There are moments in life that are too difficult to put into words. Print isn’t enough to hold them. India’s women cricketers winning their ODI semi final against the mighty, seemingly invincible Australians was that moment for me.1 I knew we would win the world cup now. And I knew how much was about to change.
Later, on November 2, 2025, old heartbreaks from 2005 and 2017 had no place on the sofa with me as India walked out in blue, one more time, with one more chance not to be a beautiful tragedy. This time the World Cup was not in distant England or New Zealand; it was in our messy, noisy backyard, under floodlights that bounced off the Arabian Sea, and losing the toss was just par for the course now, not a death knell for dreams.2
Every fan carries a private pantheon. Let’s talk about some of mine: women who did not wait for the world to be ready for them.
Earlier There is Mithali Raj, who spent two decades carrying Indian batting on her back, walking in that day not as a story in progress but as a living archive. She did not give speeches about revolution; she just kept showing up, year after year, with a straight drive that made time slow down: her 409 runs in 2017 stood as India’s World Cup record until Smriti broke it in 2025.3456 In my mind, she is the one who quietly set the table so that others could feast.
There is Jhulan Goswami, the long run‑up that felt like a pilgrimage and the wrist that could still snap a ball past the best batters in the world. Watching her in 201778 was like watching a bridge between eras: one foot in the days when women’s cricket hardly existed on TV, the other in an era she would not fully get to enjoy but had made possible. In a 20-year international career she took a record 255 ODI wickets for India.910
Long ago Shantha Rangaswamy captained India’s first women’s Test side in the 1970s,1112 scoring 613 Test fifties and a hundred while also opening the bowling (21 Test wickets),13 and later became the first woman to receive the Arjuna Award for cricket.11
Diana Edulji learned her craft bowling at boys in Badhwar Park,14 then became India’s slow left‑arm heartbeat for nearly two decades, captaining the side and taking 63 official Test wickets—still the most by any Indian woman—and 46 ODI wickets.15 She also fought equally hard off the field, using her long Railways career and later her role in the BCCI’s Committee of Administrators to push for jobs, contracts, and dignity for women cricketers.161718
There are so many others who have built the spine of women’s cricket in this country vertebrate by vertebrate: Shubhangi Kulkarni, leg‑spinner and administrator, keeping the game alive in committee rooms;19 Sandhya Agarwal20 and Anju Jain,21 scoring in forgotten World Cups; Purnima Rau2223 and Neetu David,24 taking wickets and then quietly building the teams that would come after them; Anjum Chopra, captaining in the lean years and then talking women’s cricket into Indian living rooms.25
Now26272829 But this tournament had others now- Deepti, Smriti, Amanjot, Richa, Shafali, Pratika, Jemima.
If Deepti was the tournament’s quiet star with 215 runs and 22 wickets, Smriti was the metronome- India’s highest run getter with 434 and multiple catches. Both determinedly carrying this country, up the massive Everest of a home world cup.
Shafali made 199 in just the two matches she played, with that 87 in the final… but my favourite Shafali moment has to be how she was grinning already while anticipating Sune Luus’ catch.
Pratika’s 308 runs, the second‑highest tally for India constantly helped us open (hehe) doors into the match, and who knows how many the poor kid may have had if she hadn’t been injured right at the precipice of the Cup itself?
Jemi made 292 runs, including an unbeaten 127 in the semi‑final vs Australia… in many ways she’s the one who won us the tournament. Her self belief through that match, her bravery through the tournament and even in the press conferences, constantly belied by her jolly nature… Perhaps she’s opened another door for us: talking so openly about mental health in cricket, for cricketers.30
235 runs, with 12 sixes, the most by an Indian in the tournament: Richa Ghosh, keeper-bat par excellence.
Amanjot’s World Cup began with crisis. In the opener against Sri Lanka, India slid from 120 for 2 to 124 for 6, and a quiet stadium in Guwahati felt like it was reliving every old nightmare. On debut, she walked in next to Deepti and hit 57 off 56 – her maiden ODI fifty – in a 103‑run stand that yanked India to safety,3132 and later, in the final against SA, Wolvardt’s outrageous, tumbling catch that essentially won us the match and the Cup… you know the one.33 Those are what I remember.
Sneh’s most talked‑about spell came against Pakistan, in a game that could easily have become sticky. She bowled eight overs for just two wickets on paper, but the control was the real story: a chokehold that kept the chase at arm’s length.
Radha had to wait, watching the first six matches from the bench while everyone discussed India’s “settled XI”. When she finally got her chance against Bangladesh, she made it impossible to ignore her again: 3 for 30, plus a brilliant direct‑hit run‑out. That performance is what pushed her name back into the semi‑final conversation and reminded everyone that India’s spin depth now extends all the way to the dugout.
Renuka’s World Cup was all about the early overs. Even when she went wicketless, like against Pakistan and England, she strangled the run rate – 2.9 an over in one match, 4.6 in another – so that chases never got to breathe. Her new‑ball spell against New Zealand, where she combined discipline with two top‑order wickets, set up the very platform from which Smriti and Pratika later tore the game away.
Harleen, who once went viral for that impossible boundary catch in England,34 spent this World Cup doing the unglamorous versions of the same thing – sharp stops in the ring, calm hands on the rope, and those 20‑run cameos in the middle order that stop an innings from fraying.
Arundhati’s spells were often shorter, sharper: two‑ and three‑over bursts in the middle that changed the mood of an innings more than the scorecard, the kind of work you only notice when it’s missing. But her contribution often also came as that player who wasn’t in the XI, and still carried the team’s attitude. So brilliant.
Sree became one of those quiet tournament stories that suddenly erupts into view at the end. The 21‑year‑old left‑arm spinner from Kadapa took 14 wickets across the World Cup, leading India’s spin tally and being welcomed home to Andhra Pradesh like a local folk hero- as she should be.
Kranti’s World Cup became a small‑town fairy tale written in seam. Already known for a 6 for 52 against England earlier in the year,35 she arrived at the tournament as a young quick with raw menace and left it as one of India’s biggest match‑winners. Her 3 for 20 against Pakistan in the group stage, sharing the new ball with Renuka, smashed the chase early and earned her a Player‑of‑the‑Match award that felt like a coming‑of‑age ceremony.
While Yastika’s job this time, was mostly to wait – pads on, gloves ready, rehearsing every scenario in her head in case anything happened to Richa, Uma showed us what the future looks like. A galaxy of stars awaits.
Harmanpreet Kaur went into this tournament as the oldest player in our XI, one day younger than me, and carrying at least ten extra years of history. She had seen 2005 from afar, 2017 from the middle, and every year since then from inside the weight room, the nets, the press conferences where she was asked about the word “chokers” without anyone quite using it. When my girl lifted the trophy, with the Bhangra and her team waiting for her, It’s difficult to explain the joy. Sometimes things can just be felt.
After the world cup, what struck me most was how lightly they wore their victory. No chest-thumping, no proclamations of dominance. Just gratitude, relief, and a deep, unmistakable sense of togetherness. Even Australia, knocked out in the semi‑final absorbed the defeat like a bruise, not a scar. And South Africa, losing the most important match of their life, were still gracious enough to accept hugs.
Women’s cricket, at its best, feels like the game stripped back to its point: the joy of being allowed to play. The records matter. The trophies matter. But they feel like by‑products of something more important: the right to take up space on a cricket field.
So when I call these women immortal, I don’t mean that highlights of Shafali’s 87 or Deepti’s 5 for 39 will live forever on some server farm in Dubai. I mean that a girl somewhere in the tiniest, dustiest, and possibly even the most gender-backward, village possible, balancing a taped tennis ball on her fingers, will one day hear these names and believe that the world will not need to be ready for her either.
In 2013, while conducting research for my Master’s thesis, I met corporate leaders who did not understand why climate change was something businesses were being held responsible for. They were often quite resentful, and yet, nearly all of their organisations had suffered from the Mumbai floods that happened that year- for one of them, a logistics company, the losses were so heavy they planned to shift their warehouses out of the city.
Climate change was viewed as a political issue, even as it was already disrupting operations. However, climate risk is no longer about ethics or disclosure; it is about competitive survival.
A viral picture of the Goldman Sachs building that remained powered and largely unscathed despite being in a mandatory evacuation zone during Hurricane Sandy in 2012.1
The point is not abstract. During Hurricane Sandy in 2012, a widely shared image showed the Goldman Sachs building in lower Manhattan lit and operational while much of the surrounding area was dark. The firm had invested heavily in resilience infrastructure. Business continuity became a competitive advantage.
In a 2015 speech,2 Mark Carney, then Governor of the Bank of England, argued that climate change is a “tragedy of the horizon” because its worst effects will be felt beyond the traditional horizons of business planning, political cycles, monetary policy, and financial regulation. Current decision‑makers therefore have weak incentives to act even though future generations will bear the costs, creating a structural mismatch between where the risks sit and where the power to respond lies.
He highlighted three channels through which climate change threatens financial stability:2
Physical risks: losses from more frequent and severe floods, storms, heatwaves, and other weather‑related disasters.
Liability risks: lawsuits and compensation claims against firms and directors for contributing to or failing to manage climate harms.
Transition risks: repricing of assets as policy, technology, and consumer preferences shift toward a low‑carbon economy, creating “stranded assets,” especially in fossil fuels.
Because standard risk models and planning cycles rarely look out beyond a decade, they miss non‑linear climate shocks and underestimate the scale of structural change required, especially under scenarios that keep warming well below 2°C.34
Climate change is no longer a CSR issue; it is a core strategic, financial, and operational risk56 affecting supply chains, asset location decisions, insurance costs, regulatory exposure, consumer demand, and access to capital.
Breaking the tragedy of the horizon requires extending risk management beyond conventional timeframes and embedding climate risk into today’s decision systems. We are already experiencing climate risk, and there is no way to fully insulate every asset from its effects.
For financial institutions, climate risk shows up as credit risk (borrowers’ ability to repay), market risk (asset price changes), operational risk (disruptions to operations), and reputational risk (backlash over financing high‑emitting activities). Empirical work on banks shows that exposures to transition risk are currently modest in portfolio terms but concentrated in specific sectors, and that banks signing net‑zero alliances have begun to reduce lending to the riskiest industries.78
For corporations, the following may help:
Risk identification: Map climate hazards and drivers (heat, floods, drought, storms, sea‑level rise; carbon prices; regulations; technology shifts) to specific assets, operations, and supply chains.
Assessment and quantification: Use tools ranging from high‑level heatmaps to asset‑level hazard models and financial impact assessments (e.g., revenue at risk, cost of goods sold, capex needs).
Integration into Enterprise Risk Management (ERM): Incorporate climate risks into risk registers, materiality assessments, internal controls, and capital budgeting, with clear thresholds for escalation.
For financial institutions, more technical steps include:
Exposure mapping: Quantify portfolio exposure to vulnerable sectors and geographies as a share of lending and investment books.
Climate-adjusted credit analysis: Incorporate emissions intensity, transition plans, and physical risk exposure into underwriting and pricing.
Scenario stress testing: Use Network for Greening the Financial System (NGFS) or equivalent scenarios to assess losses under combinations of policy tightening and physical shocks.
Regulators increasingly expect banks and insurers to demonstrate that climate risks are integrated into their internal capital adequacy assessments, risk appetite statements, and supervisory dialogues.9
For banks and investors, an important nuance is that reducing portfolio emissions too mechanically by divesting from high‑emitting sectors can undermine real‑economy transition, because those same sectors (power, steel, transport) require capital to decarbonise. Leading practice therefore shifts from simple “brown exclusion” to engagement, conditional finance, and transition‑linked instruments.1011
All of this reframes climate change from a distant macro-risk into an immediate business continuity problem. The question is no longer whether climate risk matters, but how organisations operationalise it within decisions made today. Businesses and financial institutions must change how they allocate capital and design products. Climate‑aligned finance involves both reducing exposure to misaligned activities and growing exposure to solutions.12
For non‑financial corporates:
Shift capex toward energy efficiency, low‑carbon technologies, and resilience measures (e.g., relocating assets, flood‑proofing, cooling infrastructure), guided by scenario‑tested business cases.
Integrate internal carbon pricing into investment decisions and product design to reflect transition risk and incentivise low‑carbon choices.
Explore innovative risk‑sharing instruments, such as parametric insurance for climate‑related losses or resilience bonds linked to infrastructure upgrades.
For financial institutions:
Develop green and sustainability‑linked products (green bonds, sustainability‑linked loans, transition bonds) with clear use‑of‑proceeds criteria and performance‑based pricing.
Use portfolio alignment tools (e.g., implied temperature rise metrics, sectoral pathways) to steer lending and investment toward net‑zero‑compatible activities, while monitoring credit risk.
Avoid “paper decarbonisation” that simply sells high‑emitting assets to less regulated owners; instead, engage with clients to finance credible transition plans and set conditions for continued support.
Research shows that, so far, banks’ transitions have been gradual and often focus more on emissions metrics than on real‑economy outcomes, underscoring the need to link commitments to enforceable policies and incentives.
To translate this into an actionable agenda, organisations can focus on a staged approach:
Diagnose and govern: Brief boards on climate risk exposure. Assign clear oversight at board and executive levels.
Measure and disclose: Strengthen scenario analysis, emissions tracking, and exposure metrics. Build data systems aligned with emerging standards.
Integrate into risk and strategy: Embed climate considerations into ERM, capital budgeting, procurement, and sector strategies.
Align capital and incentives: Set science-based targets with interim milestones. Adjust lending and investment policies to phase out clearly misaligned activities while scaling transition and resilience finance.
Engage and collaborate: Work with regulators, alliances, clients, and suppliers to raise standards and avoid a race to the bottom.
Traditional business continuity frameworks assume that shocks are temporary, insurable, and geographically contained. Climate risk increasingly violates all three assumptions. The tragedy of the horizon is therefore not just about time, but about governance. Climate risks accumulate slowly, crystallise suddenly, and cascade across balance sheets, supply chains, and communities. By the time they appear in backward-looking metrics, strategic options have already narrowed.
For corporations and financial institutions alike, the challenge is no longer one of awareness or disclosure. It is whether decision-making systems — capital allocation, product design, credit assessment, and continuity planning — can be rewired to operate under conditions of deep uncertainty and irreversible change. Those that succeed will not eliminate climate risk (that’s impossible). They will internalise it early, adapt faster, and preserve optionality as the transition unfolds. Those that do not may find themselves where many firms were in the early 2010s—surprised by risks that were already visible, and outperformed by competitors who prepared earlier.
I’ve worked for a couple of projects on e-waste and e-waste recycling, and I wanted to revise that and see what’s going on in the space, so here is a series of posts about these topics.
In 2022, the world generated 62 million tonnes of electronic waste. Only 22.3% of that waste was properly recycled. By 2030, we’re on track to hit 82 million tonnes annually—while our recycling rate is projected to drop to 20%.12 The gap between what we’re throwing away and what we’re recovering isn’t just an environmental problem. It’s an economic disaster not even bothering to hide, and yet few pay attention. That 62 million tonnes of waste contains an estimated $62 billion worth of recoverable materials—gold, silver, copper, rare earth metals—currently rotting in landfills or being processed in unsafe conditions.2
EEE E-waste, according to the European Union’s WEEE (Waste Electrical and Electronic Equipment) Directive, is “equipment which is dependent on electric currents or electromagnetic fields in order to work properly”.3 India’s E-Waste Management Rules 2022 define it as “electrical and electronic equipment, whole or in part discarded as waste by the consumer or bulk consumer as well as rejects from manufacturing, refurbishment and repair processes”.4 The US Environmental Protection Agency divides e-waste into ten broad categories:5
Large household appliances: refrigerators, air conditioners, washing machines
Small household appliances: toasters, coffee makers, vacuum cleaners
IT equipment: computers, laptops, monitors, printers
Lamps and luminaires: LED bulbs, fluorescent tubes
Toys: electronic games, remote-controlled cars
Tools: power drills, electric saws
Medical devices: blood pressure monitors, glucose meters
Monitoring and control instruments: thermostats, smoke detectors
Automatic dispensers: vending machines, ATMs
And critically, this includes batteries of all types:6
Alkaline and zinc-carbon batteries: the everyday AA, AAA batteries we use in remotes and toys
Lithium-Ion batteries (Li-ion): found in smartphones, laptops, electric vehicles—these have high energy density and long life, but are highly reactive and flammable
Lead-acid batteries: used in vehicles and industrial applications—low cost but heavy and toxic
Nickel-cadmium batteries (NiCd): known for consistent performance but containing toxic cadmium
Why should we recycle e-waste? Why not? Electronics contain both valuable materials and dangerous ones, and throwing them away is economically silly and environmentally irresponsible. For one, recovering gold produces 80% less carbon emissions than primary mining.7 Recycling lithium-ion batteries instead of mining new metals reduces greenhouse gas emissions by 58-81%, water use by 72-88%, and energy consumption by 77-89%.8910 If we extend the lifespan of existing devices—through repair, reuse, and high-quality refurbishment—we drastically reduce the need to manufacture new ones.
Hazard Electronic devices are chemical cocktails. Circuit boards, batteries, and screens contain an array of hazardous substances:111213
Lead: damages the nervous system, kidneys, and reproductive system. Particularly harmful to children’s developing brains. Found in cathode ray tubes (those old bulky TVs and monitors) and soldering materials.
Mercury: a potent neurotoxin that accumulates in the body, causing neurological and developmental issues. Present in flat-screen displays, fluorescent lamps, and some batteries.
Cadmium: linked to kidney damage, lung cancer, and bone disease. Found in rechargeable NiCd batteries, old CRT screens, and printer drums.
Chromium (specifically hexavalent chromium): a recognized carcinogen that can cause lung cancer, respiratory issues, and skin irritation. Extremely soluble, so it easily contaminates groundwater.
Brominated flame retardants: used in plastic components to prevent fires, but they release toxic dioxins when burned or heated. These cause hormonal disorders.
Beryllium: found in power supply boxes. Exposure can cause chronic lung disease.
The World Health Organization has identified e-waste as one of the fastest-growing solid waste streams posing serious health risks.14 When e-waste is dumped in landfills, these toxic materials leach into soil and groundwater. When it’s burned—as happens in much of the informal recycling sector—they’re released into the air as poisonous gases. Studies in communities near informal e-waste recycling sites show elevated rates of respiratory illnesses, cardiovascular problems, neurological disorders, and cancers. Children and pregnant women are particularly vulnerable.1516
Urban Mining Electronics are concentrated sources of valuable materials—far more concentrated than their natural ore deposits:171819
Gold: one tonne of circuit boards contains approximately 350 grams of gold. To put that in perspective, the gold content in circuit boards is 800 times higher than in natural gold ore. Mining one tonne of gold ore might yield just 5 grams of gold; circuit boards yield 350 grams.
Silver: that same tonne contains about 2 kilograms of silver.
Copper: 120 kilograms per tonne of circuit boards.
Other precious metals: aluminum, platinum, cobalt, palladium, rare earth elements.
To make this concrete: recycling one million cell phones can yield approximately 35,000 pounds of copper, 772 pounds of silver, and 75 pounds of gold. The total value of recoverable materials from global e-waste in 2022 was estimated at $62 billion.19This is what researchers call “urban mining”—recovering valuable materials from discarded electronics rather than extracting them from the earth.20
If e-waste is valuable, dangerous, and growing, why is it still handled so badly? The answer isn’t technology or awareness. It’s incentives—and the policy instrument meant to fix this problem may be quietly making it worse. In the next post, I’ll unpack EPR (Extended Producer Responsibility) — the policy tool we’ve pinned our hopes on, and why it’s not delivering what it promises yet.
Why did Ishan Kishan come out swinging at 6/2 chasing 209 instead of playing it safe? Why did Pat Cummins bowl first in the 2023 World Cup final despite everyone expecting him to bat? Why did Harmanpreet Kaur throw the ball to part-time bowler Shafali Verma in the 2025 Women’s World Cup final when India desperately needed wickets?
These aren’t random decisions. They follow patterns that psychologists and economists have studied for decades. Three frameworks help us understand these three cricket choices:
Expected Utility Theory – How perfectly rational people should make decisions (decision making for robots)
Prospect Theory – How people actually make decisions when facing risk, or when they feel like they are winning or losing
Behavioral Economics – The mental shortcuts and biases that affect our choices
Expected Utility Theory1 Expected Utility Theory assumes people make decisions by calculating the average outcome of their choices. They think about the all the possible outcomes, try to understand how likely each outcome is, and how much they would like or dislike it if any of these outcomes happened. Then pick the option where this calculation works out best.
Expected Utility Theory assumes three things:
People can calculate probabilities accurately
They will pick the option with the best average outcome
They make decisions based on pure logic, not emotions
This theory is useful because it gives us a standard for what “rational” decision-making looks like. It’s like the baseline or the “correct answer” against which we can compare real human behavior.
But here’s the problem: people don’t actually follow this framework, because we are not always rational beings.
Prospect Theory2 Developed by Nobel Prize-winning psychologists Daniel Kahneman and Amos Tversky,3 Prospect Theory says that people behave in predictable but “irrational” ways. The central insight of the theory is that Losses hurt about twice as much as equivalent gains feel good,4 and that outcomes are evaluated based on the current position of the person evaluating them- not on absolute values of satisfaction.
Here are two examples:
Scenario 1: Gain Frame
Option A: You’re guaranteed to get $450
Option B: Flip a coin—50% chance you get $1,000, 50% chance you get nothing
Expected Utility Theory says: Both options have the same expected value ($500- the value you would get on average if the coin is flipped many times), so you should be indifferent.
But Prospect Theory predicts: Most people choose Option A (the guaranteed $450). Why? Because the certainty of a gain feels good, even if it’s smaller.
Scenario 2: Loss Frame
Option A: You’re guaranteed to lose $450
Option B: Flip a coin—50% chance you lose $1,000, 50% chance you lose nothing
Expected Utility Theory says: Both have the same average loss, so again you should be indifferent.
But Prospect Theory predicts: Most people choose Option B (the coin flip). Why? Because they’ll take a gamble to avoid a certain loss. The possibility of losing nothing appeals to them.
Behavioral Economics5 While Expected Utility Theory focuses on rationality and Prospect Theory focuses on how we evaluate gains vs. losses, Behavioral Economics is the broader field studying all the ways our brains take shortcuts that lead us astray. It’s the study of cognitive biases.
Anchoring Bias: We get too attached to the first piece of information we hear, even if it’s wrong or irrelevant.
Status Quo Bias: We prefer to keep things as they are, even if alternatives are better (“We’ve Always Done It This Way”).
Confirmation Bias: We seek out information that confirms what we already believe, and ignore contradictory evidence.
Availability heuristic: Overweighting recent memorable incidents while discounting regular events. A heuristic is a mental short cut, like a rule of thumb. For example, my dad just wears whatever my mom takes out for him to wear. If he has to make a decision, his heuristic is to wear whatever is at the top of the pile of clothes in his cupboard.
Recency Bias: We overweight recent events when making decisions, ignoring longer-term patterns.
Sunk Cost Bias: We make decisions based on money we’ve already spent, even though that money is gone and shouldn’t affect future decisions.
These biases often work together to distort decisions:
Anchoring + Confirmation bias = You anchor on an initial belief, then only see evidence confirming it
Recency bias + Availability heuristic = Recent vivid events feel more common than they are
Status quo bias + Sunk cost bias = You stick with current choices because of what you’ve already invested, even if better alternatives exist
Kishan7 Now back to cricket. Ishan Kishan walked in and launched an all-out assault—76 runs off just 32 balls at a strike rate of 237.5. He reached his fifty in 21 balls, the fastest by any Indian against New Zealand. Together with Suryakumar Yadav, he added 122 runs in just 49 balls. India won with 28 balls remaining.
Captain Suryakumar later said: “I’ve never seen anyone bat at 6/2 in that manner and still end the powerplay around 67 or 70”.8
From a pure Expected Utility perspective, when chasing very high totals in T20 cricket, the mathematics often favor immediate aggression because conservative batting creates an impossible required run rate in later overs.9Studies using dynamic programming and, more recently, advanced machine learning techniques to analyse Twenty20 (T20) cricket suggest that, when facing high targets, chasing teams are often more successful when they adopt an aggressive approach from the beginning, which inherently requires accepting elevated risk.10
In Prospect Theory terms:
Reference point: The current losing position (6/2, massive target)
Frame: Loss domain (already behind, likely heading toward defeat)
Predicted behavior: Risk-seeking to escape the loss domain
Research on sports shows11 that athletes in trailing positions consistently take more risks: higher shot volumes in basketball, more aggressive substitutions in football, elevated foul rates. Trailing teams recognise that maintaining the status quo (playing safe) guarantees defeat, so they escalate risk dramatically.
Kishan’s aggressive batting aligns perfectly with Prospect Theory’s prediction: when facing almost certain defeat through conventional cricket, players become willing to take massive risks for a chance at victory. The post-match quote captures this psychology: “I asked myself, can I do it again? I had a very clear answer”.8 This suggests Kishan mentally framed the situation as an opportunity (a chance to produce something extraordinary) rather than a threat (protecting his wicket).
The partnership transformed what looked like a losing position into a comfortable victory. India reached the target with 28 balls to spare. Kishan’s risk-seeking behavior in a loss frame achieved precisely what conservative cricket might not have done—a pathway to victory from an apparently losing position.
Cummins12 In the 2023 CWC final, Pat Cummins won the toss and chose to field. Conventional wisdom… indeed old Australian wisdom certainly suggested batting first and setting a target,13 but against an unbeaten India playing at home, his instincts were unfortunately proven correct (Cummins admitted he was “unsure right until the toss”14).
Cummins articulated this logic: “Not getting it right with the bat first would be fatal in a way not doing so with the ball wouldn’t”.14 This is sophisticated risk assessment—recognising that different choices carry different consequences even if probabilities are similar. Besides, research on toss decisions shows that in modern ODI cricket, there’s no consistent advantage to batting first.15The decision was called “one of the bravest in Australian sport history”, because if it failed, criticism would be merciless.16 The “safe” choice (bat first) protects reputation even if suboptimal. Cummins accepted the reputational risk to make what he calculated as the statistically better decision. Rare leadership.
Abhishek Sharma, India’s incandescent T20 opener later spoke with his IPL team mate Travis Head to understand Head’s mindset during Australia’s chase. Abhishek says Head told him, “when I asked him about his mindset in the World Cup, he told me that we only had the batter’s meeting. And in the batter’s meeting, we only thought about how to make 400 today”.17
Now think from an Indian batter’s perspective. The pressure of playing a home world cup final in front of thousands of fans vociferously supporting your team… I would have thought it would let them express themselves openly, but the opposite happened.
Why did the pressure of a home World Cup final constrain Indian batters instead of liberating them? The answer might sit at the intersection of Prospect Theory, loss aversion, and reputational risk.
Prospect Theory tells us that people in a gain frame become risk-averse. After winning every match before the final and spreading true joy through the nation, every wicket that fell in the final may have felt like a loss from a guaranteed future, not a normal match event. Loss aversion might have kicked in hard here: the pain of being the one who throws it away may have felt far greater than the joy of being the hero. This is textbook loss aversion: the psychological weight of potential failure exceeded the psychological reward of potential glory.
So Indian batters subconsciously optimised for:
Minimising blame
Preserving wickets
Maintaining respectability
Not maximising runs.
Contrast this with Ishan Kishan whacking the skin off the cricket ball earlier this week… the contrast is clear, isn’t it? Note here that Kishan had earlier been dropped and treated poorly by the BCCI after making a double hundred,18 plus he had failed in the previous match. He still backed himself and chose the (objectively) riskiest option.
Elite cricket decisions are clearly less about skill or courage and more about how players psychologically locate themselves on the gain–loss spectrum. In all three moments—Kishan’s assault, Cummins’ toss call, and India’s batting freeze—the decisive factor wasn’t talent or tactics, but where each decision-maker placed their psychological reference point. None of these decisions become correct because they succeeded or failed. They become understandable because the theory predicts them before the outcome is known. Human beings behave differently under different frames—and elite sport amplifies those tendencies.
Kaur And now to something joyful. Remember when Harmanpreet Kaur threw the ball in the final to Shafali Verma?19 Me too! Shafali is a specialist batter who had bowled only 14 overs in 30 ODIs with just 1 wicket.20 Shafali took 2 wickets in her first over (Sune Luus caught and bowled, Marizanne Kapp).19
From a rational Expected Utility perspective, Harmanpreet’s decision seems questionable. Pure EUT would favour specialist bowlers with known probabilities and track records over using an untested part-timer who could get whacked for a 30 run over on a bad day. But Shafali was having a good day, and Harman trusted that. Shafali’s ongoing frame of mind was of confidence. and Prospect Theory says people evaluate their options based on their current position. Shafali also represented an unexpected variation that South African batters hadn’t prepared for.
Harman successfully overcame several behavioural biases to toss the ball to Shafali that night:
Status Quo Bias Overcome: The “safe” choice was continuing with regular bowlers—what teams typically do. Harman broke this pattern. Research shows captains typically exhibit strong status quo bias, especially in high-pressure situations. Harman went against this natural tendency.
Sunk Cost Fallacy Avoided: Teams often persist with established bowlers because they’re “supposed to be” the specialists—they’ve been selected for this role, practiced extensively, etc. Harman didn’t fall into this trap. The fact that Shafali wasn’t a specialist shouldn’t matter if the situation calls for something different.
Availability Heuristic Countered: The most “available” option mentally was the regular bowlers—they’re the specialists, they’ve bowled throughout the match. But Harman looked beyond the obvious choice.
She later explained, “When Laura and Sune were batting, they were looking really good, and I just saw Shafali standing there. The way she was batting today, I knew today’s her day. She was doing something special today, and I just thought I have to go with my gut feeling”.20 This represents what researchers call “recognition-primed decision making”—experienced decision-makers recognising patterns and trusting intuition developed through years of experience.21 MS Dhoni’s captaincy showed similar intuitive leaps: giving the last over to Joginder Sharma in the 2007 T20 World Cup final, promoting himself ahead of Yuvraj in 2011.22 Neither Kaur nor MS South African captain Laura Wolvaardt later admitted: “Shafali’s bowling was the surprise factor, frustrating that we didn’t expect it”.23
In all,
Ishan was risk-seeking because he perceived himself in a loss frame.
Indian batters became risk-averse because they perceived themselves in a gain frame.
Cummins accepted reputational risk to avoid catastrophic match risk.
Harman overrode status quo bias by compressing experience into instinct.
Ultimately, none of these choices were brave because they succeeded; they were brave because they resisted the gravitational pull of risk aversion, reputation, and habit. Under pressure, cricket strips decision-making down to its psychological core: how afraid are you? Elite sport doesn’t reward those who merely minimise mistakes. It rewards those who understand when the cost of caution is greater than the cost of failure — and who are willing to act accordingly. The moments we celebrate are not triumphs of bravery so much as triumphs over instinct—reminders that greatness often lives in decisions that feel unsafe.
On a visit to Singapore, after I gushed to him about the beautiful, and very large, museum I had just visited, my cab driver—a man without any personal connection to cricket—said something touching and thoughtful: cricket seemed like a “pan-Indian language.” He’d watched Indian migrant workers playing the sport in parks and public spaces, and felt it was “a way for the Indian workers to keep up their connection with the homeland.”
Academic research confirms what the cab driver observed. Sport provides “diasporic communities with a powerful means for creating transnational ties” while shaping “ideas of their ethnic and racial identities.” Cricket becomes “a significant medium through which local experiences are translated, diasporic parameters reconfigured and national identity complicated.”1
In growth economics, Robert Solow’s work separates economic growth into two parts: what can be explained by adding more labour and capital, and what cannot. The unexplained part is the Solow residual – the contribution of technology, organisation and ideas once more workers and more machines have been accounted for.23
In economics:
Labour is the work people do, and
Capital means all the tools and equipment people use to do the work
Applied to cricket, the analogy is:
Labour: players, coaches, umpires, support staff, administrators.
Capital: stadiums, training facilities, broadcast infrastructure, league investments, media rights.
Cricket’s global rise can be thought of in the same language. There is a visible story of more players, more matches, more money and more stadiums. Alongside these are formats, platforms, new audiences and institutions that are helping the game grow, beyond what labour and capital alone would predict.
This is cricket’s Solow growth story.
The Solow lens: growth beyond inputs In a standard growth-accounting framework, output Y depends on capital K, labour L and a technology term A. When economists measure growth over time, they first calculate how much extra output comes from increases in K and L. The remainder of growth – the part not explained by these inputs – is attributed to changes in A, the Solow residual. It represents effects like better technology, improved organisation and more efficient processes.
Cricket’s output may be thought of in terms of:
Fans and viewership
Match attendance
Revenue and commercial value
Participation and playing nations
Once the contribution of labour and capital is recognised, there is still a large “something else” driving growth: formats, digital reach, women’s cricket, new markets, governance changes and cultural dynamics.
The visible inputs: labour and capital in cricket Over the last two decades, more players, support staff, and officials have been able to treat cricket as full-time work.45 On the capital side, investment has risen sharply.67 More labour and more capital would, on their own, be expected to expand cricket’s footprint. However, the scale and pattern of growth indicate that additional forces are at work.
Residual drivers In Solow’s terms, technology is not just gadgets; it is a better way of combining labour and capital to produce more output. In cricket, “technology” may be read broadly: formats, platforms, governance models and cultural transmission.23
Formats here function like a productivity-enhancing technology, since T20s allow the same talent pool and stadium infrastructure to generate more matches, more broadcast hours and more global attention per season than longer formats alone can do.
The second residual driver is how cricket uses digital platforms to reach and retain fans. Digital reach allows cricket to penetrate markets where linear television had limited presence, to offer short-form content to casual viewers, and to collect granular data on fan behaviour.8910
A major structural shift in cricket’s growth story is the rise of women’s cricket, which expands both the playing base and the fanbase. This is more than an incremental increase in labour. Incorporating women fully into the professional game changes the scale and diversity of talent, opens new commercial categories and attracts new audiences.11121314
Cricket’s growth is also being shaped by its spread into new geographies, particularly through structured leagues and global events.1516
Global tournaments amplify this effect:
ICC has expanded the number of teams in men’s and women’s T20 World Cups and increased the frequency of global events, providing more nations with regular exposure on major platforms.17
Cricket’s inclusion in the Los Angeles 2028 Olympics was confirmed in 2023, with a six-team T20 competition for both men and women approved by the International Olympic Committee.15
Olympic participation is expected to support recognition and funding for cricket in national sports systems that previously gave it little priority, especially in the Americas, Europe and parts of Asia.18
Together, these developments act like opening new export markets in economic growth: the same product – cricket – now reaches more consumers in more countries.
A further contributor to cricket’s Solow-style residual is how the game is organised and governed. The franchise model aligns investors, broadcasters and local boards around shared incentives.1920 Long-term agreements for leagues like the IPL, WPL, PSL, BBL, SA20, ILT20 and MLC encourage sustained investment in academies, scouting and marketing.21
ICC’s governance and commercial approach has also evolved towards a portfolio of global events, with structured revenue-sharing mechanisms and clear qualification pathways, rather than relying solely on bilateral (and my much-missed trilateral) series for income and exposure.2223Separate and more tailored media-rights packages for different regions and for men’s and women’s events reflect more sophisticated commercial design, allowing cricket’s governing bodies to capture greater value from diverse markets.242526
As with economic growth, productivity gains in cricket are not evenly distributed. Franchise leagues and global events concentrate revenues and influence among a small set of boards and investors, widening the gap between cricket’s core and its periphery.27282930At the same time, calendar congestion reflects a classic growth constraint: more formats and competitions compete for the same finite player time, increasing injury risk and diluting bilateral cricket.3132 Rising output, in short, comes with coordination costs—and not all participants share equally in the gains.
No economist can measure the value of a game that allows a migrant worker to feel briefly at home. But whatever that value is, it compounds enough that the cab driver without any links to cricket was able to feel for it. In economic terms, cricket’s “A” – its equivalent of total factor productivity – is rising. The story of the sport’s future will depend not only on how many people play and how much money flows in, but on how effectively formats, institutions and cultures continue to convert those inputs into sustainable, global growth.
This is not a regular post, just me musing out aloud here. AI is economically disruptive not because it is intelligent, but because it behaves unlike anything our existing factors of production were designed to describe.
Economics does not have a formal checklist for what qualifies as a factor of production, but there is a recognisable pattern. A factor tends to be:123
A necessary input to production (you can’t produce at scale without some of it)
Scarce relative to demand (so it commands a price and has an opportunity cost)
Distinct enough that tracking its quantity and return separately actually improves our understanding of the economy
This is how we ended up with land, labour, capital, and entrepreneurship.
FoPs also have their own characteristic of return:4
S. No.
Factor
Return
1.
Land
Rent
2.
Labour
Wages
3.
Capital
Interest
4.
Entrepreneurship
Profit
5.
Artificial Intelligence (?)
Data/ Information (?)
What stands out immediately is that all traditional returns are monetary, because economics measures factor rewards in money terms. A person lifting a bag and moving it somewhere else is not “producing money”; they are supplying labour that is then valued in money. At the moment we don’t have anything like a standardised, broad market that prices raw data or AI outputs in the same way. AI primarily produces streams of information—predictions, classifications, strategies, compressed knowledge. Money appears later, once those outputs are embedded into decisions and products.
Another difference is agency. All existing factors require humans to operate them. AI operates within parameters set by humans, and will likely continue to do so for the foreseeable future. But within those parameters, it can often act independently—choosing, ranking, deciding. That alone makes it behave differently from land, machines, or even software in the traditional sense.
A factor of production isn’t just a philosophical label. It exists to help us explain and measure the economy. If adding a factor doesn’t improve growth accounting, policy design, or business strategy, economists won’t bother. This is why some researchers talk about “digital labour” or “machine intelligence”: not because they want new categories, but because too much productivity is currently being buried in the Solow residual—the box labeled “we don’t quite know where this came from.”
AI clearly enhances human productivity. That part isn’t controversial. In that sense, today’s AI can reasonably be described as technology—a powerful one, but still technology. It processes information created by humans and executes objectives defined by humans. Like other technologies, it raises output.
But AI also does something no previous technology has done at this scale. It automates parts of cognition itself. Even if it is only rearranging human-made information, no human can do so at its speed, breadth, or consistency. This is where the analogy with ordinary technology starts to strain.
If AI were simply capital, it would behave like other capital goods. It doesn’t. If it were just labour-saving technology, it would enhance labour without resembling it. It increasingly does resemble labour—except non-human, infinitely replicable, and made rather than born.
This is why I’m inclined to think AI may become a factor of production—not because it is “intelligent” in a human sense, but because it fits awkwardly into every existing category. I’m wondering if, when something doesn’t fit any of the existing buckets cleanly, maybe it deserves its own bucket. For now, AI probably still sits closest to technology: a tool that dramatically enhances output. But it is an unusual tool—one that changes the production function itself by substituting for certain kinds of cognition while amplifying others.
My next thought was what would happen if we did recognise AI as a separate factor. No country’s GDP would suddenly change; what would change is how we explain and decompose that GDP.
Production approach: sum of value added = output − intermediate inputs
Expenditure approach: C + I + G + (X − M)
Income approach: sum of factor incomes (wages, profits, interest, rent) plus taxes less subsidies
All three are about the value of final goods and services produced in a period, not about how many “factors” are in the textbook. So just declaring “AI is now a factor” would not suddenly make India’s or any country’s GDP number jump.
In growth economics, output of an economy is often represented as a function of two primary, measurable inputs:78
Labour
Capital
A standard production function can be written as:
Y = F(K, L, A)
where Y is income, K is capital, L is labour, and A is a catch‑all “technology” term—the Solow residual. If AI or “digital labour” became a recognised factor, you’d move to something like:
Y = F(K, L, A, D)
where D is an explicit AI/digital labour input, and A remains the residual technology that is not AI.
That doesn’t change the level of Y we measure as GDP, but it does change the story of where Y came from: part of what is now “mystery productivity” (TFP/Solow residual) would be reassigned to a measured AI input. In other words, the pie stays the same size, but we start saying, more precisely, which ingredient did how much of the baking.
This is a quick post explaining the various common types of green finance mechanisms.
Financial Instruments123456 Before getting into specific instruments, it helps to see that every financial mechanism, at its core, answers the same small set of questions. Whether it is a bond, a guarantee, a carbon credit, or a crowdfunding campaign, the structure is really a way of formalising: who puts money in, who gets money out, under what conditions, over what time horizon, and with what risks attached.
The first design step is to be clear about purpose and users. A mechanism should specify: Who is this for? Is it aimed at sovereigns, cities, large corporates, project developers, households, or small farmers? And what is it trying to achieve—cheap long‑term capital for infrastructure, early‑stage risk capital for new technology, quick payouts after disasters, or a way for individuals to participate in small projects? The same high‑level tool (say, a bond) will look very different if it is structured for a G20 sovereign building a metro system versus a Small Island Developing State financing a mangrove restoration programme.
Then there is the cash‑flow logic: where the money comes from, and how it is repaid. Any mechanism should make transparent:
What is the return? This could be a fixed interest rate, a share of project revenues, a one‑off payout if a trigger event happens, or the sale of carbon credits over time, or any other means of return.
How is the return calculated? For a bond, it is a coupon (interest rate) on the face value; for a carbon project, it might be the number of verified tonnes of CO₂ times a contracted price; for a crowdfunding loan, it might be a fixed annual percentage of the amount invested.
Over what time horizon? Some mechanisms (like grants or one‑year parametric insurance contracts) are short‑term; others (like sovereign green bonds or infrastructure PPPs) can run 10–30 years. Matching the tenor of the finance to the underlying project is a key design choice.
Alongside cash flows, a good mechanism makes risk allocation explicit. Every contract should answer: What could go wrong, and who bears which risk? In climate projects, typical risks include:
Construction risk (the project is delayed or over budget),
Operating risk (it underperforms technically),
Market risk (power prices or carbon prices are lower than expected),
Policy risk (subsidies or regulations change), and, for some instruments,
Physical climate risk (storms, droughts, floods).
Different tools push these risks onto different shoulders: guarantees shift credit risk from banks to public guarantors; blended finance pushes first losses onto concessional funders; results‑based finance pushes performance risk onto the developer; parametric insurance transfers climate shock risk from farmers or governments to insurers. A “good” mechanism is not one where there is no risk (this does not exist), but one where risks are held by the actor best able to manage them.
Because these are contracts, not just concepts, they also need clear rules and triggers. This includes: what counts as success or failure; what data will be used to judge performance; who verifies it; what happens if targets are missed or events don’t unfold as expected (for example, does the interest rate step up, does a guarantee get called, does a results‑based payment simply not happen?). In climate finance, this is where measurement, reporting and verification (MRV) comes in: a mechanism that pays “per tonne of CO₂ avoided” or “per tonne removed” has to say exactly how those tonnes will be measured, by whom, and according to which standard.
Finally, every mechanism needs some thought on governance and alignment. Who decides which projects are eligible? How are conflicts of interest handled (for example, if the verifier is paid by the project developer)? How are environmental and social safeguards built in, so that climate finance does not create new harms? And how does the mechanism align with broader frameworks—national climate plans, sustainable finance taxonomies (A taxonomy is just a classification system: a structured way of deciding “what counts as what” and grouping things into clear categories. A sustainable finance taxonomy is a list of economic activities, with detailed criteria, that a country or region has decided will count as “environmentally sustainable” or “transition‑aligned”. The point is to give investors and regulators a common language so they can tell when an investment is genuinely green, and reduce greenwashing. The EU Taxonomy defines which activities (renewables, buildings, transport, etc.) are aligned with EU climate and environmental goals, and sets technical thresholds and “do no significant harm” rules)7, or net‑zero standards? Answering these questions up front helps determine whether the instrument will attract serious capital and be seen as credible.
Once you see these common building blocks—purpose and users, cash flows and returns, risk allocation, rules and triggers, and governance and alignment—the individual instruments in the table below become much easier to understand. Each one is simply a different way of arranging those elements to solve a particular climate finance problem.
A note:
Use‑of‑proceeds instruments (green, blue, transition bonds, green sukuk, most multilateral loans) = money must be spent on eligible activities.8
Performance‑linked instruments (SLBs, some RBCF and AMCs) = money can be used broadly, but cash flows change depending on whether measurable indicators are met.1
Here’s an explanation of typical green finance instruments:
First: what is a carbon credit? A carbon credit is a certificate that represents one tonne of CO₂ (or equivalent greenhouse gas) either not emitted or removed from the atmosphere. It’s like a “receipt” that a verified climate benefit has occurred somewhere.
How carbon credits work: A project (for example, a wind farm, a forest protection programme, or a direct‑air‑capture plant) is measured against a “baseline” of what emissions would have been without the project. The difference—verified by independent auditors—can be turned into credits. Each credit can be sold to a company or individual that wants to “offset” or compensate for their own emissions.
Two big families: 1) Avoidance/reduction credits – the project prevents emissions (e.g., replacing coal power with wind, distributing clean cookstoves, avoiding deforestation). 2) Removal credits – the project draws CO₂ out of the air and stores it (e.g., reforestation, biochar, direct air capture with geological storage).
Why it matters: Carbon credits turn climate outcomes into a tradable product. That creates a revenue stream for climate projects, which can unlock financing from banks and investors.
First: what is a bond? A bond is basically an IOU: an investor lends money to a government or company; in return, the issuer promises to pay regular interest and repay the principal at a fixed date. It’s like a structured loan that many investors can buy.
What is a green bond? A green bond is a regular bond where the money raised is earmarked for environmentally beneficial projects. The issuer commits that the proceeds will go only to qualifying “green” activities (renewable energy, energy efficiency, clean transport, green buildings, etc.), and usually reports on how the funds are used.
How it works in climate projects: Instead of financing “general corporate purposes”, a green bond might finance: a solar farm (emissions avoidance), a mass‑transit rail line (avoidance), or potentially large‑scale reforestation or wetland restoration (carbon removal). The bond itself doesn’t change financially—what makes it “green” is the use of proceeds and the issuer’s transparency and reporting.
First: what is a bond? A bond is essentially a tradable IOU. An investor lends money to a government, development bank, or company; in return, the issuer promises to pay regular interest and repay the principal at a set maturity date. It’s a way for issuers to raise large sums from many investors at once.
What is a blue bond in simple terms? A blue bond is a special type of green bond where the money raised is earmarked specifically for ocean and water‑related projects. In other words, it is a debt instrument issued to finance activities that protect or sustainably use marine and freshwater resources—things like healthy oceans, coasts, rivers, and water systems.
Blue bonds are bonds issued by governments, development banks, or other entities to raise funds from investors for marine and ocean‑based projects that generate positive environmental, economic, and climate benefits. They are a “subset” of green bonds, with a narrower focus on the “blue economy”—the part of the economy that depends on oceans and water (fisheries, shipping, tourism, coastal infrastructure, etc.).
What kinds of projects do blue bonds finance? Proceeds must go to clearly defined “blue” uses, for example:
Marine conservation: Expanding and managing marine protected areas, coral reef and mangrove restoration, protection of endangered marine species.
Sustainable fisheries and aquaculture: Transitioning fisheries to sustainable quotas, improving monitoring and enforcement, supporting low‑impact aquaculture that doesn’t destroy habitats.
Coastal resilience and adaptation: Restoring mangroves and wetlands to act as natural flood defences, reducing coastal erosion, protecting communities from storm surges and sea‑level rise.
Water and wastewater management: Improving urban water supply, wastewater treatment, and preventing sewage or nutrient pollution from entering rivers and seas.
Pollution reduction: Cutting plastic leakage into oceans, improving solid‑waste management, and cleaning up polluted waterways.
Sustainable “blue economy”: Supporting eco‑friendly coastal tourism, low‑carbon shipping, and offshore renewable energy (e.g., offshore wind).
Who issues blue bonds?
Sovereign blue bonds: Issued by national governments—Seychelles (2018) was the first, using a US$15 million sovereign blue bond to support sustainable fisheries and ocean conservation.
Development banks and IFIs: Institutions like the World Bank or IFC issue blue bonds or blue loans to finance portfolios of water/ocean projects.
Sub‑sovereigns and corporates: State‑owned utilities, port authorities, or private companies involved in shipping, water utilities, tourism, or fisheries can also issue blue bonds.
How are blue bonds structured financially? Financially, blue bonds work like normal bonds: investors receive periodic interest payments and principal at maturity. What makes them “blue” is: (1) the use‑of‑proceeds commitment to eligible blue projects, (2) adherence to blue/green bond guidelines, and (3) ongoing reporting on how funds are used and what environmental benefits they deliver. Often, multilateral banks or climate funds provide credit enhancements—like guarantees or concessional loans—to reduce risk and make the bond attractive. In the Seychelles case, the World Bank guarantee and GEF concessional funding cut the effective interest rate from about 6.5% to 2.8% for the issuer.
Blue bonds and debt‑for‑nature swaps: In some cases, blue bonds are combined with sovereign debt restructuring. For example, Belize and Seychelles used “blue bond + debt‑for‑nature swap” structures to reduce their overall debt burden while committing to long‑term marine conservation (note: not all blue bonds are tied to swaps—some are plain use‑of‑proceeds bonds with no debt restructuring component)12 Creditors accepted changes in the terms of existing debt in exchange for conservation commitments, while new blue bonds or blue loans financed marine protection. This hybrid model makes blue bonds especially attractive to small island and coastal developing states that are both ocean‑dependent and heavily indebted.
Why blue bonds matter in climate discussions: Healthy oceans and coasts are crucial for climate mitigation and adaptation: they absorb a large share of global CO₂, protect coasts from storms and sea‑level rise, and support livelihoods in many vulnerable countries. Yet “blue” sectors have historically received little climate finance compared to energy or land‑based projects. Blue bonds offer a way to channel large‑scale capital into the sustainable ocean economy, supporting: (a) mitigation via nature‑based solutions and low‑carbon maritime activities, and (b) adaptation via coastal resilience.
First: difference vs. green bonds. Green bonds restrict how the money is spent. Sustainability‑linked bonds do not; instead, they change the financial terms depending on performance.
What is an SLB? An SLB is a bond where the issuer (a company or government) promises to meet certain sustainability targets—for example, “reduce our greenhouse gas emissions by 40% by 2030.” If the issuer fails, the bond’s coupon (interest rate) usually steps up, meaning the issuer pays more to investors.
How it works in climate: The bond can finance anything (new factories, general operations, etc.), but the issuer is financially rewarded or penalised based on whether it hits climate‑related key performance indicators (KPIs). To reach these KPIs, the issuer might: invest in avoidance (efficiency, renewables, new processes) and/or removal (buying high‑quality carbon removals, investing in carbon capture). For investors, SLBs are a way of tying climate performance to money even when funds are not ring‑fenced.
First: what is “transition finance”? Transition finance is funding that helps high‑emitting companies or sectors move from “brown” to “green”, even if they’re not green yet. Think of steel, cement, aviation, oil and gas—industries that can’t decarbonise overnight.
What is a transition bond? A transition bond is similar to a green bond, but specifically aimed at financing credible transition activities in high‑emitting sectors—such as replacing old coal plants with much cleaner alternatives, upgrading industrial processes, or adding carbon capture equipment. The money must be used for projects that materially reduce emissions relative to business‑as‑usual. Climate Transition Bonds go a step further, following specific guidelines (e.g., by ICMA) requiring a science‑based transition plan and strong disclosure.
How it works in climate: Proceeds mainly support emissions avoidance (e.g., process efficiency, fuel switching), but can also finance removal‑enabling infrastructure, like CO₂ transport and storage hubs or BECCS/CCS installations on existing plants. The aim is to fund the journey from high emissions to low emissions in a transparent, Paris‑aligned way.
First: what problem is it solving? Many climate projects (especially in developing countries or new technologies like direct air capture) are too risky or unfamiliar for purely commercial investors. Their returns might be fine on paper, but perceived risks (country risk, technology risk, policy risk) scare capital away.
What is blended finance? Blended finance is a structure, not a single product. It combines “concessional” capital from public or philanthropic sources with commercial capital from private investors. The concessional portion takes on more risk or lower returns—through first‑loss tranches, subordinated debt, or guarantees—so that private investors feel safer coming in.
How it works in climate: Imagine a fund where a development bank provides a junior, low‑return tranche, and private investors provide a senior, market‑rate tranche. If things go wrong, the public tranche loses money first, protecting the private investors. This can make renewables in emerging markets, efficiency upgrades, or early‑stage CDR projects bankable. Blended finance is thus a risk‑sharing tool to crowd in private capital to projects that serve the public good but would otherwise be under‑financed.
First: what is results‑based finance? Instead of paying for inputs (like building a plant) or promises, results‑based finance pays only when measurable, verified outcomes are delivered—like “X MWh of clean electricity” or “Y tonnes of CO₂ reduced”.
What is RBCF in climate? In results‑based climate finance, a funder (often a government, climate fund, or development bank) agrees to pay a fixed amount per tonne of CO₂ reduced or removed, or per unit of a climate‑relevant result (e.g., number of clean cookstoves in regular use). Independent auditors verify the results; only then is money disbursed.
How it works in climate: For an avoidance project, payments might be made per tonne of emissions avoided by a renewable plant compared to a fossil baseline, or per hectare of forest not cut down. For a removal project, payments might be made per tonne of carbon actually stored in restored forests or wetlands. RBCF aligns finance with verified impacts, and can complement or substitute carbon credit revenues.
First: what is concessional finance? Concessional finance is money offered on softer terms than the market—for example, loans with below‑market interest rates, longer grace periods, longer maturities, or even outright grants that don’t have to be repaid. It is usually provided by governments, development banks, or climate funds.
Grants vs. concessional loans: A grant is money given with no expectation of repayment, often used for project preparation, technical assistance, or to cover parts of capital costs. A concessional loan must be repaid, but on easier terms than commercial loans (cheaper and slower).
How it works in climate: Concessional finance is used to: (a) make marginal projects (like rural solar mini‑grids, resilience infrastructure, or new removal technologies) financially viable; (b) absorb early‑stage risks; and (c) support countries or communities that cannot afford purely commercial debt. It can directly fund projects or be used inside blended‑finance structures to crowd in private capital.
First: what is a guarantee? A guarantee is a promise by a third party (the guarantor) to repay part or all of a loan if the borrower defaults. This third party can be a development bank, a government agency, or a specialised guarantee fund. Think of it as “credit insurance”: it doesn’t provide money up front, but it stands ready to cover losses if something goes wrong.
Types of risk covered: Guarantees can cover commercial risk (borrower can’t pay), political risk (expropriation, currency transfer restrictions), or even certain performance risks of a project.
How it works in climate: Suppose a bank is hesitant to lend to a wind project in a lower‑income country. If a multilateral bank guarantees, say, 50% of the loan, the bank’s risk is effectively halved. That means it is more likely to lend and at a better interest rate. Similarly, future CDR projects might be financed if a public entity guarantees minimum carbon price or offtake payments, making long‑term investments less risky. Guarantees are powerful because a small amount of guarantee capital can unlock a much larger volume of private lending.
First: what is a multilateral fund? A multilateral fund pools money from many countries (donor governments) and sometimes other contributors, and channels it into projects in developing countries. It is usually overseen by a board representing those countries, and implemented through development banks or UN agencies.
Examples: The Green Climate Fund (GCF), Global Environment Facility (GEF), Climate Investment Funds (CIF), and Adaptation Fund.
How they work in climate: These funds provide grants, concessional loans, equity, and guarantees to support mitigation (emission cuts), adaptation (climate resilience), and sometimes explicit carbon removal (e.g., forest restoration). Because they are backed by governments, they can take on more risk or accept lower returns than private investors. They often act as anchor funders in blended finance structures, or provide results‑based payments to governments and project developers. For many low‑income countries, multilateral funds are the primary external source of climate finance.
First: what is a “swap” in this context? In general finance, a “swap” is an agreement to exchange one set of cash‑flow obligations for another. In the sovereign context here, it’s more like a structured re‑negotiation of debt terms.
What is a debt‑for‑climate swap? A debt‑for‑climate (or debt‑for‑nature) swap is a deal where a country’s existing external debt is reduced, refinanced on better terms, or partially cancelled, in exchange for the government committing to invest in specific climate or conservation projects. Creditors might accept a discount on what they are owed, and the “savings” are ring‑fenced for climate activities.
How it works in climate: For a country heavily indebted and vulnerable to climate impacts, creditors might agree that US$X of debt is refinanced into a cheaper “blue bond” or climate bond, while the country commits to spend a portion of the freed‑up money on, say, coastal protection, forest conservation, or resilient agriculture. This simultaneously reduces debt stress and increases climate investment. Most current swaps focus on adaptation and conservation (i.e., resilience and avoided emissions), but in principle they could also fund large‑scale ecosystem restoration (a form of carbon removal).
First: what is carbon pricing? Carbon pricing means putting a price on greenhouse gas emissions through either: (1) a carbon tax (pay a fee per tonne of CO₂ emitted), or (2) an emissions trading system (ETS), where companies must hold tradable “allowances” for every tonne they emit. If they emit less, they can sell spare allowances; if more, they must buy extra.
How this creates finance: Carbon pricing changes behaviour (by making pollution more expensive) and raises revenue for governments. Those revenues can be used to fund climate projects—grants, concessional loans, results‑based schemes, or subsidies for clean technologies.
What is CBAM? CBAM stands for Carbon Border Adjustment Mechanism. It is essentially a system (pioneered by the EU) that charges imports for the carbon embedded in them, so that foreign producers face a similar carbon cost as domestic producers subject to carbon pricing. The idea is to avoid “carbon leakage” (moving dirty production abroad).
CBAM‑linked flows: The money collected through CBAM can, in principle, be channelled back into climate finance—for example, supporting decarbonisation in poorer exporting countries, or buying high‑quality credits. Depending on design, this can steer finance towards both avoidance (clean production) and removal (credit purchases or CDR investments).
First: what is CDR? CDR stands for Carbon Dioxide Removal—any process that actively takes CO₂ out of the atmosphere and stores it for long periods. This includes natural methods (reforestation, restoring peatlands, mangroves) and engineered methods (direct air capture, BECCS, enhanced weathering, biochar, etc.).
What is an AMC? An Advance Market Commitment (AMC) is a pledge by buyers—often governments or large companies—to purchase a certain amount of a product in the future at a pre‑agreed price, if that product can be delivered with agreed‑upon standards. AMCs were used successfully to accelerate vaccine development: companies invested in R&D and capacity knowing that a market would exist.
What are AMCs for CDR? AMCs for CDR are long‑term purchase commitments for future carbon removals. Buyers say: “If you can remove and durably store CO₂ to standard X, we promise to buy Y tonnes at price Z over the next decade.” This gives CDR developers the revenue certainty needed to secure financing for expensive plants. Without AMCs, many CDR businesses are stuck in the “valley of death” where costs are high and markets uncertain. AMCs therefore are a demand‑side tool to de‑risk investment in new removal technologies.
First: what is insurance in general? Traditional insurance compensates you for actual losses incurred: you prove your loss (e.g., damage from a storm), and the insurer reimburses you up to your policy limit, after assessment. This can be slow and administratively heavy.
What is parametric insurance? Parametric insurance pays out automatically when a specified event happens, based on a measurable parameter—such as wind speed above X, rainfall below Y, or an earthquake of magnitude Z or more. Payout is triggered by the parameter, not by proof of actual loss.
How it works in climate: For climate‑related risks (hurricanes, droughts, floods), parametric insurance can provide very fast, predictable payouts to governments, utilities, or farmers. For example, a country might get a pre‑agreed payout if a hurricane stronger than Category 4 passes within a certain distance. A solar farm might receive payments if cloud cover or wind speeds deviate too far from the norm. While this doesn’t directly reduce or remove emissions, it improves climate resilience, protects revenue streams for renewable projects, and makes banks more willing to finance assets in climate‑vulnerable regions.
First: what is a sukuk? In Islamic finance, charging or paying interest in the conventional sense is prohibited. A sukuk is a Shariah‑compliant financial instrument that is often described as an “Islamic bond”, but technically it represents ownership in an underlying asset or project, and returns are generated via profit‑sharing or lease‑like structures, not explicit interest.
What is a green sukuk? A green sukuk is a sukuk where the underlying assets or projects are environmentally beneficial—for example, a solar farm, a wind park, or a water treatment plant. It must satisfy both: (1) Shariah requirements (no prohibited activities, asset backing, fair risk‑sharing), and (2) green criteria (as defined by taxonomies or standards).
How it works in climate: Governments and companies in Muslim‑majority countries can issue green sukuk to finance renewable energy, clean transport, efficient buildings, or even nature‑based climate projects. Investors receive periodic distributions from project revenues (e.g., electricity sales), not interest, and gain exposure to both financial and environmental returns. Islamic green sukuk expand the pool of climate capital by tapping investors who prefer or require Shariah‑compliant instruments.
First: what is crowdfunding? Crowdfunding is when many individuals each contribute relatively small sums of money, usually via an online platform, to fund a project, business, or cause. In return, they might get rewards, interest, profit‑sharing, or simply the satisfaction of supporting something they believe in.
What are climate/green crowdfunding platforms? These are specialised platforms that allow people to directly invest in or donate to renewable energy, energy‑efficiency, conservation, or climate‑tech projects. Minimum investments can be very low (e.g., €10 or INR25), making participation broadly accessible.
How it works in climate: A developer might list a community solar project on a platform; hundreds of individuals fund part of the project and receive a fixed interest payment or share of revenues over time. This model is particularly well‑suited to small‑scale, local avoidance projects—like rooftop solar, community wind turbines, building retrofits—where community buy‑in is crucial. It is less suited (for now) to capital‑intensive, highly technical removal projects, but it plays a powerful role in democratising climate finance and building public support for the transition.
Now you ask: “What is the chance that it rained, given that the ground is wet?”
That’s exactly the kind of question Bayes’ Theorem answers.
Think of Bayes’ Theorem as a smart way of changing your mind when new information appears. In life, we start with a belief based on past experience. Then something new happens. Instead of ignoring it, we usually update what we believe. It’s a part of Probability Theory that helps you combine old information you already have, with new information you have just received.
It looks mad, doesn’t it? It took me months to be able to remember the Bayes formula, and it took cricket to help me learn it finally. But first, an explanation of what we have above:
In the formula,
A = the thing you care about (Example: It rained). This is your starting belief before you see new evidence. It could be anything, such as, it’s dry season so it won’t rain today.
P(A) is the probability of the starting belief.
B = the evidence you see. (Example: The ground is wet). This is new information.
P(B) is the probability of the new information happening.
the “|” sign in the formula means “given” so P(A|B) will be read as Probability of A given B, meaning that the probability that A is still true given that the new information B is now known (“Now that I see the ground is wet, how likely is it that it rained?”), and P(B|A) is the probability that B is true given that we know that A happened (“If it did rain, how likely is the ground to be wet?”).
Now let’s take some help from cricket WITH MADE UP NUMBERS:
Let’s say India wins 70% of all cricket matches. This is P(A), where A = India wins 70% of all cricket matches, okay?
Now imagine Virat Kohli makes a century in 40% of the matches he plays. This is P(B), where B is Virat’s imaginary (I haven’t checked) century strike rate.
P(A|B) is the probability that India won a match given that Virat hit a century. Let’s keep this at 80%.Yes I’m a fan, how did you guess?
Now the new information is that India has won a match. So given that we now know that India has won a match, what is the probability that Virat hit a century?
So, now,
P(India winning a match for any reason) = 70% = 0.7
P(Virat’s century in a winning or losing cause) = 40% = 0.4
P(India winning given that Virat has hit a century) = 80% = 0.8
So, if we know India has won, what is the probability that Virat hit a century?
P(Virat’s Century given that India has won) = [P(Virat’s century in a winning or losing cause) × P(India winning given that Virat has hit a century)] / P(India winning a match for any reason)
I know this is all new and complex for many readers (it took me lots of effort and a Virat-inspired intervention to learn this too), so take your time to read it again if you need to, as many times as might help.
Player of Series Monsters At this point I want you to know that Cricinfo doesn’t have a list of women cricketers in decreasing order of player of series awards like they do for the men. There’s also a paucity of tabulated data available for women’s cricket generally. So I’m concentrating only on the men. The list of men is clearly documented, as mentioned:
Name
PoS Awards (Tests, ODIs, T20Is)
Virat Kohli (India)
22
Sachin Tendulkar (India)
20
Shakib Al Hasan (Bangladesh)
17
Jaques Kallis (South Africa)
15
David Warner (Australia)
13
Sanath Jayasuriya (Sri Lanka)
13
Of these, I got Perplexity AI to do some data finding and number crunching for me for Virat, Sachin, and Shakib for ODIs.
BayesUSING REAL NUMBERS When the team won, how often was this player the reason?
~24% of India ODI wins with him include a Kohli hundred
Tendulkar
India (ODI)
0.505
0.11
0.67
0.14 (14%)
~14% of India ODI wins with him include a Tendulkar hundred
Shakib
Bangladesh (ODI)
0.36
0.03
0.77
0.07 (7%)
~7% of all Bangladesh ODI wins include a Shakib hundred
Bayes calculation for Virat, Sachin, and Shakib
What this means
Virat Kohli in a strong India: One in every four ODI wins arrives with a Kohli century inside it. He does not just bat well; he bats well in a machine that is already built to win. His centuries are the accelerant on a fire that’s already burning. When India wins, there’s a strong chance he is the one who decided the margin, the pace, the emotional tone of the chase.
Sachin Tendulkar in a medium India: One in every seven wins contains a Tendulkar century. He played across eras—through the ’90s when Indian cricket was still finding its feet, through the 2000s when it became a force. His centuries had to do more heavy lifting because the team around him was less consistently dominant. The win probability bump he created had to be steeper, had to arrive at moments when India could genuinely lose without him.
Shakib Al Hasan in a historically weaker Bangladesh: One in every fourteen overall Bangladesh ODI wins includes a Shakib century—but here’s the insight: when he does score a hundred, Bangladesh almost never lose that game (6 of 7). On a much thinner winning base, his performances are load‑bearing. He is not the beneficiary of team strength; he is the architect of team possibility.
Shakib is kind of amazing in this that 6 of his 7 centuries have come in wins, and it got me curious about how many 50+ scores have these gents made in wins, but that data is not available in a clean Bayes format.
Kohli and Tendulkar sit on mountains of 50+ scores in ODIs – over a hundred each when you add fifties to centuries.8 Where they differ is in what happens after fifty.9 Kohli’s conversion rate from 50 to 100 in ODIs is significantly higher than Sachin’s. Once he’s crossed fifty, he tends to keep going, especially in chases. Part of that is temperament – an almost obsessive refusal to give away his wicket once set – but a big part is structural: India in his era often had deeper batting, was better at chasing (or he was better at chasing anyway), and capable partnerships.
Tendulkar’s 50+ scores, by contrast, sit in a very different ecosystem. He played long stretches of his career in teams that were less stable, so his fifties often had to be the innings and the platform at the same time. The conversion to hundreds is lower not because the intent wasn’t there, but because the conditional environment around him – partners, match situations, opposition attacks – made it much harder to keep going at the same rate. Yet even as “just” fifties, those scores were repeatedly the spine that held up India’s innings.
Bangladesh’s baseline ODI win percentage is far lower than India’s. That means:
A Shakib 50 – even without going on to a hundred – does outsized work.
His 50+ scores in tournaments like the 2019 World Cup (where he reeled off one high‑impact innings after another) are not just “good knocks”; they are the narrow ledges on which Bangladesh’s entire chase or defence balances.
And because he does this as an all‑rounder, a fifty for Shakib often comes with 10 overs of spin as well, and Bangladesh tend to look competitive almost exactly on the days Shakib has a good outing.
So much of cricket is about context, and this post reinforced that for me. Virat Kohli doesn’t just score centuries; he does so in a system that consistently wins, amplifying his influence. Sachin Tendulkar carried innings for teams that sometimes struggled, meaning his 50+ scores were often the backbone of a win rather than just the flourish. And Shakib Al Hasan? In a team with fewer wins overall, his big performances don’t ride on a strong machine — they create the machine.
While most countries are trying for “net zero” status (the point at which their greenhouse gas emissions are balanced by removals), there are some that are currently carbon negative: they remove more carbon dioxide from the atmosphere than they emit.
Three nations have achieved this status: Bhutan, Suriname, and Panama.1
Bhutan, the world’s first officially carbon-negative country, absorbs approximately six tonnes of carbon dioxide per capita through its vast forests, while emitting two tonnes per capita (the nation’s constitution mandates that at least 60 percent of its land remain forested “for all time,” a commitment it reaffirmed at COP15 in Copenhagen in 2009 and again at COP21 in 2016).23 Suriname, the most forested country on Earth with 97 percent forest cover, absorbs roughly 8.8 million tons of carbon annually while emitting 7 million tons.4 Panama achieved carbon-negative status through a combination of bold energy sector transitions and conservation measures, with 65 percent of its territory covered in forest.5
But how do we know how much carbon they emit and how much they remove from the atmosphere? The answer is carbon accounting.
Carbon Accounting Carbon accounting (also called greenhouse gas accounting) is the systematic method of measuring, recording, and reporting the greenhouse gas emissions generated by activities at the individual, organisational, or national level.
You can read more about it here, here, and also here (this is a technical post) in that order.
Understanding Carbon Negativity In climate work, experts distinguish between production-based emissions and consumption-based emissions. This distinction can alter whether an entity appears to be carbon positive, neutral, or negative.6
Production-based emissions represent what’s emitted physically within a country’s borders. This is the usual approach taken by national greenhouse gas inventories following UNFCCC (United Nations Framework Convention on Climate Change) guidelines. This accounting is relatively straightforward: it estimates emissions from all the oil, coal, and gas consumed within a country by private households, industrial production, and electricity generation.7
Consumption-based emissions, are “all the greenhouse gas emissions needed, globally, to satisfy the final demand of residents of this country”. This approach acknowledges that occur in one location to produce goods and services consumed elsewhere.8
The standard formula for consumption-based emissions is:910
Consumption-based emissions = Production-based emissions + emissions from imports − emissions from exports
Consider the implications: if the United Kingdom closes its domestic steel industry and begins importing steel from China, UK production-based emissions fall while Chinese production-based emissions rise. Yet from a consumption perspective, those emissions still relate to UK-based consumption—the steel is still being used in Britain, regardless of where it was produced.
The difference between these two accounting methods can be substantial. When accounting for emissions on a consumption basis rather than territorial (production) level, United States emissions increase by 10.9 percent,11 while China’s emissions would decrease by substantially.11 For large European economies, net imported emissions represent 20–50% of consumption emissions;11 in Japan, they account for 17.8 percent, and in the United States, 10.8 percent.11
Accounting methods matter: whether a nation appears carbon negative may depend not just on physical realities but on how boundaries are drawn, what emissions are counted, and how carbon sinks are calculated.
The Macroeconomic perspective From a macroeconomic perspective, production-based emissions align with a nation’s Gross Domestic Product (GDP). The national income identity expresses GDP as:12
GDP = C + I + G + (X − M)
where:
C = household (private) consumption
I = investment
G = government spending
X = exports
M = imports
Production‑based emissions follow the same logic as GDP: they count what is produced within a country’s borders, regardless of where those goods are ultimately consumed. In that sense, a country can run not only a financial trade surplus or deficit, but also a carbon trade surplus or deficit.
This concept is often framed through the Pollution Haven Hypothesis, which suggests that carbon-intensive production tends to migrate to jurisdictions with looser environmental regulations or lower energy costs (often developing nations), while cleaner, service-oriented economies (often developed nations) import the resulting goods.13
We can visualize this by mapping carbon flows against the standard macroeconomic identity for the trade balance (X – M):
The Carbon Exporter (Trade Surplus X > M): Countries like China or Russia often function as the world’s “smokestacks.” They run trade surpluses in manufactured goods, meaning their Production-Based Emissions are significantly higher than their Consumption-Based Emissions. They are effectively exporting the “embodied carbon” of steel, cement, and electronics to the rest of the world.14
The Carbon Importer (Trade Deficit X < M): Service-oriented economies like the UK or US often run trade deficits in goods. Their domestic factories are cleaner (or closed), lowering their territorial emissions. However, their consumption demands are met by imports, creating a “carbon trade deficit”: they consume far more emissions than they produce physically within their borders.15
This dynamic creates a “Carbon Loophole.” If the UK closes a steel mill to meet a “Net Zero” target but immediately starts importing steel from China, global atmospheric emissions haven’t changed—they have simply moved across a border. This leakage is the primary economic argument for policies like the European Union’s Carbon Border Adjustment Mechanism (CBAM), which attempts to tax the “embodied carbon” in imports, effectively reconciling the difference between production and consumption accounting at the border.1617
Consumption-Based Emissions Consumption-based emissions take a fundamentally different approach. They represent “all the greenhouse gas emissions needed, globally, to satisfy the final demand of residents of this country”.11
The standard formula for consumption-based emissions is:18
Consumption-based emissions = Production-based emissions + emissions from imports − emissions from exports
More specifically:
Production-based emissions: what’s emitted within the country’s borders (the usual UNFCCC inventory)
Emissions from imports: emissions that happened abroad while producing goods and services that residents import and consume
Emissions from exports: emissions that happened domestically to produce goods that are consumed abroad; these are subtracted because they “belong” to foreign consumers in this method
Consumption-based accounting takes care of the problem that CO₂ emissions are mobile internationally through trade. A decrease in one country’s production-based emissions may be more or less directly related to an increase in another country’s emissions if production has simply shifted locations.19
Implications for Climate Policy and Carbon Negativity The choice between production-based and consumption-based accounting has profound implications for assessing climate responsibility, setting reduction targets, and understanding whether a nation is truly carbon negative.
Consider again our carbon-negative exemplars: Bhutan, Suriname, and Panama. These countries achieve carbon-negative status through their vast forest cover, which acts as carbon sinks absorbing more CO₂ than their economies emit.
Using production-based accounting, these assessments are straightforward:
Bhutan emits 2 tonnes CO₂ per capita while its forests absorb 6 tonnes per capita
Suriname’s forests absorb 8.8 million tons annually while national production-based emissions are 7 million tons
Panama’s forests and conservation reserves create net carbon sequestration exceeding territorial emissions
But what if we applied consumption-based accounting? These nations, like all countries, import goods and services that embody emissions from production elsewhere.
The question essentially is, while the nation is carbon negative, are its citizens?
This question reveals the complexity of carbon accounting at the national level. A nation might be a net carbon sink based on territorial emissions and removals, yet still contribute to global emissions through its consumption patterns. Conversely, a nation with high production-based emissions might argue that much of its emissions serve to produce goods consumed elsewhere.
Which Accounting Method Should Prevail? There is ongoing debate among climate policy experts about whether consumption-based or production-based accounting should be the primary basis for climate policy.
Arguments for production-based accounting:
It’s simpler to measure and verify
It aligns with territorial sovereignty and national control
Countries have direct policy leverage over production within their borders
It’s the basis for UNFCCC inventories and the Paris Agreement commitments
Arguments for consumption-based accounting:
It better reflects true climate responsibility
It prevents “carbon leakage” where emissions are simply offshored
It accounts for the full lifecycle of consumption patterns
It can inform more comprehensive climate policies including consumption measures and border adjustments
In practice, most climate policy continues to be based on production-based accounting through UNFCCC inventories, but consumption-based approaches are increasingly used to complement this picture and inform policy discussions about trade, consumption, and global equity.
The Path Forward For nations aspiring to carbon neutrality or carbon negativity, the journey requires:
Comprehensive measurement following standards like ISO 14064-1 to understand the full scope of emissions across all categories, including often-overlooked indirect emissions.
Clear baseline establishment with robust base year policies and recalculation procedures to enable meaningful tracking of progress over time.
Strategic mitigation through a combination of emissions reduction (shifting to renewable energy, improving efficiency, transforming industrial processes) and removal enhancement (protecting and expanding forests, implementing carbon capture, restoring degraded lands).
Project-level quantification using frameworks like ISO 14064-2 to measure the specific impact of mitigation initiatives, with conservative assumptions and comprehensive accounting of all affected sources, sinks, and reservoirs.
Independent verification following ISO 14064-3 to provide credible assurance to domestic and international stakeholders that reported emissions, removals, and reduction claims are accurate.
Transparent reporting that discloses methodologies, boundaries, assumptions, data sources, and uncertainties, enabling users to understand and evaluate climate claims.
Consistent application over time, with clear documentation of any methodological changes and appropriate recalculations to maintain comparability.
Carbon negativity represents a climate milestone that reflects a fundamental restructuring of an economy’s relationship with atmospheric carbon. Understanding how these countries achieve carbon negativity, helps us understand both, how climate responsibility is allocated in a globally interconnected economy, and what nations must do to achieve carbon negativity.
A 40-year-old non-smoker in Delhi faces a measurable probability of dying in the next year. If the 40 year old is a woman, she will have a slightly better chance at life than a male counterpart. If she lives in a wealthy area, her chances are once again better than another woman living in a less privileged location.123
How do we know this? We know this because actuaries work with mortality and health data from millions of people, and build tables that segment risk by age, gender, smoking status, income, and even geography, to price policies accurately.4
Types of risk Over time, experts have classified risk into different types. Here’s a table about the different types of risk:
The possibility of loss from natural events or accidents. The oldest, most intuitive kind of risk.
• Unintended—nobody wants them • Objective frequency data—insurers have centuries of records • Insurable—probability and consequence can be estimated from historical data • Cannot create profit—only causes loss
• Fire and property damage • Windstorms and hail • Theft and burglary • Flooding • Liability from personal injury
The risk that your business’s internal machinery breaks down. Unlike hazard risk, it’s inherent to doing business—you can’t eliminate it, only manage it. Also cannot be diversified away. Defined by Basel II as: “Risk of loss from inadequate or failed internal processes, people and systems, or external events.”
• Inherent to operations—impossible to eliminate • Non-diversifiable—all firms in an industry face similar operational risks • Hard to quantify—driven by control quality and governance, which are difficult to measure • Multiple sources—spans people, processes, systems, and external events
Process Failures: Accountant enters data incorrectly, leading to wrong financial statements; Wrong calculation of tax liabilities
Human Error: Surgeon operates on wrong patient; Employee sends confidential email to wrong recipient; Trader executes wrong order
System Failures: Bank’s payment system crashes; Company’s website goes down during peak shopping season; Database corruption losing customer data
Risk from changes in financial variables: credit defaults, price movements, or inability to access funds. Encompasses three subcategories.
• Market-driven—determined by supply and demand in public markets • Observable prices—interest rates, bond spreads, stock prices are public • High correlation—multiple financial risks often move together during crises
Credit Risk: Borrower fails to repay loan; Bank faces default
Market Risk (Interest Rate, Equity, Currency, Commodity): Interest rates rise, bond portfolio value falls; Stock prices decline; Rupee weakens against dollar; Oil prices spike increasing business costs
Liquidity Risk (Asset & Funding): Cannot sell asset when needed (asset liquidity); Cannot raise cash when obligations due (funding liquidity)
Risk that your business strategy is wrong. Risk from strategic decisions and competitive threats that can derail long-term objectives. Highest impact, but low frequency.
• High impact, low frequency—rare but potentially catastrophic • Long-term consequences—effects persist for years • Cross-functional impact—affects entire organization • Forward-looking—requires anticipating future changes • Not quantifiable—each situation is somewhat unique
Poor Strategy Decisions: Entering unviable new markets; Expanding too quickly into new industries; Pricing strategy that’s unprofitable
Competitive Threats: New disruptive competitor; Competitor’s aggressive pricing; Merger of competitors
Technological Disruption: Emerging technology makes business model obsolete (e.g., ride-sharing disrupting taxis); Failed innovation or delayed product launches
Resource Misalignment: Allocating resources to declining products instead of growth opportunities
Market/Industry Changes: Shift in customer needs and expectations; Regulatory changes forcing business model changes
The risk that you violate laws, regulations, or internal policies, resulting in fines, legal action, or reputational damage. The regulatory environment is constantly changing.
• Pervasive—affects all areas of organization • Constantly evolving—new regulations, changing requirements • Penalties escalating—fines and enforcement becoming more severe • Jurisdiction-dependent—different rules in different countries • Partly controllable—you can strengthen controls, but regulatory changes are external
Financial Crimes: Money laundering violations; Bribery and corruption; Sanctions violations
Data & Privacy: GDPR violations (Europe); CCPA violations (California); HIPAA violations (healthcare); Customer data breaches
The risk that negative publicity damages your brand, eroding customer trust, investor confidence, investor perception, or ability to attract talent. One of the hardest risks to quantify.
• Hidden until it happens—not visible in normal operations • Disproportionate impact—market values reputation more than the direct financial loss • Self-inflicted worse than external—fraud damages reputation 2x more than accidents • Long recovery time—trust takes years to rebuild • Interconnected—affects customer base, employees, investors, partners simultaneously
Product/Service Failures: Volkswagen emissions scandal (2015): $30B+ in losses, brand destroyed, took years to recover; Boeing 737 MAX crashes: customer confidence shattered; Product recalls damaging trust
Ethical/Fraud Issues: Wells Fargo account scandal: reputation destroyed despite being largest bank; Facebook/Meta privacy scandals: customer trust eroded
The risk of losses from disruption or failure of IT systems, data breaches, ransomware attacks, or technology obsolescence. Increasingly distinct from general operational risk.
• Rapidly evolving threat landscape—new attack vectors constantly emerge • Control-dependent—pricing based on current security posture, not history • Insurance available—unlike most strategic risks, cyber can be insured • Industry-dependent—high-risk sectors (finance, healthcare) pay more • Improving controls reduce premiums—strong incentive alignment
Data Breaches: Hackers steal customer information; Personal data of millions exposed; Regulatory fines and lawsuits follow
Ransomware Attacks: Criminals lock you out of systems; Demand payment to restore access; Business operations halt
System Failures: Software bugs or aging infrastructure cause crashes; Website goes down; Payment systems fail
DDoS Attacks: Website flooded with traffic, becomes inaccessible; Business loses revenue during attack
The possibility of loss from natural events or accidents. The oldest, most intuitive kind of risk.
Relatively straightforward to price because: Historical data is abundant and reliable Frequency and severity are stable over time
Easiest to price. Insurers have vast datasets spanning centuries showing how often fires, floods, and accidents occur. This precision makes hazard risk the most competitively priced and cheapest form of risk insurance.
The risk that your business’s internal machinery breaks down. Unlike hazard risk, it’s inherent to doing business—you can’t eliminate it, only manage it. Also cannot be diversified away. Defined by Basel II as: “Risk of loss from inadequate or failed internal processes, people and systems, or external events.”
• Real drivers (control quality, governance, employee skill) are hard to measure • Cannot use simple historical formulas • Basel II uses crude proxy: operational risk capital = percentage of gross income • Limited historical data compared to hazard risk • Outcomes are correlated across firms during crises
Cannot diversify away. When 100 banks all face the same operational risk (say, a payment system cyberattack), they all suffer simultaneously. This systemic nature makes operational risk expensive to accept and pricing it requires judgment, not just formulas.
Risk from changes in financial variables: credit defaults, price movements, or inability to access funds. Encompasses three subcategories.
• Models based on historical data miss tail risk (rare catastrophic events) • Correlation assumptions break during crises (2008 showed this) • Pricing assumes future resembles past • Volatile and difficult to predict
Impossible to price accurately at extremes. Financial risk is driven by market sentiment, which can shift suddenly. Models work 99% of the time but fail catastrophically in the 1% (like 2008), when many risks materialize simultaneously.
Risk that your business strategy is wrong. Risk from strategic decisions and competitive threats that can derail long-term objectives. Highest impact, but low frequency.
• No historical data for “probability that our strategy fails” • Each strategic decision is somewhat unique • Cannot use formulas or actuarial tables • Outcomes depend on management judgment and execution • Extremely difficult to quantify in advance
Cannot be insured. Strategic risk is almost entirely uninsurable because each company’s strategy is unique. CEOs and boards must accept this risk as part of doing business. Pricing relies on scenario analysis and management judgment, not hard data.
The risk that you violate laws, regulations, or internal policies, resulting in fines, legal action, or reputational damage. The regulatory environment is constantly changing.
• Probability of enforcement depends on regulator priorities (which change) • Penalties are often discretionary and unpredictable • New regulations create retroactive compliance challenges • Conflicting guidance from different regulators • Costs increase with regulatory tightening
Costs are rising fast. Regulators are increasingly aggressive, penalties are larger, and reputational consequences are severe. Organizations must continuously invest in compliance infrastructure (legal teams, compliance officers, audits) as a cost of doing business.
The risk that negative publicity damages your brand, eroding customer trust, investor confidence, investor perception, or ability to attract talent. One of the hardest risks to quantify.
• Stock price falls MORE than announced loss (2x for fraud, 1x for accidents) • 26% of company value is directly attributable to reputation (one study) • No standard pricing model • Very difficult to quantify until it happens • Historical data limited
Stock market values reputation more than we can measure. When a company announces a $1B fraud loss, stock price might fall 5% ($5B loss in value). The extra $4B is “reputational loss”—the market’s judgment that the company is now riskier. Yet most companies can’t quantify or insure this risk.
The risk of losses from disruption or failure of IT systems, data breaches, ransomware attacks, or technology obsolescence. Increasingly distinct from general operational risk.
• Unlike hazard risk (stable data over decades), cyber threats evolve rapidly • Historical data is unreliable—new attack types didn’t exist 5 years ago • Pricing focuses on current security posture not past incidents • Rapidly changing insurance market (premiums spiked 80% in 2021-2022) • Standardization emerging (ISO 27001, NIST)
Pricing is behavior-based. Unlike traditional insurance (fixed premium regardless of actions), cyber insurance prices based on your current controls. Companies with firewalls, multi-factor authentication, and ISO 27001 certification pay ₹80,000/year. Those with weak security might pay ₹3,00,000 or be denied coverage. This creates powerful incentives to improve security.
Therefore, risk can technically be transferred from one person to another. And this can be offered as a business service, for a price.
Now, before we go into this further, please understand that some risks can never be transferred- just that the effect of their impact can be mitigated. People will die, that is life. But by buying term insurance, we can ensure our families don’t suffer financial loss as well as the loss of our love and support. Similarly, living beings get sick- by purchasing health insurance we can just make sure we don’t face financial difficulties if we ourselves get sick in a way that costs a lot of money to fix. We are not transferring the death and decay, we are transferring the financial cost of these events.
1. The Formula2021 With that out of the way, when someone asks you to bear their risk, you charge them a price. That price is made up of several components:
Price of Risk = Expected Loss + Administrative Costs + Risk Loading + Profit Margin
Where:
Expected Loss is simply: Probability × Consequence. If there’s a 2% chance of a ₹100,000 loss, the expected loss is ₹2,000.
Administrative Costs are the cost of doing business. For an insurer, this includes underwriting (reviewing your application), policy servicing (managing your account), claims processing, and marketing. For a bank, it includes loan documentation, monitoring your creditworthiness, and collecting payments if you default.
Risk Loading is the “insurance premium on the insurance premium.” It’s an extra charge you demand to accept the fact that reality might differ from your expectations. This is where variance becomes critical.22
Profit Margin is what you keep as profit.
2. Variance
Variance is uncertainty about whether actual outcomes will match expected outcomes. As risk increases, variance often increases faster. Why? This happens because most people will fall closer to the middle of the normal distribution (discussed in the post linked at the beginning of the paragraph), but as risk increases, the number of people who are either that risky or are willing to take that risk are fewer and fewer (few will skydive, more will bungee jump, most will fly commercial). The fewer the number of people to whom a risk applies, greater the chances of variance (because the insurer has fewer people over whom to spread the risk). In other words, the law of large numbers works less effectively with small groups. With 1 million people, outcomes average out predictably, so let’s say you get the same or very similar number of claims every year. With 50 people, you might get zero claims one year and three claims the next—massive volatility.
I just want to be sure this is clear, so here is another example. Suppose two people pool their money every month, and decide that if one of them gets sick, the sick person can to use a certain percentage of the total money pooled (collected) by both of them to pay for the treatment. It is possible that for many years no one gets sick, but it is also possible that one (50%) of the total contributors or both (100% of the total contributors) get sick one day. On the other hand, in a pooled health insurance which has many contributors, say 1 million contributors, if 1 person gets sick, they are 1/1,000,000 of the total number of contributors (or 0.0001% of the pool- much, much less than 50%, right?).
Secondly, higher-risk individuals have more uncertain outcomes—meaning it’s harder to predict exactly what will happen. A skydiver faces multiple possible outcomes with varying probabilities: they could live unharmed, break bones, die from equipment failure, die from a heart attack mid-jump, or face other unpredictable complications. Each outcome has a different probability, making the overall risk calculation more complex. In contrast, a person simply walking on the ground faces far fewer potential causes of serious injury or death, so the range of possible outcomes (variance) is much narrower. Another way of looking at this is that a 30 year old healthy non smoker likely has fewer known causes of death historically than a 70 year old smoker.
This is why insurance premiums for risky people increase disproportionately:
The insurer must hold more capital to protect against bad luck.
A 30-year-old non-smoker with a 0.05% probability of death in a year might have a premium of ₹3,000.
A 60-year-old smoker with a 1% probability of death (20x higher) doesn’t pay 20x the premium (₹60,000). They pay 50x+ the premium (₹1,50,000 or more) because:
The absolute expected loss is 20x higher.
The variance around that expected loss is also much higher (more uncertainty about outcomes).
Insurers also worry about correlation—the risk that many claims happen simultaneously. A life insurer pricing individual deaths assumes they’re independent. But if a pandemic strikes, many policyholders might die at once. This correlation risk requires extra capital, adding to the risk loading.2324
Uncertainty When an insurer lacks information about a particular risk, they will charge more for it, because they do not know how potent the risk is, or how frequently it occurs.2526
Suppose a bank is deciding whether to lend to two borrowers, both with self-reported income of ₹10 lakhs per year.
Borrower A: A salaried employee with 10 years of bank statements, tax returns, and employer verification. The bank has rich information about their actual, consistent income.
Borrower B: A self-employed consultant with only 2 years of tax returns. Income has varied between ₹5 lakhs and ₹15 lakhs per year. The bank’s uncertainty about their true ability to repay is high.
Both might have estimated default probabilities of, say, 2% based on available data. But the bank will charge Borrower B a higher interest rate, not because their actual default probability is higher, but because the bank’s uncertainty about that probability is higher.
This principle explains all of the following:
Businesses in developed countries with strong financial reporting get cheaper capital than those in developing countries with weak disclosure.2728
Companies listed on stock exchanges get better rates than private companies (more transparency).29
Established firms in regulated industries get better rates than startups in emerging sectors.30
Therefore, the more standardised and measurable a risk, the cheaper it is to price and the lower the price demanded. Insurance for hazard risk (with centuries of actuarial data) is cheaper relative to coverage than climate insurance (with only decades of data).31 VaR models for market risk are widely accepted because market prices are observable. But there’s no standard model for reputational risk, so it’s not widely insured.32
This creates a system where:
Predictable, measurable, insurable risks get priced accurately and competitively.
Unpredictable, hard-to-measure risks are either:
Not insured at all (like most strategic risk).
Priced with huge margins because of the uncertainty (like reputational risk).
This is a profound source of inefficiency in capital allocation. Risks that are easiest to measure and quantify get the cheapest pricing and most capital. Risks that are hardest to measure—sometimes the ones that matter most—get starved of capital or don’t get priced at all.
A problem that has emerged from this is that historical models can simply not price tail risks (risks at the corners of normal distributions). An area this affects is climate risk, and its pricing.3334 A different example many of us lived through was the 2008-09 subprime financial crisis. In 2008, banks had calculated that simultaneous mortgage defaults across their portfolio should happen once every few thousand years. Yet it happened in 2007-2008. Why?35
The models went with historical data and assumed:
Housing prices wouldn’t decline nationwide (they always went up historically).36
Unemployment wouldn’t spike across industries simultaneously.37
But all three happened together, creating a “perfect storm” that the models had assigned nearly zero probability. The tail risk was real; the pricing was wrong. Financial institutions now conduct stress testing—asking, “What if housing prices fell 30%? What if unemployment doubled? What if credit markets froze?“—precisely because historical models miss these scenarios.
Thus, if a financial advisor saying “stocks haven’t crashed in 50 years, so the probability is very low” is engaging in tail risk underpricing, and yet, we do still use the method to price some kinds of risk. The next section talks about this and other methods of risk pricing.
Pricing different risks
Methodology 1: The Actuarial Approach (Hazard Risk)4 Insurance companies maintain vast databases of historical claims. For life insurance, they track millions of deaths by age, gender, health status, and lifestyle. For home insurance, they track fire and weather damage claims by location and property type. For auto insurance, they track accidents by driver age, vehicle type, and location. From this data, actuaries calculate frequency (how often does the event occur?) and severity (how much damage when it does?). The math relies on:
Having huge sample sizes (law of large numbers).
Accurate historical data (actuarial tables updated constantly).
Stable risk—the probability of death doesn’t change dramatically over time.
Why this works: Hazard risk has all these properties. Insurers have massive datasets, deaths are well-documented, and the probability of death doesn’t swing wildly year to year.
Why it fails: When underlying assumptions break, actuarial models fail. During COVID-19, mortality rates spiked unexpectedly, and life insurers faced massive losses. The historical tables became temporarily unreliable.
Methodology 2: The Credit Approach (Financial Risk)383940 Banks estimate the Probability of Default (PD) of a borrower. This comes from:
Credit ratings (developed from historical default rates of companies with similar characteristics).
Loan characteristics (collateral, loan-to-value ratio, term length).
They also estimate Loss Given Default (LGD)—how much money the bank recovers if the borrower defaults. If a borrower defaults on a ₹100 lakh loan backed by ₹60 lakhs of collateral, the LGD is 40%.
The interest rate spread (the premium above the risk-free rate) is then set approximately as:
Compensation for the risk that a government blocks or restricts cross-border payments, even if the borrower wants to pay
Different types of risk premiums that may be charged by banks on loans
Why this works: Credit markets are large and competitive. Banks have decades of default data. Collateral can be valued. PD and LGD can be estimated with reasonable accuracy.
Why it fails: When credit conditions change suddenly (as in 2008), the relationship between PD and actual defaults breaks. A borrower who seemed safe (PD 1%) might suddenly have a 20% probability of default if the economy collapses. This is called “correlation risk”—risks that seemed independent are actually correlated, and they all materialize simultaneously.
Suppose you hold a portfolio of Indian stocks worth ₹1 crore. You want to know your VaR at 95% confidence for one day.
Here’s how you calculate it:
Gather historical data: Look at how much your portfolio’s value changed each day over the past 5 years (roughly 1,250 trading days).
Calculate daily returns: On some days, your portfolio gained 2%. On others, it lost 3%. Most days, changes were small (±0.5%).
Rank all the losses: Sort all the daily changes from worst to best.
Worst day: -10% (₹10 lakh loss)
95% of days: losses were less than -7%
Typical days: ±1%
Identify the 95th percentile: Find the loss that was exceeded on only 5% of days (the worst 5% of outcomes). Let’s say this was -7%.
Your VaR is ₹7 lakhs.
What this means in plain English: “Based on historical patterns, we are 95% confident that on any given day, we won’t lose more than ₹7 lakhs. But on 1 out of every 20 days (5% of the time), we might lose more than this—possibly much more.”
How Banks Use VaR:
Banks use VaR for three main purposes:
Setting risk limits: “No trader can hold a position with VaR greater than ₹50 lakhs.”
Allocating capital: “This trading desk’s portfolio has VaR of ₹2 crore, so we must set aside ₹2 crore in capital to cover potential losses.”
Pricing risk: “We need to earn at least 10% return on our ₹2 crore capital (₹20 lakhs per year), so the portfolio must generate returns higher than the risk-free rate by at least this amount.”
Why this works: Market prices are observable and historical data is abundant. VaR is simple to calculate and widely understood.
Why it fails spectacularly: VaR assumes the future resembles the past. When it doesn’t—when a “tail risk” event occurs that’s much worse than historical data suggested—VaR provides false confidence. Black swan events—outliers far beyond historical norms—happen more often in real markets than VaR predicts. This is why sophisticated risk managers now conduct stress tests: “What if housing fell 30%? What if correlation across assets went to 1.0 (everything moves together)?” These scenarios often have probabilities that can’t be estimated from historical data.
Methodology 4: Reputational Risk Quantification16175556 Reputational risk is one of the hardest to price because reputation damage is:
Yet we know reputation has enormous value because research shows that roughly 26% of a company’s market value is directly attributable to its reputation.57 So how do we price something intangible?
The Stock Price Method: When a company announces a major negative event (fraud, scandal, product failure), the stock price falls. But often, the stock price falls more than the announced financial loss. The difference is the market’s estimate of reputational damage.
Reputation Risk Quantification Models that try to systematically price reputation risk:
Identify reputation threats: Product recalls, scandals, poor earnings, social media backlash
Estimate frequency: How often does each type of event happen in this industry?
Model financial impact: Customer loss, revenue decline, employee turnover costs
Quantify total effect: Project impact on profits over 3-5 years
However, unlike life insurance (centuries of death data) or credit risk (decades of default data), reputation damage is:
Context-dependent: The same scandal might destroy one company but barely hurt another
Hard to predict: Social media can amplify or diminish reputational harm unpredictably
Self-reinforcing: Initial reputation damage can trigger customer flight, making things worse
This is why most companies don’t buy reputation risk insurance:
Insurers can’t agree on how to price it
Coverage is extremely expensive when available
Policies have many exclusions
So reputation risk remains largely self-insured—companies must manage it through strong governance, ethical culture, and crisis response planning, but they can’t transfer it to an insurer the way they can with fire risk or credit risk.
Methodology 5: The Security Audit Approach (Cyber Risk)585960 Historically treated as operational risk, cyber risk is now often priced separately. Unlike traditional hazard risk (based on decades of historical data), cyber insurance prices risk based on current security posture. Insurers conduct security audits assessing:
Business context: Industry (finance = higher risk), revenue size, number of employees, data sensitivity.
Unlike traditional insurance (where you pay a fixed premium regardless of your actions), cyber insurance creates incentive alignment. Companies are rewarded for improving security. This is why cyber premiums vary so widely—from ₹80,000 to ₹3,00,000 for similar coverage, depending on security posture, so if the insured company becomes better prepared, its insurance premium can go down. The industry is evolving rapidly. As cyber threats evolve, pricing models are updated. Premiums spiked 80% in 2021-2022 (due to ransomware explosion) but have stabilized as companies improved controls and insurers refined models.
Methodology 6: Scenario Analysis (Strategic Risk)6162 Strategic risk is fundamentally different because:
Can’t be insured—no insurer will cover “your strategy might be wrong”
No historical data exists for “probability our specific strategy fails”
Each decision is unique—your market entry isn’t comparable to another company’s
Outcomes depend on management judgment, execution capability, and competitor actions
Instead of formulas, companies use scenario analysis—imagining multiple possible futures and testing strategy robustness across them.
The Process:
Step 1: Define the Current Strategy: Example: An e-commerce company currently selling books and electronics is considering expanding into furniture delivery.
Step 2: Imagine Alternative Futures (Scenarios): Scenario planning typically develops 3-5 scenarios representing different ways the future might unfold. Assign probabilities to different scenarios and how much loss your company would bear, for example, a company may have a scenario that
Step 3: Calculate Expected Value (With Huge Caveats).
Example:
Scenario A: “Competitive Onslaught”
3 major competitors enter within 18 months
Price war erupts, margins drop 20%
Company loses ₹50 crore over 3 years
Probability: 60%
Scenario B: “Logistics Nightmare”
Delivery complexity exceeds expectations
High return rates (15%)
Company loses ₹30 crore
Probability: 40%
Scenario C: “Weak Demand”
Market adoption slower than projected
Company loses ₹80 crore
Probability: 30%
Scenario D: “Success”
Market responds positively
Company gains ₹150 crore
Probability: 20%
Note: Probabilities don’t need to sum to 100% because scenarios aren’t mutually exclusive—multiple scenarios could occur simultaneously (e.g., you could face both competitive pressure AND logistics challenges).
Expected Outcome = (Probability of Scenario × Impact)
Why this works: Strategic risk isn’t insurable. There’s no historical data on “furniture market entry outcomes” for this specific company. Each strategic decision is somewhat unique. Organizations can’t buy insurance for strategic risk; they must manage it through planning, contingency analysis, and management judgment.
Why it fails: Scenarios often miss the most important surprises. In 2020, COVID-19 wasn’t in most companies’ scenarios. When reality diverges from scenarios, organizations must adapt on the fly. This is why CEOs, not risk managers, bear ultimate responsibility for strategic risk.
Chasing Tales 