of its time on Delhi's roads. Delivery riders, auto-rickshaws, and fleet vehicles are on the road far more than private cars, meaning electrifying them could have the greatest impact on reducing transport emissions.  Operators like the policy, but charging delays and poor infrastructure are still a daily headache. Delivery workers face long charger queues that hurt both work and wages.Charging service providers say keeping up with demand will mean rolling out public chargers faster, expanding battery-swapping networks, and speeding up approvals for new infrastructure.Every new EV sold today also creates a future battery management challenge. With more EVs on the road, more batteries will soon hit the end of their usable life. According to experts, recycling infrastructure must keep pace with EV sales to prevent long-term environmental harm. A sustainable EV ecosystem will depend on proper battery collection, recycling and reuse.  The policy also brings a new challenge: can Delhi's power grid keep pace? As more EVs hit the road, charging thousands of vehicles could place significant pressure on the electricity network, especially during peak hours.   Energy experts say Delhi's long-term EV transition will depend on smart charging, greater use of renewable energy, and better management of electricity demand.  Real impact will come when EVs are paired with reliable public transport and walkable neighbourhoods. Experts argue that cleaner mobility is not only about replacing petrol vehicles with electric ones.Expanding metro connectivity, improving bus services, building safe cycling tracks and creating pedestrian-friendly roads can reduce traffic while reducing emissions further.  Environmental groups have praised the policy's direction but the real challenge begins now. Incentives may encourage adoption, but effective implementation will determine whether the transition succeeds. MeasurePurposePurchase incentivesEncourage EV adoptionCharging stationsImprove accessibilityBattery swappingReduce charging timeSupport for commercial EVsFaster transition for high-mileage vehiclesBattery recyclingReduce environmental impact   EVs are only half the story. The real test is infrastructure, recycling, public transport, and planning that brings everything together.Going green in Delhi will take more than just swapping engines for batteries.The success of Delhi's EV transition will not be measured by the number of vehicles sold, but by whether the transport system can support them.Sustainable mobility requires more than electric vehicles - it requires the infrastructure to keep them going.  Sources: Government of NCT of Delhi, NITI Aayog Ministry of Heavy Industries,      NITI Aayog Ministry of Heavy Industries                      , Central Electricity Authority  Delhi Pollution Control Committee, The Indian Express ...Read more

20 Jul 2026

Floods don't begin in the clouds. They begin in the way we shape our cities.    By Tiyasha Ghosh    Can we keep blaming just the rain for floods? Or are our cities part of the problem even today? The monsoon arrives with hope, every year.Water for our reservoirs, life for our farms, and relief from the heat.However, every year, it leaves behind waterlogged streets, damaged infrastructure, destroyed homes and many lost lives. Two places, two disasters: Mumbai drowned, Wayanad collapsed! One is a city of skyscrapers and the other is a quiet forested district. Different locations but identical warning! The sky changed faster than the concrete below it. Our infrastructure was designed for a climate that no longer exists. We used old rainfall recording system and assumed stability. Today, climate change delivers heavier rain with no warning, everything at once.  Rain is arriving faster than we can handle. Cloudbursts are turning mountains into landslide zones. The question isn’t “how much rain this season?” But the question isn’t “how much?” It’s “how fast?” - and can our land and roads survive it? Which leaves us with one question: Whether India’s design standards use up-to-date rainfall data, or continue to rely on old IDF curves that don’t represent today’s climate.According to engineers, many drainage systems were built to handle rainfall expected once in several decades. However, climate records indicate that extreme rainfall events are occurring more frequently. Events once termed "once-in-a-century" storms may be happening much more often now.You can see the impact all over the country. Roads vanish underwater in hours. Drains can’t keep up. Buildings drown even after crores spent on their upgrades. In the hills, the ground itself gives way - mud, rocks and debris crashing into villages below. According to experts, the cause goes beyond rainfall - it points to failures in urban and infrastructure planning. Wetlands that previously stored excess rainwater have been reclaimed for development. Natural drainage channels have been constricted or obstructed. Hillsides have been cut to accommodate roads, hotels and buildings. In many vulnerable regions, declining forest cover has reduced the land’s capacity to absorb water during heavy rainfall. The cost goes far beyond concrete and steel. People lose homes and income. Kids stay out of school. Businesses close. Transport comes to a standstill. Hospitals get overcrowded. These storms are no longer just environmental problems - they hit our economy and society too. Experts argue that India needs to stop treating floods, landslides and waterlogging as separate events. They point to a larger issue like climate change, rapid urbanisation and weak planning coming all together. Unless cities plan for future rainfall instead of past records, every monsoon will bring the same question: Are we preparing for the next storm- or simply recovering from the last one? Heavy Rain       ↓ Wetlands & Lakes       ↓ Natural Streams       ↓ Rivers       ↓ Groundwater Recharge   (Current Situation)   Heavy Rain       ↓ Concrete Roads       ↓ Blocked Drains       ↓ Waterlogging       ↓ Floods & Landslides Natural drainage systems once absorbed excess rainwater. Urbanisation has disrupted these pathways, increasing flood risks The rain hasn't changed. The ground beneath it, has.Like water on concrete instead of a sponge, India's cities can no longer absorb what falls from the sky. Nature once managed the rain. Wetlands, forests, floodplains, and open land worked together to absorb, slow, and store water. Today, many of these natural safeguards have disappeared. Wetlands are disappearing beneath housing projects. Floodplains are turning into commercial hubs. Hillsides are being cut for development. And across India's cities, concrete has replaced the open ground that once soaked up rain. With heavy downpour, water becomes stagnant with no outlet for respite. It keeps flowing until it floods roads, homes, and entire neighbourhoods. Floods today are shaped as much by land use as by rainfall, experts say. Here's why. How do engineers decide how big a drain should be? They use Intensity-Duration-Frequency (IDF) curves, which estimate how much rain can fall, how quickly it may arrive, and how often such events are expected. The problem? A lot of these rules were made using old rainfall data. But climate change has changed those patterns. Cloudbursts have grown more frequent and short-duration rainfall has become more intense. For example, 100 millimetres of rain that previously fell for an entire day can now occur within two to three hours. Drainage systems have not evolved in line with changing rainfall conditions. Many continue to operate based on historical rainfall patterns that are no longer valid. Experts say India can no longer rely on yesterday's rainfall patterns. Infrastructure must be designed using today's climate realities. The challenge is even greater in the hills. Unlike cities, where water usually causes flooding, mountain regions face another danger- landslides. Cutting down forests and carving slopes for roads or buildings loosens up the soil. When heavy and long rainfall persists, water soaks in, weakens the slope, and everything collapses. The Wayanad landslide was a painful reminder: when heavy rain hits fragile hills and if we ignore the risks, it can turn deadly.Scientists say this is why climate adaptation can no longer remain separated from urban or infrastructure planning. Every new road, bridge, housing project, and drainage system must answer one question: Is it built for tomorrow's rainfall? ParameterEarlier ClimateCurrent ClimateRainfall PatternSpread over longer periodsIntense rainfall in short burstsDrainage DesignBased on historical rainfallFrequently exceededWetlandsLarger natural storageRapidly shrinkingFlood FrequencyLess frequentIncreasingClimate RiskModerateHigh Rain may trigger the disaster. But building for yesterday's climate could make it inevitable. Experts say India must rethink how it builds its cities. Instead of forcing water to adapt to development, development must adapt to water. And that begins with something many places have lost, i.e., space. Protecting floodplains, wetlands, hills, and stormwater channels isn't just about conserving nature- it's about protecting people. Because when nature's defenses disappear, concrete isn't enough. Experts say cities can't plan for tomorrow using yesterday's flood maps. Updated rainfall data should guide every development decision, and flood-prone areas must be identified before new roads, housing projects, or commercial complexes that are built. Experts also say IDF curves should be updated regularly so drainage systems are built for today's climate- not yesterday's. Technology can also make a huge difference. Floods can't always be prevented. But with accurate forecasts and real-time monitoring, their impact can be reduced through timely warnings and faster action. But technology alone is not enough; good governance is equally important. Experts say flood management shouldn't begin when the rain starts- it should begin long before. Drains need to be cleared before the monsoon, natural waterways kept free of invasions, and construction in high-risk areas are strictly regulated. Most importantly, agencies must work together before the disaster strikes. Communities also play a crucial role. Communities hold critical, lived knowledge like which streets flood first, which drains fail annually, and which areas remain mostly exposed. When local knowledge becomes part of disaster planning, warnings arrive sooner and responses become more effective. Small actions can also create a big impact. Keeping drains free of plastic waste, protecting neighbourhood ponds, planting trees, avoiding construction on natural drainage channels and following official weather advisories all help reduce flood risks. The lesson extends beyond Mumbai or Wayanad. Urban growth and climate change are colliding. One is covering the ground with concrete, the other is bringing heavier rain. What we build today will shape tomorrow's disasters. India has a choice: keep rebuilding after every disaster- or start preventing the next one. Or we can act now by investing in smarter planning, stronger natural defences, modern infrastructure and cities built for a changing climate. Because resilience is not built during an emergency. It is built way before the first raindrop falls. The cost of preparing may be high but the cost of not preparing will be higher. Mumbai and Wayanad were more than disasters - they were warnings. AspectMumbaiWayanadMain HazardUrban FloodingLandslidesPrimary CauseBlocked drainage & urbanisationFragile slopes & intense rainfallNatural Buffer LostWetlands & mangrovesForest coverMain ImpactWaterlogging & transport disruptionLoss of lives & infrastructure For decades, India has responded after the damage has been done. But experts say rebuilding after every flood and landslide is no longer enough in a climate where extreme weather is becoming the new normal. The focus must shift now from disaster response to disaster prevention. The solution begins with working alongside nature - protecting wetlands, restoring rivers, safeguarding forests, and modernizing drainage standards. It also means planning every new project around future rainfall, not outdated climate records. Climate resilience begins with collective action.Governments, businesses, planners, engineers, and citizens all have a major role to play. Because every protected wetland, every clear drain, and every preserved green space make a city stronger when the next storm arrives. The cost of acting may seem high today but the cost of doing nothing is higher. Every flooded street, every collapsed hillside, and every displaced family carry the same message: preparing before disaster is less costly than rebuilding after. Nature has always played by its own rules. Water will always find its way. Rivers will always seek their floodplains. Hills will always become unstable when forests disappear and slopes are pushed beyond their limits. The real choice is whether we build with nature- or keep building against it. Resilience isn't about rebuilding faster. It's about ensuring there's less to rebuild. As India enters a warmer and more uncertain future, every road, bridge, neighbourhood, and city will reflect the choices we make today. Because tomorrow's resilience is being built long before the next storm arrives. DOCUMENT & DATA STACK  DocumentPurposeIndia Meteorological Department (IMD) Rainfall DataCompare historical and current rainfall intensity.National Disaster Management Authority (NDMA) – Urban Flooding GuidelinesIndia's official recommendations for urban flood management.Geological Survey of India (GSI) – National Landslide Susceptibility MappingExplains why regions like Wayanad remain highly landslide-prone.IPCC Sixth Assessment Report (AR6)Scientific evidence linking climate change to increasing extreme rainfall events.Ministry of Housing & Urban Affairs (MoHUA)Urban drainage and climate-resilient infrastructure guidelines.Central Water Commission (CWC)Flood monitoring and drainage management data.ISRO National Wetland InventoryWetland loss and land-use changes across Indian cities. Key Data Points: TopicData/ObservationRainfall PatternIndia is witnessing more frequent short-duration, high-intensity rainfall events due to climate change.Urban FloodingExisting stormwater drains in many cities were designed using historical rainfall data that no longer reflects today's climate.WayanadHighly vulnerable due to steep slopes, fragile geology and extreme monsoon rainfall.WetlandsShrinking wetlands and encroached floodplains reduce natural flood storage capacity.Climate AdaptationExperts recommend updating Intensity-Duration-Frequency (IDF) curves using present-day climate observations.   ProblemSolutionUrban FloodingRestore wetlandsWaterloggingPermeable pavementsLandslidesAfforestation & slope stabilisationDrain OverflowRegular desilting & drain maintenanceClimate RiskClimate-resilient urban planning   Sources:India Meteorological Department (IMD) National Disaster Management Authority (NDMA) Geological Survey of India (GSI) Central Water Commission (CWC) Ministry of Housing & Urban Affairs (MoHUA) Intergovernmental Panel on Climate Change (IPCC AR6) ISRO National Wetland Inventory The Times of India (base report) ...Read more

19 Jul 2026

Chatbots feel weightless. The infrastructure behind them is anything but Ujjwal K Chowdhury Strapline: Every AI answer that appears instantly on a screen is the visible tip of an invisible supply chain of electricity, water, minerals and hardware — one that is expanding faster than the systems built to measure, let alone restrain, it. The illusion of weightlessness Type a question into a chatbot and the reply arrives in a second or two, apparently out of nowhere. That apparent weightlessness is the single biggest reason today’s mainstream artificial intelligence has drifted into an anti-ecological pattern: the interface hides a resource system as physical as a steel mill, while feeling as immaterial as thought itself.   Behind that reply sits a chain most users never see: a data centre drawing power from a regional grid; racks of accelerators converting electricity into heat; water or refrigerant carrying that heat away; a supply chain of mined minerals and fabricated silicon that had to exist before any of it could run; and, increasingly, an autonomous “agent” that may have quietly called the model dozens of times — planning, retrieving, verifying, retrying — before it ever answered. None of that shows up in the two seconds a user waits for a reply. That gap between visible convenience and invisible cost is where the ecological problem lives. Four ways today’s AI works against the planet 1. It treats electricity as free and infinite The scale is no longer subtle. Global data-centre electricity demand grew about 17% in 2025 — more than five times the growth rate of overall global electricity demand — while AI-specific facilities grew around 50% in the same year, according to the International Energy Agency’s most recent assessment. The agency’s satellite-tracking programme shows dedicated “AI factory” capacity has more than tripled in the past eighteen months alone. Lawrence Berkeley National Laboratory estimates that data centres already consumed 4.4% of all US electricity in 2023, on a path toward as much as 12% by 2028. This is not evenly distributed misfortune. It concentrates in specific places until local grids buckle: Ireland’s data centres now draw over a fifth of the country’s entire electricity supply, with Dublin’s local share pushing toward 80%; parts of Virginia, Arizona and the Netherlands face similar strain. AI accelerator rack density has risen roughly elevenfold since 2020 and could quadruple again within a couple of years, meaning the same floor space now demands vastly more power and cooling than it did five years ago — a physical fact that data-centre neighbourhoods, substations and transmission lines were never designed around. FACT BOX > - Data-centre electricity growth in 2025: ~17% globally (AI-specific: ~50%) > - AI-factory capacity: more than tripled in 18 months (IEA satellite tracking) > - Rack power density: up roughly 11x since 2020 > - Ireland’s data-centre electricity share: over 20% nationally, near 80% in Dublin 2. It treats water as someone else’s problem Cooling AI hardware consumes water directly, and generating the electricity that powers it consumes water indirectly, through the power plants themselves. A peer-reviewed 2025 review found that water use per AI workload can vary by more than 10,000-fold depending on the cooling system, the water intensity of the local grid, climate and utilisation — an enormous range that makes any single “AI uses X litres” headline close to meaningless without context. Earlier modelling had estimated the direct water cost of training a single large language model at roughly 700,000 litres, and projected global AI-related water withdrawal could reach several billion cubic metres by 2027. The ecological offence is not simply the volume; it is where that volume is drawn. Data centres frequently compete for water in the same watersheds as households, farms and ecosystems, often in drought-prone or rapidly urbanising regions. A company can accurately claim it “replenished” water somewhere else in the world while a local community, in the actual basin where the facility sits, faces real seasonal scarcity. Water taken from a stressed basin in July is not made whole by a replenishment project in a different river system entirely. 3. It hides its hardware and mineral footprint The environmental conversation about AI has focused heavily on electricity, but the physical hardware underneath it carries its own anti-ecological weight. Semiconductor fabrication requires high-temperature processing, fluorinated gases and ultrapure water; servers require aluminium, copper, steel and a list of critical minerals mined and processed through globally concentrated, often environmentally and socially fraught supply chains. Because the industry races to deploy ever more capable accelerators, hardware is frequently retired well before the end of its useful life — front-loading manufacturing emissions and generating electronic waste that is notoriously difficult to refurbish because of security requirements and proprietary designs. A narrow focus on data-centre electricity efficiency can therefore simply displace environmental burden upstream, onto mines and fabrication plants far from public view. 4. It multiplies itself through autonomy The newest and fastest-growing anti-ecological pattern comes from agentic AI — systems that plan, browse, write and execute code, call other software, and retry when something fails, often with limited human supervision. A single user request can silently become a “trajectory” of dozens or hundreds of underlying model and tool calls. Early research has found up to a 9.4-fold difference in energy use between agent architectures solving identical software tasks, driven mainly by unproductive loops, redundant multi-agent “debate,” and overly conservative verification steps. A 2026 study proposing an “Energy per Successful Goal” metric found agentic workflows used, on average, more than four times the energy of simpler linear approaches to reach the same outcome. Because these systems can be scheduled to run continuously, across thousands of accounts, with nobody watching each internal step, agentic AI represents autonomy without accountability — precisely the combination ecological management is built to prevent. The underlying design flaw: rebound Underneath all four patterns sits a single structural problem economists have seen before: rebound. Each time AI becomes more efficient per task, that efficiency tends to make AI cheaper and faster to deploy — which drives organisations to use far more of it, not less. A cheaper model attracts more users; a faster agent gets scheduled more often; saved computing capacity gets redirected into training an even larger model. This is a modern instance of the nineteenth-century Jevons paradox, in which more fuel-efficient steam engines led to more coal being burned overall, because efficiency expanded the ways coal could be profitably used. Today’s AI industry is repeating that pattern at digital speed: intensity per task is falling in many cases, even as total electricity, water and hardware consumption keeps climbing. HIGHLIGHT > “Efficient models can lower energy per task but may stimulate more use — a rebound effect analogous to Jevons’ paradox.” Tackling the challenge: what can actually be done The good news, according to researchers working across computer science, engineering and environmental policy, is that anti-ecological AI is a design failure, not a law of physics — and design failures can be corrected. Make lifecycle accounting mandatory, not voluntary. Model developers should be required to publish energy, water and carbon figures covering research, training, fine-tuning and expected inference use — not just the headline training run. Regulatory movement already exists: the European Union’s data-centre reporting rules and the emerging AI Act standards for general-purpose systems are early attempts to make these disclosures routine rather than exceptional. Measure outcomes, not tokens. “Energy per prompt” is a start, but a genuinely useful metric asks how much energy, water and carbon were spent per successfully completed, quality-adjusted task — capturing failed attempts, retries and wasted agent loops rather than rewarding systems that simply generate more text per watt. Treat water as a local-risk issue, not a global volume. Responsible siting means water-stress screening, seasonal operating limits, non-potable cooling sources and transparent, basin-specific disclosure — replacing vague corporate replenishment claims with site-level accountability. Put budgets and brakes on autonomous agents. Concrete engineering controls — hard limits on tool calls and reasoning steps, loop detection, model routing that defaults to the smallest sufficient model, and outcome-aware verification applied only where risk warrants it — can curb the silent multiplication effect that makes agentic AI so much more resource-hungry than a single chatbot exchange. Extend hardware life and close the supply chain loop. Modular, repairable server designs, transparent recycling, redeployment of older accelerators to less demanding tasks, and procurement rules that reward useful work per lifecycle impact — rather than peak benchmark performance alone — would blunt the upstream mineral and manufacturing burden. Impose absolute limits alongside efficiency targets. Because rebound can erase intensity gains, organisations need annual caps on total energy, water and hardware consumption — not only per-task efficiency goals — paired with an honest test of whether any given deployment is actually necessary. Who ends up paying The anti-ecological pattern is not only an environmental story; it is fast becoming a household economics story too. As data-centre campuses draw more power than entire cities, the cost of grid upgrades, new transmission lines and backup capacity has to be paid by someone — and much of it is landing on ordinary electricity ratepayers rather than the companies building the facilities. US utilities requested tens of billions of dollars in rate increases in a single recent year, with retail electricity prices climbing well ahead of inflation, and energy-policy researchers have begun openly asking whether households should be subsidising the power needs of trillion-dollar technology firms. That question — who bears the cost of AI’s physical footprint — is quietly becoming as contentious as the technology’s better-known debates over jobs, bias or misinformation. Communities near proposed data centres are pushing back for similar reasons. Objections increasingly cite not just water and electricity but noise, construction traffic, backup diesel generators and the strain that a single large campus can place on municipal services — concerns that rarely register in a corporate sustainability report measured in global percentages, because the burden is intensely local even when the company’s overall footprint looks modest on paper. Signs the pattern can be broken None of this is inevitable, and there is genuine evidence of course correction. Regulators in the European Union now require structured data-centre energy and water reporting. Some grid operators are experimenting with letting data centres act as flexible loads — absorbing surplus renewable power and throttling back during scarcity — turning a liability into a grid asset if the incentives are designed correctly. Chip-level closed-loop cooling systems, deployed by major cloud operators, are demonstrably cutting water use at the facilities where they have been installed. And a growing number of enterprises are beginning to ask, before deploying any AI feature, whether a smaller model or a simpler workflow could do the job just as well — a habit of restraint that barely existed in the industry two years ago. None of this requires abandoning AI’s genuine benefits — in climate modelling, grid management, disease research and elsewhere. It requires abandoning the pretence that those benefits are free. The technology that feels weightless on a screen is, underneath, one of the most physically demanding infrastructure projects humanity has ever built at speed. Recognising that is the first step toward building it responsibly rather than merely quickly. ...Read more

19 Jul 2026

Two philosophies are fighting over how artificial intelligence should be built — one chases scale at any cost, the other asks what that cost actually is Ujjwal K Chowdhury Strapline: For a decade, AI research had one scoreboard: accuracy. A new one is forcing its way onto the field — energy, water, carbon and hardware. The contest between “Red AI” and “Green AI” is no longer academic; it is shaping how the world’s most powerful technology gets built. The paper that named the problem In 2020, a small group of computer scientists — Roy Schwartz, Jesse Dodge, Noah A. Smith and Oren Etzioni — published a short, blunt paper in the Communications of the ACM with a title that stuck: “Green AI.” It drew a line through the field. On one side sat what the authors called Red AI: research that chases state-of-the-art results by throwing ever more computation at a problem, treating accuracy as the only currency that matters. On the other side stood Green AI: research that treats efficiency — the resources spent per unit of result — as a first-class scientific goal, not an afterthought. The label was provocative on purpose. Red AI was not, the authors were careful to say, morally wrong. It had produced genuine breakthroughs. But it had also quietly normalised an arms race in which each new record-setting model consumed dramatically more compute than the last, with the environmental bill rarely itemised in the paper’s appendix, let alone its abstract. Six years on, that argument reads less like a provocation and more like a prophecy. Generative and agentic AI systems now sit inside search engines, office software, customer service lines and increasingly autonomous workflows that plan, browse, code and retry without a human in the loop. The scoreboard Schwartz and colleagues warned about has expanded from leaderboard rankings to gigawatts, litres and tonnes of carbon dioxide. Two philosophies, one industry Red AI, at its core, is a bet that more computation reliably buys more capability — bigger models, longer training runs, wider search over architectures, more parameters, more data, more reasoning steps at inference time. It is the logic behind scaling laws, and it has worked spectacularly well as a research strategy. But it has a hidden accounting problem: the “winning” run reported in a paper or press release is usually just the tip of an iceberg of failed experiments, architecture searches, ablations and evaluation runs that never make it into the final number. Recent lifecycle research — including a 2025 study led by Jacob Morrison that traced the full environmental cost of building a language-model family — found that model development contributed roughly half of the total training-related impact, not the celebrated final run alone. Green AI, by contrast, asks a different question of every architectural choice, every training run and every product feature: what is the smallest, most efficient way to achieve an acceptable outcome? It treats efficiency — measured in floating-point operations, energy, water and, increasingly, successful outcomes per unit of resource — as an evaluation criterion sitting alongside accuracy, not subordinate to it. Crucially, Green AI has matured past its original, somewhat narrow framing. In 2020 it was largely about training compute. Today, researchers describe it as the quality- and outcome-constrained minimisation of lifecycle environmental impact — a formulation that captures something Red-versus-Green rhetoric can miss: a computationally hungry model is not automatically the villain, and a lean one is not automatically virtuous. A large model solving a genuinely high-value problem in a handful of steps can outperform, environmentally, a small model that fails repeatedly and triggers costly retries. The real dividing line is not model size; it is whether computation is productive. Why the contest matters now The urgency comes from scale. According to the International Energy Agency’s most recent assessment, global data-centre electricity demand rose roughly 17% in 2025 alone — more than five times faster than overall global electricity growth — while electricity consumption specifically tied to AI-focused facilities surged around 50% in the same year. The IEA’s satellite-tracking programme, which watches construction of dedicated “AI factories” from orbit, found that their combined capacity has more than tripled in eighteen months. Data-centre electricity use worldwide, which stood at roughly 415–485 TWh depending on the estimate and year, is on a trajectory toward roughly 950 TWh to beyond 1,000 TWh by 2030 — comparable to the entire annual electricity consumption of Japan.   FAST FACTS > - Global data-centre electricity demand: ~485 TWh in 2025, heading toward ~950 TWh by 2030 (IEA) > - AI-focused data-centre demand: up ~50% in 2025 alone > - US data-centre share of national electricity: 4.4% in 2023, projected 6.7–12% by 2028 (LBNL) > - AI-rack power density: up roughly elevenfold, 2020–2025 (IEA) > - Ireland’s data centres already draw over a fifth of national electricity; Dublin’s local share runs close to 80% This is precisely the terrain Red AI was warned about: growth compounding on growth, with local grids in Ireland, Northern Virginia and parts of the Netherlands already straining, and utilities in the United States requesting billions of dollars in rate increases partly attributable to data-centre load growth. Energy-policy academics have begun asking, pointedly, whether ordinary electricity customers should effectively subsidise the power appetite of trillion-dollar technology companies — a question with no comfortable answer for regulators. Where the two camps actually clash The Red AI/Green AI split is not simply “big model bad, small model good.” It shows up in concrete engineering and business decisions: 1. Model selection. Red-style practice defaults to the most capable, largest available model for every task, regardless of whether the task warrants it. Green practice builds a portfolio: small or domain-specific models for routine work, escalating to frontier models only when complexity demands it. Systems such as FrugalGPT, which learned to route easy queries to cheaper models and reserve expensive ones for hard cases, demonstrated cost reductions of up to 98% on selected benchmarks without materially sacrificing quality. 2. Reporting practice. Red AI habitually reports only the final training run’s cost. Green AI insists on lifecycle transparency — development experimentation, fine-tuning, evaluation, and the electricity, water and embodied-hardware cost of years of subsequent inference, which can dwarf the original training bill many times over. 3. Agentic design. This is the newest and sharpest fault line. An autonomous agent can quietly multiply a single user request into dozens or hundreds of model calls, tool invocations, retries and multi-agent “debates.” Early benchmark research has found up to a 9.4-fold energy difference between agent-framework designs solving the same software-engineering tasks, driven mostly by wasted loops and redundant verification. A 2026 preprint proposing a metric called Energy per Successful Goal (EpG) found that agentic workflows consumed, on average, 4.33 times more energy per completed goal than equivalent linear, non-agentic approaches. Red AI treats agent autonomy as an unqualified upgrade; Green AI treats it as a resource-management problem requiring budgets, loop detection and outcome-based evaluation. 4. The rebound trap. Perhaps the most uncomfortable insight from Green AI research is that efficiency gains alone do not guarantee lower total impact. If a model becomes twice as cheap to run, organisations often respond by running it far more than twice as often — generating more content, running more experiments, automating tasks nobody previously bothered to automate. This is a version of the century-old Jevons paradox, in which efficiency improvements in coal-fired steam engines led, historically, to more coal consumption, not less, because cheaper power expanded its uses. Green AI researchers now argue that intensity metrics (energy per task) must be paired with absolute-impact accounting (total annual energy, water and carbon) precisely to catch this rebound before it erases hard-won efficiency gains. The measurement mess neither side can ignore Part of what makes the Red/Green debate so combustible is that reliable, comparable numbers are still scarce. A landmark 2025 measurement of Google’s production systems found a median energy cost of just 0.24 watt-hours and 0.26 millilitres of water per text prompt — a strikingly small figure. Around the same time, a separate academic benchmark estimated that complex, long-context reasoning queries on certain models could consume more than 33 watt-hours — over a hundred times more. Both figures are credible within their own scope; they simply describe different systems, different tasks and different accounting boundaries. A 2025 peer-reviewed review of data-centre water use went further, finding that water consumption per workload can vary by more than 10,000-fold depending on cooling technology, grid water intensity, climate and utilisation. This is why serious Green AI researchers are wary of single, universal “footprint per query” numbers circulating in the media — they tend to flatten an extraordinarily heterogeneous reality into a misleadingly precise soundbite. The more defensible approach, gaining traction in both research and emerging regulation such as the European Union’s data-centre reporting rules, is a layered hierarchy: from raw activity counts (tokens, model calls), up through compute energy, facility-adjusted energy, environmental impact (carbon and water, adjusted for time and place), full lifecycle impact including embodied hardware emissions, and finally outcome-normalised impact — energy and water per successfully completed task, not per token generated. Not a morality play — a design discipline It would be easy, and wrong, to read Red AI and Green AI as heroes and villains. Some of the most consequential AI applications — climate modelling, grid forecasting, drug discovery, materials science for batteries and solar cells — are legitimately compute-intensive, and restricting them to “small and frugal” would forfeit real value. The IEA itself estimates that mature AI applications could trim energy costs across several industries by 3 to 10 percentage points, and Google has reported enabling tens of millions of tonnes of avoided CO2-equivalent emissions through AI-optimised products in a single year. Green AI’s actual claim is narrower and more rigorous: that value should be measured against lifecycle cost, that claims of benefit require credible counterfactual evidence, and that scale should be earned by demonstrated necessity rather than assumed by default. HIGHLIGHT > “A Green AI system is not simply smaller or faster. It is appropriately capable, transparently measured, powered and cooled responsibly, designed to avoid waste, and deployed where its verified value exceeds its environmental cost.” What comes next Expect the Red/Green fault line to move from academic papers into contracts and regulation. Procurement teams are beginning to demand model-level energy and water disclosures before signing cloud contracts. The EU’s AI Act ecosystem is developing standards for reporting the resource performance of general-purpose AI systems. Enterprises are experimenting with model-routing rules that default to the smallest sufficient model rather than the flashiest one. And a growing chorus of researchers argues that the next frontier metric will not be accuracy, or even energy per token, but energy per successful goal — a number that punishes both wasteful agents and models that fail so often they need constant escalation. The Red AI era was not a mistake; it built the models the world now depends on. But the bill for that approach is now visible in gigawatts, litres and rising electricity tariffs, and it is arriving at a moment when climate constraints leave little room for waste. Green AI’s proposition is simple, if not easy: intelligence, at any scale, should have to justify its keep. Reading the two camps side by side  Red AIGreen AICore metricAccuracy / benchmark scoreQuality-adjusted efficiency (energy, water, carbon per successful task)Model choiceBiggest available, by defaultSmallest sufficient model, escalate only when neededReportingFinal training run onlyFull lifecycle: development, training, inference, hardwareAgentsAutonomy as unqualified upgradeAutonomy as a budgeted, monitored resourceRiskRebound erases efficiency gainsAbsolute-impact caps alongside intensity targets Framed this way, the contest is less a war between two tribes of researchers than a description of a choice every AI-building organisation now has to make, explicitly or by default, every time it ships a feature. The instinctive path — reach for the largest available model, let an agent iterate until it seems to have solved the problem, publish the headline benchmark and move on — is Red AI, whether or not anyone in the room uses the term. The alternative requires more upfront engineering discipline: measuring what a task actually needs, instrumenting the full resource cost, and being willing to report a less flattering number if that is the honest one. Neither side of the debate disputes that AI can create enormous value. The disagreement is about method — whether that value is pursued by default at maximum scale, or earned deliberately at the scale a task actually requires. As electricity bills, water permits and carbon disclosures increasingly follow AI systems out of the lab and into public scrutiny, that distinction is starting to carry real financial and regulatory weight, not just scientific interest. ...Read more

18 Jul 2026

New Delhi, July 18: Environmentalist, education reformer and climate activist Sonam Wangchuk was removed from the protest site at Jantar Mantar early on Saturday morning and taken to VMMC–Safdarjung Hospital as his indefinite hunger strike entered its 21st day. Delhi Police said Wangchuk was shifted to hospital on medical advice after concerns were raised about his deteriorating health. Protest organisers, however, alleged that he was taken away against his wishes. Supporters formed a human chain and attempted to prevent the police from removing him from the site, leading to tense scenes during the early-morning operation. According to the latest available medical update, Wangchuk was conscious, alert and clinically stable, but weak and mildly dehydrated after fasting for nearly three weeks. Doctors reportedly advised continuous observation and monitoring of his electrolyte levels and other vital parameters. His wife and fellow educationist, Gitanjali J. Angmo, questioned the manner in which he was hospitalised. She said that medical intervention should be undertaken only with Wangchuk’s informed consent and in consultation with his family and the doctors who had been monitoring him during the protest. Angmo also reportedly asked that no oral or intravenous medication be administered without proper discussion. She maintained that Wangchuk remained mentally strong and continued his fast while under observation, consuming only water with salt. Protest Against Examination Irregularities The agitation at Jantar Mantar began as a youth- and student-led protest against alleged irregularities, paper leaks and failures in the country’s examination system. Wangchuk joined the movement in solidarity with students affected by repeated examination controversies and began his indefinite hunger strike on June 28. Protesters have demanded an independent investigation into examination-related scandals, greater accountability from the education authorities and structural reforms to restore transparency and credibility to the testing process. The protest has gradually expanded beyond the immediate issue of examination irregularities. It has become a wider movement seeking accountability in education, protection of students’ futures and a more responsive democratic system. Following Wangchuk’s removal, organisers said the agitation would continue and that other participants would carry forward the hunger strike and planned protest programmes. Opposition and Civil Society Extend Support The protest has received support from a wide range of opposition parties, student organisations, farmers’ groups, academics, writers, filmmakers and civil society representatives. Leaders associated with the Congress, Aam Aadmi Party, Samajwadi Party, Trinamool Congress, Shiv Sena (Uddhav Balasaheb Thackeray), Nationalist Congress Party–Sharadchandra Pawar, Communist Party of India (Marxist) and Rashtriya Janata Dal have either visited the protest site, expressed solidarity or criticised the manner in which Wangchuk was removed. Farmer representatives and student activists have also joined the mobilisation. Several civil society figures have appealed to the Union government to open a dialogue with the protesters and address the concerns being raised. Prominent academics, authors, artists and public intellectuals have urged Wangchuk to end his fast, describing his contribution to education, environmental protection and public life as invaluable. Their appeal has also placed responsibility on the government to initiate meaningful negotiations before the situation worsens. A Lifetime Dedicated to Education and Sustainability Wangchuk’s participation in the protest must be viewed in the context of his lifelong engagement with education, ecology and democratic action. In 1988, he co-founded the Students’ Educational and Cultural Movement of Ladakh, widely known as SECMOL. The initiative emerged in response to an education system that was failing many children in Ladakh because it was disconnected from their language, culture, environment and lived realities. SECMOL developed an alternative model of education centred on practical learning, community participation, environmental responsibility and self-reliance. Students are involved in managing the campus, farming, construction, renewable-energy systems and everyday decision-making. The SECMOL campus has become internationally known for its use of solar energy, passive heating, local building materials and sustainable design. It demonstrates how education can be connected with climate responsibility and community life rather than being limited to examinations and classroom instruction. Wangchuk is also known for developing the “ice stupa” concept, an innovative method of storing winter water in the form of artificial glaciers. These structures release water gradually during the spring and early summer months, when farmers in Ladakh face severe water shortages. His work has consistently focused on protecting Ladakh’s fragile high-altitude ecosystem from climate change, unregulated construction, excessive tourism and resource-intensive development. A Gandhian Method of Protest Wangchuk has repeatedly adopted peaceful and non-violent methods to draw attention to public issues. His campaigns have included climate fasts, marches, public appeals, dialogue and community mobilisation. He has also advocated constitutional safeguards for Ladakh, protection of local land and natural resources, greater democratic representation and recognition of the rights of indigenous communities. His hunger strike at Jantar Mantar is therefore consistent with his broader philosophy of public action. It reflects a Gandhian approach in which personal sacrifice, moral persuasion and non-violence are used to awaken public conscience and compel authorities to respond. His removal from the protest site and hospitalisation have intensified the political and public focus on the agitation. The immediate concern now remains his health, even as the larger questions raised by the protesters-about education, accountability, democracy and the right to peaceful dissent—continue to demand answers. ...Read more

17 Jul 2026

The floods exposed more than clogged drains. They revealed a city struggling to keep pace with a changing climate   By Tiyasha Ghosh   Kolkata | July 17, 2026:   Rain isn’t new to Mumbai. So why is the city still caught off guard? Why does it take just a few hours for roads to become rivers? And if we face this every year, why are we still asking the same questions? Because intense rainfall between June 30 and July 6 brought large parts of Mumbai to a standstill and these questions returned once again!Roads were submerged, train services were disrupted, flights were delayed, and thousands of people found themselves stranded as water quickly flooded homes, markets, and streets. Roads were underwater, trains were hit, flights were delayed, and thousands were stranded as water rushed into homes, markets and streets. But this isn’t just about another rainy week! Experts say it’s not just about how much it rains anymore. What matters is how quickly it falls, where it falls, and if the city can cope or not. In many areas, the intensity of rainfall has outpaced what old drainage systems were built for. Mumbai's drainage system was built decades ago based on rainfall patterns that has changed significantly over time. Today, short but extremely heavy cloudbursts dump large volumes of water within hours, overwhelming stormwater drains before they can carry the water away. At the same time, rapid urbanisation has worsened this issue. The wetlands that soaked up rainwater have vanished. Concrete buildings, roads and parking areas have replaced open land. Instead of draining into the soil, rain now flows over hard surfaces and quickly floods low-lying neighbourhoods. Experts additionally identify solid waste as an escalating concern. We dump plastic, construction waste and household trash into drains all year. So, when the monsoon arrives, the clogged drains don’t just carry water away - they spill it right into people’s houses.  The ordinary people suffer the most. Transport shutdown means lost wages for daily workers, missed classes for students, and hours of closure for small businesses. People in informal settlements suffer the most - floodwater gets into houses, damages property, and spreads water-borne illnesses. According to urban planners, Mumbai should shift from tracking only daily rain to measuring rainfall intensity. New drainage systems must be designed for extreme downpours instead of outdated estimates. Experts also say cities should regularly check if drainage capacity matches actual rainfall intensity. These checks can identify problems before the monsoon, not after the roads have already been flooded. Residents and infrastructure experts say that bigger drains alone won’t solve flooding. We also need to restore wetlands, protect rivers and mangroves, improve waste management, add more permeable surfaces, and strengthen disaster planning at the grassroots level. Small steps by residents can make a huge difference. Clearing litter from drains, reporting blockages, avoiding construction debris dumping, and obeying flood advisories will help cut down local flooding. Mumbai just got another wake-up call - climate change means heavier rain. The good news? Not every flood is destiny. Plan better, build stronger, develop smarter, and we can take the hit out of the next storm. We know the rain will come back, hard .The only thing left to ask is: will Mumbai be ready this time? Source:  The Economic Times (9 July 2026)India Meteorological Department (IMD)Brihanmumbai Municipal Corporation (BMC). ...Read more

17 Jul 2026

Massive investments promise clean power, jobs and growth. But can development keep up with nature?   By Tiyasha Ghosh   Kolkata | July 17, 2026:   Can one state reshape India’s sustainable energy roadmap? Can thousands of crores drive both growth and environmental protection? And can the Northeast emerge as India’s next green powerhouse? These questions sit at the heart of Assam’s ₹77,000-crore power investment plan - one of the largest clean energy initiatives announced by any Indian state. From hydropower and solar to batteries, transmission, and conventional plants - the investment is built to turn the Northeast into a powerhouse for India’s energy transition. The moment couldn’t be more critical. India's electricity demand is rising every year.EVs are on the rise. Data centres are booming. ACs are now a household need. With industries growing and cities consuming more, electricity demand has never been higher.  Balancing increasing electricity demand with the goal of coal reduction represents one of India’s most pressing challenges. Assam aims to be a key part of the answer. The proposed projects are designed to boost clean power generation, reinforce the national grid, and enhance energy security.New power highways will carry electricity from the Northeast to the rest of the country. Battery projects will save clean energy now so we can use it when demand spikes.Together, this is about steadier power today and a cleaner future tomorrow.   This goes beyond just funding and megawatts.   The central challenge is ensuring that rapid development does not compromise the unique ecological assets of the Northeast. Hydropower projects typically need dams, and transmission lines often run through forests and ecologically fragile zones. Assam and its neighbouring states have rich biodiversity, large river systems, and wildlife habitats that sustain both nature and local communities.  Environmental experts and civil society groups are therefore urging thorough ecological studies before proceeding with major projects. According to them, protecting forests, rivers, and biodiversity is as important as growing renewable energy. Another key question is whether these investments will actually benefit local communities. While construction brings short-term employment, experts argue that real success requires local industry, a skilled workforce, and sustainable jobs for people throughout the region.If local businesses, engineers, and workers are included in the clean energy supply chain, Assam’s investment could fuel economic growth for decades. According to experts, successful implementation is key to whether the investment works or not.Speeding up approvals isn’t enough. We also need strong transmission networks, modern battery storage, efficient project management, and clear environmental safeguards to work together.Without these basics in place, even big investments could fall short of providing reliable electricity. For ordinary citizens, the outcome matters more than any investment figure. A stronger electricity network can reduce power cuts, improve access to clean energy, create employment and support new industries. Stronger infrastructure may draw business investment to the Northeast and expand economic opportunities there. With India moving faster toward clean energy, Assam is now at a key turning point.This isn’t just about electricity. The ₹77,000-crore bet is on whether clean energy and growth can go hand-in-hand with responsibility.   If we get the balance right, Assam could do much more than power the Northeast - it could help drive India towards a greener and sustainable future!          Source:  The Times of India (10 July 2026) Ministry of New and Renewable Energy (MNRE); Ministry of Power, Government of India. ...Read more

17 Jul 2026

It is not one invention but a discipline — spanning smarter models, honest measurement and hard limits on waste Prof Ujjwal K Chowdhury Strapline: Green AI will not arrive as a single breakthrough. It is being assembled, piece by piece, out of smarter algorithms, redesigned data centres, new accounting rules and a willingness to ask whether a task needed a supercomputer in the first place. Defining the term properly “Green AI” is often used loosely, as a synonym for “AI that feels less wasteful.” Researchers who work in the field define it more precisely: the quality- and outcome-constrained minimisation of the lifecycle environmental impact of an AI system. Every word in that definition is doing work. “Lifecycle” means the accounting cannot stop at a single training run — it must include research and experimentation, data preparation, fine-tuning, round-the-clock inference, agent orchestration, data-centre construction and cooling, electricity generation, and the mining, fabrication and eventual disposal of hardware. “Quality- and outcome-constrained” means Green AI is not simply “use less compute” — a model that saves energy but fails at its task, or that needs constant human correction, has not achieved anything green at all. The term traces to a 2020 paper by Roy Schwartz and colleagues that contrasted this approach with “Red AI” — the pursuit of state-of-the-art accuracy through ever-larger computation, with efficiency treated as an afterthought. Since then the field has broadened well beyond machine-learning theory into data-centre engineering, materials science, distributed systems, water science, economics and public policy. How Green AI actually reduces impact — the toolkit Researchers and engineers now have a genuine, tested toolkit for cutting AI’s resource footprint, operating at every layer of the stack. At the model level. Not every task needs a frontier-scale model. Task-specific and domain models can match performance on narrow jobs at a fraction of the memory and compute cost, and can often run on local devices rather than cloud data centres. Model cascades — systems that route easy requests to small, cheap models and escalate only genuinely difficult ones to larger models — have demonstrated dramatic savings; the research system FrugalGPT showed cost reductions of up to 98% on selected tasks while preserving output quality. Quantisation (reducing the numerical precision of a model’s internal weights) and distillation (training a smaller “student” model from a larger “teacher”) both cut deployment energy substantially, at the cost of some upfront retraining effort. Sparse or mixture-of-experts architectures activate only a fraction of a model’s total parameters for any given input, expanding capacity without proportionally expanding energy use per request. At the inference level. Because generating output token-by-token is often the most expensive part of serving a model, techniques such as speculative decoding (a small draft model proposes text that a larger model merely verifies, rather than generating from scratch), key-value caching (reusing previously computed information instead of recalculating it), and adaptive or “early-exit” reasoning (stopping once a model is confident enough, rather than always running the maximum computation) can cut energy per request without changing the underlying answer. A large 2025 study measuring more than 32,000 configurations across models and GPU hardware found that matching architecture to hardware, and tuning batching and utilisation, mattered as much as model choice itself. At the infrastructure level. Data-centre engineering has moved from generic efficiency metrics toward site-specific redesign: direct-to-chip and immersion liquid cooling, which can cut water use dramatically compared with evaporative systems in the right climate; heat-reuse schemes that feed data-centre waste heat into district heating or industrial processes; battery storage and demand-response systems that let facilities absorb the rapid power swings AI workloads create; and a shift from annual renewable-energy accounting toward genuine hour-by-hour carbon-free electricity matching, which prevents companies from claiming a “clean” annual average while still drawing fossil-heavy power at peak evening hours. At the agent level — the newest frontier. Because autonomous, tool-using agents can silently balloon a single request into dozens of model calls, Green AI research has begun proposing agent-specific controls: hard budgets on the number of steps, tokens and tool calls a workflow may use; automatic loop detection to catch agents stuck repeating unproductive cycles; routing that assigns the smallest capable model to each sub-task, escalating only when genuinely necessary; and outcome-based evaluation using a proposed metric called Energy per Successful Goal, which penalises agent designs that waste computation on retries, redundant multi-agent debate or failed verification.   Selected Efficiency Results FrugalGPT model-cascade routingUp to 98% cost reduction on selected tasksAgent-framework redesignUp to 9.4× difference in energy use for the same completed task (2025 benchmark)Direct chip-level closed-loop coolingOne major operator claims more than 125 million litres of water saved per data centre annually GoogleReports 12 GW of clean energy contracted in 2025 and 78% of freshwater consumption replenished (company-reported)   Measuring what matters — and admitting what we don’t know yet A recurring theme among Green AI researchers is that measurement itself remains immature, and that this is not a minor technical gap but a governance problem. A landmark 2025 study of Google’s production systems reported a median cost of just 0.24 watt-hours and 0.26 millilitres of water per typical text prompt, alongside large year-on-year efficiency gains. A separate academic benchmark, using different models and a different methodology, estimated more than 33 watt-hours for complex, long-context reasoning queries — over one hundred times higher. Both numbers are legitimate; they simply describe different systems and different task complexity, which is exactly the problem: without a shared functional unit and quality threshold, headline “AI footprint” figures cannot be meaningfully compared, and companies can select whichever framing flatters them. In response, researchers have proposed a seven-level hierarchy of Green AI metrics, moving from crude activity counts (tokens, model calls) through compute energy, facility-adjusted energy (including cooling and idle capacity), full environmental accounting (carbon and water adjusted for time, place and water-basin stress), lifecycle impact (including embodied hardware emissions), outcome-normalised impact (per successfully completed task), and finally absolute organisational impact — the only level capable of catching rebound effects that intensity metrics alone miss. Open measurement tools such as CodeCarbon, Carbontracker and the industry-standard MLPerf Power benchmark are improving reproducibility, but researchers caution that no measurement tool can fix a poorly defined functional unit or a missing supply-chain boundary.   The next phase of research should make energy, carbon, water and materials first-class optimisation variables. The next phase of policy should make claims auditable and local impacts visible.   The current state of progress: real, but partial How far has Green AI actually come? The honest answer is: further than five years ago, but nowhere near far enough to offset AI’s raw growth in scale. On the positive side of the ledger: efficiency per individual task is, by most credible measures, improving faster than at almost any point in computing history, driven by better model architectures, smarter serving systems and the techniques described above. Major cloud operators report substantial renewable-energy procurement and water-replenishment programmes, alongside progress on closed-loop and liquid cooling that can cut onsite water use sharply where deployed. Regulatory frameworks are catching up: the European Union now requires structured energy and water reporting from data centres, and the EU AI Act ecosystem is developing standardised resource-reporting rules for general-purpose AI models. Independent benchmarking initiatives, including the AI Energy Score project, are beginning to let outside researchers compare model efficiency on defined tasks rather than relying solely on company claims. On the other side of the ledger: global data-centre electricity demand is still climbing steeply — up roughly 17% in 2025 alone, with AI-specific facilities growing around 50% in the same year — showing that efficiency gains are, so far, being outpaced by sheer volume growth exactly as the rebound-effect research predicted. Agentic AI is expanding faster than the tools built to measure or govern its resource use, and remains, by most researchers’ assessment, in an “early-stage, high research priority” state rather than a solved problem. Water accounting remains inconsistent enough that credible studies report more than a 10,000-fold variation across otherwise comparable workloads — a sign that the industry still lacks agreed, auditable standards. And embodied hardware impact — the minerals, fabrication water and manufacturing emissions locked into every accelerator before it processes a single request — remains the least developed area of lifecycle assessment, largely because supply-chain data is scarce and closely guarded. What a genuinely green deployment looks like Researchers increasingly converge on a simple decision rule for organisations deciding whether to deploy AI at scale: proceed only when four conditions are jointly met. Necessity — the application creates material, demonstrable value. Proportionality — the model and any autonomous agent built around it are no larger or more independent than the task actually requires. Transparency — energy, carbon, water and lifecycle impacts can be measured or credibly estimated, not merely asserted. Net benefit — the quality-adjusted value delivered, socially, economically or environmentally, exceeds the lifecycle cost, with rebound effects actively monitored rather than assumed away. The opportunity for fast-growing, water-stressed economies For countries such as India — with rapid digital growth, hot climates, constrained grids and water-stressed cities — Green AI is not only a defensive necessity but an industrial opening. Policy researchers argue that fast-growing digital economies should avoid simply importing data-centre designs optimised for cooler, water-abundant regions, and instead map proposed AI capacity against transmission constraints, renewable supply and urban water plans from the outset — favouring non-potable cooling sources, dry or hybrid cooling systems and seasonal operating limits rather than defaulting to the energy- and water-intensive designs common in temperate markets. That same constraint creates a market. Efficient small models tuned for Indian languages, energy-aware edge AI that keeps processing on-device rather than in the cloud, low-water cooling technology, power electronics, and auditable sustainability software are all areas where necessity could plausibly drive genuine innovation rather than imitation. Public procurement has real leverage here: governments that require energy and water reporting as a condition of AI contracts can create demand for exactly the transparent, efficient systems Green AI research is trying to build — turning a regulatory requirement into a home-grown industry. The bottom line That rule captures what Green AI has become, seven years after the phrase was coined: not a call to make AI smaller for its own sake, but a discipline for making sure every unit of computation has to earn its keep — a shift from celebrating efficiency in isolation to managing absolute impact in full view. The technology is not yet there. But for the first time, the tools, the metrics and the regulatory appetite to get there all exist simultaneously — which is more than could be said even three years ago. ...Read more