Finrod Bites Wolves

E-waste – I: The Problem

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%.¹ ² 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
Consumer electronics: televisions, smartphones, tablets, gaming consoles
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%.⁸ ⁹ ¹⁰ 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:¹¹ ¹² ¹³

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.¹⁵ ¹⁶

Urban Mining
Electronics are concentrated sources of valuable materials—far more concentrated than their natural ore deposits:¹⁷ ¹⁸ ¹⁹

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.

Sources

Fear and Bravery in (Cricket) Decision-Making

NB: Ishan made me do this.

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.

Here are some key behavioral biases:⁶

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.

Sources

Cricket’s Solow growth story

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.² ³

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.⁴ ⁵ On the capital side, investment has risen sharply.⁶ ⁷ 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.² ³

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.¹⁵ ¹⁶

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.¹⁹ ²⁰ 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.³¹ ³² 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.

Sources

Cricket, Migration and Diasporic Communities – ResearchGate
Solow Residual: Definition, Example, vs. TFP – Investopedia
Solow Residual: Total Factor Productivity and the U.S. Economy – RSM Real Economy
A Statistical Look at How Cricket Has Changed Over the Past 30 Years: More Runs, Longer Careers, Fewer Breaks – ESPNcricinfo
International Boards, Franchises and the Future of Cricket Contracting – Lex Sportiva
Why Private Equity Loves Cricket: Deep Dive Into CVC Capital’s Investment – LinkedIn
$1.6 Billion for Two IPL Franchises: Does It Add Up? – ESPNcricinfo
How OTT Platforms Are Redefining Cricket Broadcasting During Asia’s Digital Revolution – LinkedIn
Creating Cricket for a Multi-Platform Viewership – Broadcast Pro ME
Biggest Cricket World Cup Ever Smashes Broadcast and Digital Records – ICC
Women’s Cricket in a League of Its Own – LinkedIn
Brand Support Grows for WPL After India’s World Cup Win – Women Entrepreneurs Review
The 2025 World Cup Promises to Take Women’s Cricket to Brand New Heights – ESPNcricinfo
Women’s Global Employment Report 2022 – FICA (PDF)
Cricket Confirmed for the LA28 Games – USA Cricket
MLC Gets Official List A Status from ICC Ahead of Second Season – ESPNcricinfo
All 20 Teams for 2026 T20 World Cup Decided – Kathmandu Post
T20 Cricket Confirmed as One of Five New Sports at LA28 – ESPNcricinfo
IPL Business Model in India: A Comprehensive Overview – Avira Digital Studios
The IPL Business Model: A Deep Dive into Revenue Streams – Chase Your Sport
Leagues like ILT20 ‘Not Good for the Game’ – Graeme Smith Talks SA20 Investment in Local Cricket – ESPN
ICC Global Funding Model Explainer – Emerging Cricket
ICC Launches Multi-Faceted Pathway Events Tender – SportCal
ICC to Sell Next Media Rights for Indian Market and Men’s and Women’s Events Separately – ESPNcricinfo
In-Depth with ICC Media Rights Head Manoharan – SportCal
Women’s Cricket Rights Values and Coverage Levels Entering New Age – SportCal
New ICC Finance Model Breaks Up Big Three – ESPNcricinfo
Enshrining The Might Of The BCCI: Inside the TV Deals That Made Cricket Richer and Less Equal Than Ever Before – Wisden
Cricket’s Imbalanced Financial Structure Continues to Favor the Wealthy – Arab News
ICC Revenue Model Threatens Growth of Game, Say Associate Members – Indian Express
Is Cricket’s Scheduling Problem Beyond Redemption? – Arab News
Cricket Needs a More Equitable Spread of International Fixtures – ESPN

Can AI be a new economic factor of production?

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:¹ ² ³

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.

GDP today is built from three equivalent views:⁵ ⁶

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:⁷ ⁸

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.

Sources

Financing Climate Solutions – VI: Mechanisms

This is a quick post explaining the various common types of green finance mechanisms.

Financial Instruments¹ ² ³ ⁴ ⁵ ⁶
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:

1. Carbon Credits⁶ ⁹

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.

2. Green bonds¹⁰ ¹¹

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.

3. Blue Bonds¹² ¹³

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.

4. Sustainability‑linked bonds (SLBs)¹ ¹⁴

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.

5. Transition and Climate Transition bonds¹⁵ ¹⁶

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.

6. Blended finance¹⁷ ¹⁸ ¹⁹

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.

7. Results‑based climate finance (RBCF)²⁰ ²¹

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.

8. Concessional loans & grants²² ²³

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.

9. Guarantees²⁴ ²⁵

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.

10. Multilateral climate funds²⁶ ²⁷ ²⁸

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.

11. Debt‑for‑Climate swaps²⁹ ³⁰

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).

12. Carbon pricing & CBAM‑linked flows ³¹ ³²

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).

13. AMCs for CDR³³ ³⁴

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.

14. Parametric insurance³⁵ ³⁶ ³⁷

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.

15. Islamic green sukuk³⁸ ³⁹

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.

16. Crowdfunding platforms⁴⁰ ⁴¹

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.

Sources

A Bayesian view of cricket’s player of series monsters

Imagine this:

Sometimes it rains, sometimes it doesn’t.
You notice that the ground is wet.

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.

This is the formula (don’t panic): $P(A∣B) = [P(B∣A)×P(A)] ÷ P(B)$ ¹ ²

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)

or P(Virat Century|India Win) = [P(Century) × P(India Win| Virat Century)] / P(India Win)

P(Virat century∣India wins)= (0.8×0.4) / 0.70 ≈ 0.457 = 45.7%

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.

Bayes USING REAL NUMBERS
When the team won, how often was this player the reason?

Player	Team	Definition of W	Definition of C	P(W) base win%³	P(C) frequency of centuries	P(W \| C) centuries in wins⁴
Kohli	India (ODI)	India win when Kohli in XI	Kohli scores ODI century	0.616	~0.18 (1 per 5.65 inns)	~0.83 (44 of 53 hundreds)
Tendulkar	India (ODI)	India win when Tendulkar in XI	Tendulkar scores ODI century	0.505	~0.11 (1 per 9.22 inns)	~0.67 (33 of 49 hundreds)
Shakib⁵	Bangladesh (ODI)	Bangladesh win (overall ODI record)	Shakib scores ODI century (bat)	~0.36	~0.03 (7 in 234 inns)	~0.77 (7 of first 9 tons)

Player details⁶ ⁷

Here’s the Bayes calculation:

Player	Team	P(W) base win%	P(C)	P(W \| C)	P(C \| W) calculated	Interpretation
Kohli	India (ODI)	0.616	0.18	0.83	0.24 (24%)	~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.

Sources

GHG 101 – III What is a Carbon Negative Nation?

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).² ³ 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:⁹ ¹⁰

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.¹⁶ ¹⁷

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.

Risk – III: Pricing Risk

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.¹ ² ³

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:

RISK TYPE	DEFINITION	CHARACTERISTICS	EXAMPLES
HAZARD RISK (Pure Risk)⁵ ⁶	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
OPERATIONAL RISK⁷ ⁸ ⁹ ¹⁰	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 Fraud: Employee embezzles funds; Vendor submits fake invoices; Internal collusion to bypass controls External Events: Natural disaster destroys office; Key supplier suddenly defaults; Cyberattack from external actor
FINANCIAL RISK¹¹ ¹² ¹³	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)
STRATEGIC RISK¹⁴	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
COMPLIANCE & REGULATORY RISK¹⁵	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 Contract & Market Conduct: False advertising; Market manipulation; Insider trading; Misleading disclosures Employment & Safety: Labor law violations; Health and safety violations; Harassment and discrimination Industry-Specific: Healthcare regulations (HIPAA); Financial regulations (Banking Acts); Environmental regulations
REPUTATIONAL RISK¹⁶ ¹⁷	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 Workplace Issues: Harassment scandals; Discrimination claims; Executive misconduct Environmental/Social: Oil spills; Labor exploitation; Pollution incidents
CYBER & TECHNOLOGY RISK¹⁸ ¹⁹	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 Insider Threats: Disgruntled employee steals data; System administrator sabotages operations; Contractor misuses access

Different types of risks

Each of these types of risks attracts different prices. Here’s another table:

RISK TYPE	DEFINITION	PRICING CHALLENGE	KEY INSIGHT
HAZARD RISK (Pure Risk)⁵ ⁶	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.
OPERATIONAL RISK⁷ ⁸ ⁹ ¹⁰	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.
FINANCIAL RISK¹¹ ¹² ¹³	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.
STRATEGIC RISK¹⁴	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.
COMPLIANCE & REGULATORY RISK¹⁵	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.
REPUTATIONAL RISK¹⁶ ¹⁷	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.
CYBER & TECHNOLOGY RISK¹⁸ ¹⁹	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.

Pricing different types of risks

General principles of pricing risk
People react in different ways to risk. Some of us prefer the straight and narrow and others don’t think much of doing things that would be considered too risky by others- think of how some don’t mind skydiving, whereas others prefer their feet firmly on the ground. There are risks associated with both skydiving, and staying on the Earth, but different people like different things.

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 Formula²⁰ ²¹
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.²³ ²⁴

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.²⁵ ²⁶

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.²⁷ ²⁸
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.³³ ³⁴ 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
Banks wouldn’t stop lending to each other.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)³⁸ ³⁹ ⁴⁰
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).
Credit scores (statistical models predicting default probability).
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:

Interest Rate = Risk-Free Rate + (PD × LGD + Risk Loading) + Liquidity Premium + Other Premiums⁴¹

Other premiums:

Risk Premium	Explanation
Credit Risk Premium⁴²	Compensation for the probability that the borrower defaults and the amount the lender loses if they do (PD × LGD)
Liquidity Premium⁴³	Compensation for holding an asset that is difficult to sell quickly (e.g., corporate loans are less liquid than government bonds)
Inflation Risk Premium⁴⁴	Compensation for uncertainty about future inflation; if inflation is higher than expected, the real value of repayments falls
Term Premium⁴⁴	Compensation for lending money for longer periods; longer loans have more uncertainty about interest rates and borrower circumstances
Currency Risk Premium⁴⁵	Compensation for the risk that exchange rates move unfavorably; relevant when borrowing in a foreign currency
Sovereign Risk Premium⁴⁶	Compensation for political and economic instability in the borrower’s country; reflects country-level risk beyond individual borrower risk
Regulatory Risk Premium⁴⁷	Compensation for the risk that changes in laws or regulations will harm the lender’s position
Prepayment Risk Premium⁴⁸	Compensation for the risk that the borrower repays early (often when interest rates fall), causing the lender to reinvest at lower rates
Concentration Risk Premium⁴⁹	Compensation for lending a large amount to a single borrower or sector, which increases the lender’s exposure
Call Risk Premium⁵⁰	Compensation for the risk that the bond issuer redeems the bond early, leaving investors with reinvestment risk
Event Risk Premium⁵¹	Compensation for the risk of specific one-off events (mergers, leveraged buyouts, natural disasters) that suddenly change creditworthiness
Convertibility Risk Premium ⁴⁸	Compensation for the risk that capital controls or currency restrictions prevent conversion to foreign currency
Transfer Risk Premium⁵²	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.

Methodology 3: Value at Risk (Market Risk)⁵³ ⁵⁴
When investment banks, traders, and portfolio managers hold stocks, bonds, or other financial assets, they face a fundamental question: “How much could we lose on a bad day?” Value at Risk (VaR) answers this question: “What’s the maximum loss I might suffer with 95% confidence over a given time period (usually one day)?”

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 Quantification¹⁶ ¹⁷ ⁵⁵ ⁵⁶
Reputational risk is one of the hardest to price because reputation damage is:

Invisible until it happens
Subjective (how much is brand trust worth?)
Interconnected (affects customers, employees, investors, suppliers simultaneously)

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)⁵⁸ ⁵⁹ ⁶⁰
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.
Technical controls: Firewalls, intrusion detection, endpoint protection, multi-factor authentication.
Process maturity: Patch management, vulnerability assessment, incident response plans.
Compliance: Certifications like ISO 27001 or NIST Cybersecurity Framework.
Training: Employee security awareness, phishing simulations.

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)⁶¹ ⁶²
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)

= (0.6 × -₹50cr) + (0.4 × -₹30cr) + (0.3 × -₹80cr) + (0.2 × +₹150cr)
= -₹30cr – ₹12cr – ₹24cr + ₹30cr
= -₹36 crore expected loss

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.

Sources

GHG Accounting: ISO 14064-1

Note: I know this is quite technical, but it’s about accounting, so that’s natural. Financial accounting tends to be technical too, right?

The ISO 14064 series is a family of international standards by the International Organization for Standardization (ISO) for quantification, monitoring, reporting, and verification of GHG emissions. They were developed by Technical Committee ISO/TC 207 on Environmental Management, Subcommittee SC 7 on Greenhouse Gas Management, can be adopted across different sectors, regions, and organisational types.

The ISO 14064 series currently comprises four main parts:

ISO 14064-1:2018 – “Greenhouse gases – Part 1: Specification with guidance at the organisation level for quantification and reporting of greenhouse gas emissions and removals.” This standard enables organisations to measure and report their total greenhouse gas emissions and removals.
ISO 14064-2:2019 – “Greenhouse gases – Part 2: Specification with guidance at the project level for quantification, monitoring and reporting of greenhouse gas emission reductions or removal enhancements.” This standard applies to specific projects designed to reduce emissions or enhance carbon removals, such as renewable energy installations, energy efficiency retrofits, reforestation programs, or methane capture projects.
ISO 14064-3:2019 – “Greenhouse gases – Part 3: Specification with guidance for the verification and validation of greenhouse gas statements.” This standard provides the framework for independent third-party verification and validation of GHG claims. It is the assurance mechanism that gives stakeholders confidence in reported emissions data.
ISO/TS 14064-4:2025 – “Greenhouse gases – Part 4: Guidance for the application of ISO 14064-1.” This newest addition, published in November 2025, is a Technical Specification that provides practical, step-by-step guidance for implementing ISO 14064-1. It bridges the gap between the normative requirements of the standard and real-world application, with detailed examples and case studies for different organisational types and sectors.

Additionally, the broader ISO 14060 family includes ISO 14065:2020 (requirements for bodies validating and verifying GHG statements), ISO 14066:2023 (competence requirements for verifiers and validators), and ISO 14067:2018 (carbon footprint of products).

This ecosystem of standards creates a framework:

Organisations use ISO 14064-1 and 14064-4 to calculate their emissions;
Project developers use ISO 14064-2 to quantify project benefits;
Independent verifiers use ISO 14064-3 to audit these claims; and a
Accreditation bodies use ISO 14065 and 14066 to ensure the competence and impartiality of the verifiers themselves.

The Five Core Principles

Relevance: Select the GHG sources, GHG sinks, GHG reservoirs, data and methodologies appropriate to the needs of the intended user.
Completeness: Include all relevant GHG emissions and removals.
Consistency: Enable meaningful comparisons in GHG-related information.
Accuracy: Reduce bias and uncertainties as far as is practical.
Transparency: Disclose sufficient and appropriate GHG-related information to allow intended users to make decisions with reasonable confidence.

As stated explicitly in ISO 14064-1, “The application of principles is fundamental to ensure that GHG-related information is a true and fair account. The principles are the basis for, and will guide the application of, the requirements in this document”.

Relevance: Appropriateness to User Needs
This principle recognises that GHG inventories and reports serve specific purposes and must be designed to meet the needs of those who will rely on the information to make decisions.

Relevance begins with clearly identifying the intended users of the GHG inventory and understanding their information needs. Intended users may include the organisation’s own management, investors, lenders, customers, regulators, GHG programme administrators, or other stakeholders. Different users may have different information needs. For example, investors may focus primarily on climate-related financial risks and opportunities, while regulators may require specific emissions data for compliance purposes.

The relevance principle requires organisations to make appropriate boundary decisions (determining which operations, facilities, and emissions sources to include in the inventory based on what is material and meaningful to intended users): an inventory that excludes significant emission sources or includes irrelevant information fails to serve user needs effectively.

In practice, applying the relevance principle means that organisations must engage with their stakeholders to understand what information they need and why, design inventory boundaries and methodologies to provide this information, focus effort on quantifying the most significant emissions sources, and regularly reassess whether the inventory continues to meet user needs as circumstances change.

Completeness: Including All Relevant Emissions
The completeness principle requires organisations to include all relevant GHG emissions and removals within the chosen inventory boundaries. This principle ensures that GHG inventories provide a comprehensive picture of an organisation’s climate impact rather than selectively reporting only favorable information.

Completeness operates at multiple levels. At the broadest level, it requires that organisations establish appropriate organisational and reporting boundaries and then include all sources and sinks within those boundaries. For organisational-level inventories under ISO 14064-1, this means accounting for all facilities and operations that fall within the defined organisational boundary, whether based on control or equity share. It also means including both direct emissions from sources owned or controlled by the organisation and indirect emissions that are consequences of organisational activities.

The 2018 revision fundamentally changed how organizations handle indirect emissions. Instead of treating “Scope 3” as a monolithic category, ISO now requires systematic evaluation across six specific categories. This shift reflects reality: a manufacturer’s supply chain emissions (Category 4) and product use-phase emissions (Category 5) are fundamentally different and require different strategies. Organisations must systematically identify potential sources of indirect emissions throughout their value chains and include those that are determined to be significant based on magnitude, influence, risk, and stakeholder concerns. The real problem here is data availability: an organisation might know its own production emissions precisely, but will struggle to get Scope 3 data from thousands of distributors, and this makes implementation messy and imprecise.

An important aspect of completeness is the treatment of exclusions. If specific emissions sources or greenhouse gases are excluded from the inventory, ISO 14064-1 requires organisations to disclose and justify these exclusions. Justifications must be based on legitimate reasons such as immateriality, lack of influence, or technical measurement challenges, not simply on a desire to report lower emissions.

For GHG projects under ISO 14064-2, completeness requires identifying and quantifying emissions and removals from all relevant sources, sinks, and reservoirs affected by the project, including controlled, related, and affected SSRs. Failure to account for emission increases from affected sources (often called leakage) would result in overstatement of project benefits.

Consistency: Enabling Meaningful Comparisons
The consistency principle requires that organisations enable meaningful comparisons in GHG-related information over time and, where relevant, across organisations. Consistency is essential for tracking progress toward emission reduction targets, assessing the effectiveness of mitigation initiatives, and enabling external stakeholders to compare performance across organisations or sectors.

Consistency has several dimensions. It requires using consistent methodologies, boundaries, and assumptions over time when quantifying and reporting emissions. When an organisation measures its emissions in one year using specific methodologies and emission factors, it should apply the same approaches in subsequent years to enable valid comparisons.

It is important to note that consistency does not mean organisations can never improve their methodologies or expand their boundaries. Organisations may and should refine their approaches over time to improve accuracy, expand scope, or respond to changing circumstances. However, when such changes occur, consistency requires transparent documentation of what changed and why, recalculation of prior years where necessary to maintain comparability, and clear explanation in reports so users understand the nature and impact of changes.

Case in point, the base year concept embodied in ISO 14064-1 is central to applying the consistency principle. Organisations select a specific historical period as their base year against which future emissions are compared. The base year serves as the reference point for measuring progress toward reduction targets. ISO 14064-1 requires organisations to establish policies for recalculating base year emissions when significant changes occur to organisational structure, boundaries, methodologies, or discovered errors. These recalculation policies ensure that year-over-year comparisons remain valid even as organisations evolve.

The recalculation policy is most commonly triggered by three types of organisational change. First, structural changes: acquisitions, divestitures, or mergers that materially alter the scope of operations. ISO 14064-1 and the GHG Protocol typically define “material” as changes exceeding 5% of Scope 1 and Scope 2 emissions in the base year. For example, if a retail company acquires a logistics provider representing an additional 6% of historical emissions, the base year must be recalculated to include that logistics provider, enabling fair year-on-year comparison. Second, methodology improvements: when an organisation discovers better data or more appropriate emission factors. If a facility previously used regional electricity emission factors but gains access to grid-specific data, or if a company previously estimated employee commuting emissions using averages but now collects actual commute data, these improvements warrant recalculation. The driver is not change for its own sake, but the principle that prior years should benefit from improved accuracy just as current years do. Third, discovered errors: when an organisation identifies that prior-year calculations were systematically wrong—either over or understating emissions—recalculation is not optional; it is mandatory. Transparency requires disclosing both the error and its magnitude, then correcting the historical record. Organisations often establish a threshold (commonly 5%) below which minor corrections do not trigger full recalculation; instead, they are noted as adjustments in the current year.

Accuracy: Reducing Bias and Uncertainty
Accuracy involves reducing systematic bias and reducing uncertainty.

Systematic bias occurs when quantification methods consistently overstate or understate actual emissions. For example, using an emission factor that is inappropriately high or low for the specific activity being quantified would introduce bias. The accuracy principle requires ensuring that quantification approaches are systematically neither over nor under actual emissions, as far as can be judged.
Uncertainty refers to the range of possible values that could be reasonably attributed to a quantified amount. All emission estimates involve some degree of uncertainty arising from measurement imprecision, estimation methods, sampling approaches, lack of complete data, or natural variability. The accuracy principle requires reducing these uncertainties as far as is practical through using high-quality data, appropriate methodologies, and robust measurement and calculation procedures. ISO 14064-1 requires organisations to assess uncertainty in their GHG inventories, providing both quantitative estimates of the likely range of values and qualitative descriptions of the causes of uncertainty. This assessment helps organisations identify where improvements in data quality or methodology could most effectively reduce overall inventory uncertainty.

Achieving accuracy begins with selecting appropriate quantification approaches. ISO 14064-1 recognises multiple approaches to quantification, including direct measurement of emissions, mass balance calculations, and activity-based calculations using emission factors. The most accurate approach depends on the specific source, data availability, and the significance of the emission source.

Organisations should also prioritise primary data (data obtained from direct measurement or calculation based on direct measurements) over secondary data from generic databases. Site-specific data obtained within the organisational boundary is preferable to industry-average or regional data. However, the accuracy principle also recognises practical constraints—perfect accuracy is often unachievable and unnecessary, particularly for minor emission sources.

The requirement to separately report biogenic CO₂ from fossil fuel CO₂ in Category 1 may seem like a technical distinction, but it reflects a fundamental policy divergence emerging globally. Biogenic emissions arise from the combustion of biomass (wood, agricultural waste, biogas) and are considered part of the natural carbon cycle—the carbon released was recently absorbed by growing plants or waste decomposition. Fossil emissions, by contrast, release carbon that has been sequestered for millions of years. Regulatory frameworks increasingly treat these differently. The European Union’s Emissions Trading System (EU ETS) has updated its carbon accounting rules multiple times to refine biogenic CO₂ treatment; the GHG Protocol has issued separate guidance; and emerging carbon credit schemes apply different rules depending on biogenic versus fossil origin. An organisation that reports these separately today is insulated from tomorrow’s regulatory changes. If a company bundles biogenic and fossil emissions together, it cannot easily disaggregate them later without recalculating historical data. Practically, this means a biomass energy facility, a wastewater treatment plant using anaerobic digestion, or a manufacturer using wood waste for process heat must track biogenic emissions in their systems from the outset.

Transparency: Disclosing Sufficient Information
The transparency principle requires that organisations disclose sufficient and appropriate GHG-related information to allow intended users to make decisions with reasonable confidence. Transparency is fundamental to building trust and credibility in GHG reporting—it enables users to understand what was measured, how it was measured, and what limitations exist in the reported information.

Transparency requires that organisations address all relevant issues in a factual and coherent manner, based on a clear audit trail. This means documenting the assumptions, methodologies, data sources, and calculations used to quantify emissions such that an independent party could understand and reproduce the results.

The transparency principle requires that a reader—whether a regulator, investor, or internal stakeholder—could theoretically follow the same calculation path and reach the same answer. This demands more than good intentions; it requires structural discipline in documentation. In practice, an effective audit trail captures the decision journey, not just the numbers. It documents: which emissions sources were identified as material (and why), which were excluded (and why), what data was collected and from which sources, which assumptions were necessary (e.g., assumed product lifespans, allocation methods for shared facilities), what methodologies were applied, and crucially, where uncertainty remains. For example, a beverage manufacturer’s Scope 3 inventory might document that it obtained actual emissions data from 60% of direct suppliers (by volume) but relied on industry-average factors for the remaining 40%. That gap is not hidden; it is documented as a source of uncertainty in the overall inventory. This approach serves two audiences simultaneously. Internal management gains confidence that the number is defensible. External verifiers and stakeholders understand the methodology’s strengths and limitations, enabling better-informed decisions.

A clear audit trail is essential to transparency. Organisations should maintain robust documentation that traces emissions from source data through calculations to final reported totals. This documentation should include:

descriptions of organisational and reporting boundaries;
lists of emission sources and sinks included in the inventory;
methodologies and emission factors used for each source category;
activity data, sources of data, and data collection procedures;
calculations and any assumptions made; and
any exclusions and the justifications for excluding specific sources.

Transparency requires disclosing not only the final emission totals but also the information needed to understand and evaluate those totals. ISO 14064-1 specifies extensive requirements for what must be included in GHG reports, including both mandatory and recommended disclosures. These disclosures cover methodological choices, data quality, uncertainty, significant changes from previous years, verification status, and other information relevant to interpreting the reported emissions.

The transparency principle also requires acknowledging limitations and uncertainties in the reported information. Rather than implying false precision, organisations should clearly communicate where significant uncertainties exist, what assumptions were necessary, and what information was unavailable or excluded. This honest acknowledgment of limitations enhances rather than diminishes credibility, as it demonstrates rigorous and objective assessment.

Establishing Organisational Boundaries
The first step in developing a GHG inventory is determining organisational boundaries, which means that the organisation should define what operations, facilities, and entities are included in the inventory based on the organisation’s relationship to them.

ISO 14064-1 allows organisations to choose from two primary consolidation approaches:

Equity share approach: The organisation accounts for its proportional share of GHG emissions and removals from facilities based on its ownership percentage. The equity share reflects economic interest, which is the extent of rights a company has to the risks and rewards flowing from an operation. Typically, the share of economic risks and rewards in an operation is aligned with the company’s percentage ownership of that operation, and equity share will normally be the same as the ownership percentage. Where this is not the case, the economic substance of the relationship the company has with the operation always overrides the legal ownership form to ensure that equity share reflects the percentage of economic interest.
Control approach (financial or operational): The organisation accounts for 100% of GHG emissions and removals from facilities over which it has financial or operational control, and 0% from facilities it does not control.
- Under the operational control approach, an organisation has operational control over a facility if the organisation or one of its subsidiaries has the authority to introduce and implement its operating policies at the facility. This is the most common approach, as it typically aligns best with what an organisation feels it is responsible for and often leads to the most comprehensive inclusion of assets in the inventory.
- Under the financial control approach, an organisation has financial control over a facility if the organisation has the ability to direct the financial and operating policies of the facility with a view to gaining economic benefits from its activities. Industries with complex ownership structures may be more likely to follow the equity share approach to align the reporting boundary with stakeholder interests.

The choice of consolidation approach should be consistent with the intended use of the inventory and ideally align with how the organisation consolidates financial information. For example, an organisation that consolidates its financial statements based on operational control should typically use operational control for GHG inventory boundaries as well.

Boundary Consistency with Financial Reporting: Why It Matters
The ISO standard recommends (and increasingly, regulators require) that the consolidation approach used for GHG accounting align with the approach used for financial reporting. This is more than administrative convenience. When a company consolidates financial statements using operational control, its financial stakeholders are accustomed to seeing 100% of controlled operations reflected in results. If the GHG inventory uses a different boundary—say, equity share for a joint venture while the finance team uses operational control—the GHG data will seem inconsistent and raise credibility questions. More importantly, alignment simplifies assurance. An auditor examining both financial and GHG statements does not have to reconcile conflicting boundary interpretations. A company that uses control for finance but equity share for emissions is signalling (intentionally or not) that its GHG report is using a narrower or broader lens than its financial results, inviting scrutiny about whether the difference is justified or opportunistic. Alignment also supports integrated reporting. Increasingly, investors want to see how GHG emissions correlate with financial performance—emissions intensity (tonnes CO₂e per unit of revenue, per unit of asset, per FTE), carbon risk premium, or abatement costs. These correlations only make sense if the boundary is consistent.

Defining Reporting Boundaries: The Six-Category Structure
Once organisational boundaries are established, organisations must define their reporting boundaries—what types of emissions and removals are quantified and reported within the organisational boundary.

The 2018 revision of ISO 14064-1 introduced a significant innovation: a six-category structure for classifying emissions and removals. This structure evolved from and builds upon the GHG Protocol’s three-scope approach (Scope 1 for direct emissions, Scope 2 for energy indirect emissions, Scope 3 for all other indirect emissions). The ISO categories provide more granular classification of indirect emissions, facilitating identification and management of specific emission sources throughout the value chain.

Category 1: Direct GHG emissions and removals: Direct GHG emissions are emissions from GHG sources owned or controlled by the organisation. These are emissions that occur from operations under the organisation’s direct control—for example, emissions from combustion of fuels in company-owned vehicles or boilers, emissions from industrial processes at company facilities, or fugitive emissions from refrigeration equipment owned by the company. Organisations must quantify direct GHG emissions separately for CO₂, CH₄, N₂O, NF₃, SF₆, and other fluorinated gases. Additionally, ISO 14064-1 requires organisations to report biogenic CO₂ emissions separately from fossil fuel CO₂ emissions in Category 1. This separate reporting recognises that biogenic emissions may have different policy treatments, impacts, and implications than fossil emissions.

Category 2: Indirect GHG emissions from imported energy: This category includes indirect emissions from the generation of imported electricity, steam, heat, or cooling consumed by the organisation. When an organisation purchases electricity, the emissions from generating that electricity occur at the power plant (not owned by the organisation), but they are a consequence of the organisation’s decision to purchase and consume electricity. ISO 14064-1 requires organisations to report all Category 2 emissions, making this a mandatory category alongside Category 1.

Category 3: Indirect GHG emissions from transportation: This category includes emissions from transportation services used by the organisation but operated by third parties. Examples include emissions from business travel on commercial airlines, shipping of products by third-party logistics providers, and employee commuting.

Category 4: Indirect GHG emissions from products used by the organisation: This category includes emissions that occur during the production, transportation, and disposal of goods purchased by the organisation. Examples include emissions from the manufacturing of products the organisation buys, emissions from transporting materials used to make those products, and emissions from disposing of waste created by using those products. The boundary for Category 4 is “cradle-to-gate” from the supplier’s perspective—all emissions associated with producing and delivering products to the organisation.

Category 5: Indirect GHG emissions associated with the use of products from the organisation: This category includes emissions generated by the use and end-of-life treatment of the organisation’s products after their sale. When certain data on products’ final destination is not available, organisations develop plausible scenarios for each product. This category is particularly significant for manufacturers, as use-phase emissions from products often exceed emissions from manufacturing. For example, the emissions from operating a vehicle over its lifetime typically far exceed the emissions from manufacturing it.

For many product-based companies, Category 5 is the elephant in the room. An automotive manufacturer might account for 15–20% of its footprint in manufacturing emissions (Category 1) and another 10% in supply chain emissions (Category 4), but 50%+ in the use phase (Category 5). A household appliance manufacturer faces a similar dynamic—the electricity consumed by an appliance over its 15-year lifespan vastly exceeds the emissions from manufacturing. This creates strategic tension. The organisation has direct control over manufacturing efficiency—it can redesign processes, source renewable energy, or substitute materials. But use-phase emissions depend on the consumer’s electricity grid (which it does not control) and user behaviour (how often and how long the appliance runs). Yet ISO 14064-1 requires organisations to quantify these use-phase emissions and report them transparently, because stakeholders—particularly investors and policymakers—need to understand the full climate footprint of the products being sold. When data on product final destination is unavailable (e.g., a smartphone manufacturer doesn’t know where each unit is sold, or how long consumers keep it), ISO 14064-1 allows organisations to develop “plausible scenarios”—reasonable assumptions about usage patterns, product lifetime, and grid composition. These scenarios must be documented and justified, and they should be reassessed as more data becomes available or as circumstances change (e.g., grid decarbonisation).

Category 6: Indirect GHG emissions from other sources: This category captures any indirect emissions that do not fall into Categories 2-5. It serves as a catch-all to ensure completeness while avoiding double-counting. Organisations must be careful not to count the same emissions in multiple categories—for example, if emissions from a vehicle are included in Category 3 (transportation), they should not also be included in Category 4 (products) if the vehicle was used to transport a product.

Quantifying Emissions: Global Warming Potential and CO₂ Equivalent

Risk – II: ISO 31000:2018 as applied to Indian cricket

TL;DR, because this is not a post for cricket casuals:

Fog in North India in December, heat waves in April, election clashes, and security disruptions are predictable risks, not bad luck.
Indian cricket continues to treat these as isolated incidents rather than as interconnected system-level risks that cascade across scheduling, logistics, player welfare, and revenue.
The BCCI now runs a ₹20,000-crore ecosystem, yet lacks a transparent, enterprise-wide risk management framework appropriate to that scale.
Global sports bodies manage similar uncertainties using formal risk frameworks (e.g., ISO 31000) to decide what risks to avoid, mitigate, insure, or accept.
Applying ISO 31000 to Indian cricket shows that systematic risk management would cost far less than repeated disruptions, cancellations, and credibility damage.
At this scale, ad-hoc risk management is not neutral—it is value-destructive.

And now onto the post.

This post has been inspired by watching the BCCI schedule summer matches in tropical South India, and winter season matches in our smoggy chilled North. Watching Indian cricketers roam about in Lucknow against South Africa while wearing pollution masks while broadcasters told us match was delayed due to low visibility conditions made me wonder what other risks BCCI could just avoid, or at least manage better.

These risks are predictable. ~~Fog~~Smog in North India in December isn’t a surprise. Heat waves in April aren’t black swans. Even geopolitical and security disruptions, while unpredictable, follow recognisable patterns. Yet Indian cricket continues to treat these as isolated “incidents” rather than as interconnected risks that can be anticipated, priced, and managed.

This is not about fog or heat. It’s about running a ₹20,000-crore system without an enterprise risk framework. So I’m doing an ISO 31000 evaluation for the BCCI. FOR FREE. Please someone share this with anyone influential in the BCCI.

Here’s a non-comprehensive list of some risk sources and events that can happen. You can skim through it if you like, I know it’s long, which already tells you lots:

Risk Category	Specific Risk	Example/Evidence	Risk Source	Impact Area
Geopolitical & Security	Cross-border conflict/military escalation	IPL 2025 suspension due to India-Pakistan tensions (May 2025)¹	Political/regulatory external context	Tournament suspension, revenue loss, player safety concerns
Geopolitical & Security	Communal/religious tensions	Mustafizur Rahman threats from Ujjain religious leaders (Dec 2025);²	Social/political external context	Player threats, stadium disruptions, player unavailability
Geopolitical & Security	Terrorism/security incidents	Potential attack on stadium or traveling teams	Security threat external context	Deaths/injuries, event cancellation, insurance claims
Weather & Climate	Dense fog	Lucknow T20I abandoned without a ball (Dec 17, 2025);³	Natural hazard/environmental	Match cancellation, travel disruptions, schedule compression
Weather & Climate	Extreme heat	Player heat exhaustion risks, crowd attendance decline	Environmental/climate change	Player health, match timing changes, spectator safety
Weather & Climate	Flooding/waterlogging	Monsoon season pitch damage, venue inaccessibility	Environmental/climate change	Venue unusability, match postponement, ground preparation delays
Weather & Climate	Drought	Groundwater depletion affecting pitch maintenance	Environmental/climate change	Pitch quality degradation, venue unusability
Weather & Climate	Severe storms/hailstorms	Potential infrastructure damage, match disruption	Environmental natural hazard	Venue damage, match abandonment, spectator safety
Operational & Logistics	Flight/travel cancellations	Flights cancelled across northern India^{(just search it, happens bi-weekly in December)}	Transportation system failure	Team travel delays, venue setup issues, player unavailability
Operational & Logistics	Equipment/supply disruption	Medical supplies, nutrition goods, cricket equipment delays to venues	Supply chain vulnerability	Player preparation delays, competitive disadvantage, safety risks
Operational & Logistics	Transportation of spectators	Mass transit failures, road congestion, parking unavailability	Infrastructure/logistics	Spectator attendance decline, safety concerns, venue capacity underutilization
Operational & Logistics	Accommodation unavailability	Limited hotel capacity during tournament, staff housing issues	Supply/demand mismatch	Team comfort degradation, player fatigue, franchise cost overruns
Venue & Infrastructure	Poor crowd management systems	Chinnaswamy stampede⁴	Operational/design vulnerability	Spectator casualties, reputational damage, regulatory action, venue unusability
Venue & Infrastructure	Structural deterioration	Aging concrete, roof damage, electrical system failures	Asset maintenance gap	Venue closure, safety risk, remediation costs
Venue & Infrastructure	Inadequate emergency response systems	Poor medical facilities, limited ambulance access, untrained staff	System design gap	Casualties during medical emergencies, litigation
Financial	Broadcasting rights disruption	Disney+ Hotstar and Star Sports unable to broadcast during IPL suspension	External event affecting revenue	Revenue loss for franchises/broadcasters (₹crores per day), contractual disputes
Financial	Sponsor withdrawal/advertising rate decline	Potential sponsorship cancellations due to event suspension or negative publicity	Market condition/risk perception	Franchise revenue decline, reduced capital for player wages
Financial	Insurance claims disputes	Ambiguous “war” and “riot” clauses limiting payout eligibility⁵	Contractual/insurance gap	Uncompensated losses during suspension or disruption
Financial	Currency fluctuation	Overseas player contracts, broadcast payment variability	Market/exchange rate risk	Player cost increases, sponsor revenue volatility
Financial	Franchise profitability uncertainty	Rising costs (venue, insurance, player wages) versus volatile revenue (attendance, viewership)	Business model vulnerability	Franchise owner losses, potential team withdrawal
Corruption & Integrity	Match-fixing/spot-fixing	CSK/RR spot-fixing scandal (2013);⁶ ongoing betting corruption concerns	Criminal/gambling-driven activity	Player bans, franchise suspension, sport integrity damage, legal action
Corruption & Integrity	Illegal betting rings	Vast unregulated Indian betting markets with links to match-fixers⁷ ⁸	Criminal enterprise/regulatory gap	Match manipulation, player recruitment to fixing, law enforcement involvement
Corruption & Integrity	Umpire/official bribery	Potential fixing of key decisions affecting match outcomes	Corruption risk	Match integrity compromise, game credibility loss
Personnel	Key player unavailability	International obligations, injuries, visa issues, political reasons (Mustafizur situation)	Competing objectives/external restrictions	Team competitiveness, schedule disruptions, franchise value impact
Personnel	Player health/injury risks	Heat exhaustion, match injuries, stress-related conditions from uncertainty	Physical hazards/psychological stress	Loss of key players, season disruption, franchise financial impact
Personnel	Coach/staff turnover	Mid-season departures, conflicts between franchise and coaching staff	HR/organizational risk	Team continuity loss, player morale impact
Regulatory	Government restrictions/timeline conflicts	Elections scheduling conflicts with IPL dates;⁹ security directives impacting match scheduling	Government policy/external political context	Schedule changes, venue restrictions, resource allocation changes
Regulatory	Visa/immigration restrictions	Player visa delays, border restrictions preventing team travel	Government/immigration policy	Player unavailability, team incomplete status
Regulatory	Tax/regulatory changes	Changing tax levies on sports franchises, regulatory compliance requirements	Government fiscal policy	Franchise cost increases, profitability compression
Demand & Market	Fan disengagement/viewership decline	Cancellations and disruptions reduce fan engagement, ticket sales suffer	Market/behavioral shift	Revenue decline, reduced franchise valuations, reduced sponsorship interest
Demand & Market	Competitive threat from other entertainment	Social media, gaming, OTT platforms diverting cricket viewers	Technology/market disruption	Declining viewership, reduced sponsorship value, lower ticket sales
Demand & Market	Social media backlash/reputational damage	Negative sentiment from cancellations, perceived mismanagement	Communications/perception risk	Brand damage, sponsor pressure, fan retention loss
Health & Safety	Pandemic-related restrictions	COVID-like scenarios requiring lockdowns or capacity restrictions	Health emergency/external event	Match cancellation, venue capacity limits, player quarantine requirements
Health & Safety	Food/water safety incidents	Contaminated food/water affecting teams or spectators	Health/hygiene risk	Illness outbreaks, regulatory action, liability
Health & Safety	Air quality/pollution issues	High pollution affecting visibility, player respiratory health	Environmental hazard	Match visibility issues, player health concerns, match cancellation

Before diving into solutions, let’s define what we’re actually talking about. ISO 31073¹⁰ establishes the vocabulary for various terms used in ISO 31000,¹¹ which is the ISO framework for risk management. According to the frameworks, risk is “the effect of uncertainty on objectives”.
Here,

Objectives are whatever results the organisation wishes to achieve.
Effect means a deviation from the expected, whether the deviation is positive, negative, or both;
Uncertainty occurs from a deficit of information; and

Therefore, risk is a deviation from the aims that an entity is working towards caused due to lack of knowledge about the situations surrounding the objective. The deviation can have a positive or negative outcome, but the deviation means it is still a risk, and leads to risk consequences, or outcomes that affect the objectives.

Uncertainty can never be removed entirely. As we see in the normal distribution, risk events can happen even when we are 99.999% certain of our processes. This is called residual risk, or when a risk event occurs even when controls have been applied against the risk source. An event is the occurrence or change of circumstances (the bridge collapses, prices spike, new regulations take effect that can be the source of a risk. A risk source is an element with potential to give rise to risk (think: aging infrastructure, volatile commodity prices, regulatory change). Understanding residual risk is critical for determining whether further treatment is needed or whether the organisation should accept and monitor what remains. It is important to emphasise here that everyone perceives risk differently (risk perception): engineers might see technical risks as manageable; the public might see the same risks as terrifying. Effective risk communication requires understanding these perceptual differences.

The likelihood of an event, is a broad expression of the chance of something happening, and can be expressed qualitatively or quantitatively, but in the previous posts we have understood what a probability is, as expressed between 0 and 1 (here and here), and frequency, which is when we count the number of the type of events we are quantifying. understanding these basic terms helps us understand how vulnerable we are due to our exposure to a source of risk, as well as how to build resilience. Because we’re discussing a standard, these words have specific definitions:

Vulnerability refers to intrinsic properties creating susceptibility to risk sources.
Exposure measures the extent to which an organization is subject to an event.
Resilience captures adaptive capacity in complex, changing environments, so this isn’t about preventing events, it’s about how to recover from them.

Understanding risk also helps organisations understand which risks to accept, and which to defend against. New Zealand’s sports sector adopted ISO 31000 in 2016; Australia’s sporting associations follow it; international sporting events apply it to pandemic preparedness. This is called Risk attitude- the organisation’s overall approach towards risk, and their tendency to pursue, avoid, or accept it. Attitudes towards risk always depend upon any entity’s risk appetite (the amount and type of risk they are willing to accept), and their risk tolerance, which looks at specific risks for each objective. An example of risk appetite is the willingness to invest in innovative technology, and that of risk tolerance is the amount of specific risk an organisation may accept for data breaches in particular.

ISO 31000 Framework for Indian Cricket
While it may appear that these are all just the costs of doing business in India, I don’t think this is true. Also, other sports systems facing similar uncertainties—pandemics, extreme weather, terrorism, financial volatility—don’t operate this way. They use formal risk management frameworks to decide what to avoid, what to mitigate, what to insure, and what to accept. ISO 31000 is one such framework, and it’s suited to complex, multi-stakeholder systems like Indian cricket. Here it is applied to Indian cricket:

1. Establish Context (Where Are We Playing?)

External context
- Geopolitics: India–Pakistan tensions, elections, security environment.
- Climate: Fog in North India, heat waves, monsoon, long‑term climate change.
- Market: OTT platforms, competing sports/entertainment, sponsor expectations.
Internal context
- BCCI governance and decision‑making.
- Franchise finances, contracts, insurance.
- Stadium infrastructure, ground staff capacity, logistics capability.
Risk criteria
- What level of disruption is acceptable?
- Which risks are “never acceptable” (deaths, match‑fixing, major stampedes)?
- What is the minimum acceptable probability of completing a season as scheduled?

2. Risk Assessment (What Can Go Wrong, How Bad, How Often?)

Identify risks
- Use the big table: geopolitical, weather, logistics, stadium safety, financial, corruption, personnel, regulatory, demand, health.
- For each, note: risk source → potential event → likely consequences.
Analyze risks
- Estimate likelihood (e.g. “fog in Lucknow in December” = high; “pandemic lockdown every year” = low).
- Estimate consequence (e.g. “stadium stampede” = catastrophic; “one match fogged off” = moderate).
- Factor in vulnerability (old stadiums, fragile logistics) and resilience (backup plans, cash reserves).
Evaluate risks
- Plot likelihood × consequence.
- Decide which risks are:
  - Intolerable (must be treated immediately).
  - Tolerable with treatment (controls and monitoring).
  - Acceptable (monitor only).

3. Risk Treatment (What Do We Do About Each Risk?)

For each major risk, choose a treatment option (or a mix):

Avoid the risk
- Don’t schedule T20Is in dense‑fog cities during December–January.
- Don’t use stadiums that fail minimum structural and crowd‑safety standards.
Mitigate / reduce the risk
- Upgrade stadium exits, crowd‑control systems, and medical response.
- Build travel redundancy: buffer days, alternative flight routes, backup buses/trains.
- Strengthen anti‑corruption: monitoring betting patterns, education, strict sanctions.
- Heat protocols: evening matches, drinks breaks, heat‑stress monitoring.
Share / transfer the risk
- Tournament‑wide insurance for cancellation, terrorism, extreme weather.
- Clear contracts with broadcasters/sponsors about rescheduling and force majeure.
Retain (accept) residual risk
- Accept that a few games may still be lost to weather or logistics despite controls.
- Document what level of residual risk is being accepted, by whom, and with what monitoring.

4. Implementation & Control (Who Owns What, and How Is It Run?)

Governance & roles
- BCCI Risk Committee: owns the overall risk framework and major decisions.
- Franchise risk owners: handle team‑level logistics, personnel, finances.
- Venue operators: own stadium safety, crowd management, emergency response.
Communication & consultation
- Regular briefings with teams, broadcasters, police, local authorities.
- Clear public communication on cancellations, rescheduling, and safety decisions.
Monitoring
- Track near‑misses (e.g. small crushes at gates, close calls with fog or heat).
- Maintain dashboards: incidents per season, delays, injuries, corruption alerts.

5. Review & Continuous Improvement (What Did We Learn This Season?)

After each season / major incident:

Incident reviews
- IPL suspension: What early warning signs did we miss? Could we have acted sooner?
- Chinnaswamy stampede: Which design and process failures led to casualties?
- Lucknow fog‑out: How should scheduling rules change for fog‑prone venues?
- Mustafizur threats: How do we handle politically sensitive players and venues?
Effectiveness checks
- Did our treatments reduce likelihood or consequence as expected?
- Did any controls fail or create new risks (e.g. over‑policing crowds)?
Update the system
- Revise risk criteria, appetite, and tolerances where needed.
- Amend scheduling policies, venue standards, insurance terms, and contracts.
- Feed lessons into next season’s planning: same framework, better parameters.

To-Do List
If Indian cricket embraced systematic risk management, the BCCI would have:

A Risk Management Policy (BCCI document) establishing appetite and tolerance
A Risk Register (updated quarterly) tracking all relevant risk categories with assessed severity and treatment strategies
Incident Response Protocols that trigger automatically (e.g., if weather forecast shows fog, reserve dates activate; if geopolitical tension rises, security protocols engage)
Venue Certification requiring regular safety audits for all stadiums
Insurance covering defined scenarios with unambiguous language
Player Education on corruption risks, mental health impacts of uncertainty, safety protocols
Stakeholder Transparency (fans, sponsors, broadcasters informed about residual risks and mitigation strategies)
Continuous Learning (post-incident reviews feeding into policy updates)

Why bother?
Risks are interconnected: geopolitics affects scheduling, which affects logistics, which affects player welfare, which affects performance, which affects revenue. One shock propagates through the entire system.

But the real argument is how all this affects BCCI’s income: In fiscal year 2024-25, the BCCI earned a total of ₹20,686 crore—double what it was five years earlier. But this income doesn’t flow uniformly. It comes from multiple sources, each vulnerable to different risks:

IPL: ₹5,761 crore (59.1% of FY 2024-25 BCCI revenue)¹²
International cricket (men’s): ₹361 crore (3.7%)¹²
ICC distributions: ₹1,042 crore (10.7%)¹²
WPL (women’s): ₹951 crore broadcast deal over five years = approximately ₹190 crore annually¹³
Interest and other income: ₹1,500+ crore from treasury management ¹² ¹⁴
Sponsorships, licensing, other: ₹400 crore and growing¹⁵

Total bank balance: ₹20,686 crore.¹⁶ At this scale, ad-hoc risk management is not neutral—it is negligent.

The numbers are sourced, but even if the numbers are completely wrong, the logic I’m about to present you with will still hold.

Consider the May 2025 IPL suspension. Its immediate impact was ₹1,600-2,000 crore in tournament revenue loss. But the suspension also:

Forced reschedules of international T20I series planned around IPL slots
Delayed women’s cricket planning (WPL scheduling coordination)
Created cascading effects on domestic Ranji Trophy schedules
Disrupted team preparation windows for the Asia Cup (subsequently postponed)

When the IPL shut down due to the events that followed the Pahalgam terrorism, one risk event rippled across all BCCI’s operations. The ₹3,500-4,000 crore total ecosystem loss wasn’t borne by IPL alone—it distributed across broadcasters, sponsors, franchises, international teams visiting India, and state cricket associations that depend on BCCI’s distributions (approximately ₹100-125 crore in combined sponsorship, broadcast, and match-day revenue for 16 matches ¹⁵ and the broadcaster JioCinema faced losses of ₹1,900-2,000 crore (35% of their ₹5,500 crore seasonal projection)¹⁷ While war is a systemic risk (read more here, scroll down to the risk sections), a stampede at a celebration event is not.

Now let’s do some hypothetical maths. Let’s say of BCCI’s total ₹20,686 crore exposure, 10% is under difficult-to-avoid-risk, and another 20% are things that could go wrong but if everything happened normally (planes flew on time, luggage was not lost, people had common sense, etc.) it would not go wrong. Now assume costs of mitigation to be between 10-20% of the cost of losses. This would be the breakdown of that exposure:

Risk Category	% of Total Exposure	Exposure Amount (₹ Crore)	Annual Loss Probability	Expected Annual Loss (₹ Crore)	Mitigation Cost (10-20% of loss)	Net Benefit if Mitigated
High Risk (Geopolitical, Corruption, Major Infrastructure)	10%	₹2,068.6	20-30%	₹414-620	₹41-124	₹290-579
Medium Risk (Weather, Logistics, Personnel, Sponsorship)	20%	₹4,137.2	30-40%	₹1,241-1,655	₹124-331	₹910-1,531
Low Risk (Normal operations)	70%	₹14,480.2	1-5%	₹145-724	₹15-145	₹130-709
TOTAL	100%	₹20,686	~15-20% aggregate	₹1,800-3,000	₹180-600	₹1,200-2,820

Now let’s do scenario analysis with ILLUSTRATIVE NUMBERS.

Scenario A – No Mitigation (Do Nothing)

Element	Amount (₹ Crore)	Notes
Reserves/ Bank Balance	₹20,686	Baseline
Expected Losses (unmitigated)	₹1,800-3,000	From Table 1
Insurance Recovery (40-50% of losses)	₹720-1,500	Partial coverage; war/corruption not covered
Net Loss After Insurance	₹1,080-2,280	Uninsured exposure
Effective Revenue After Losses	₹18,406-19,606	Revenue minus net loss
Annual Cost to Organization	₹0	No prevention investment
Net Outcome	₹18,406-19,606	Revenue minus losses

Scenario B – Full Mitigation (Invest in Risk Management)

Element	Amount (₹ Crore)	Notes
Reserves/ Bank Balance	₹20,686	Baseline (unchanged)
Mitigation Investment	₹180-600	Cost to prevent/reduce losses
Expected Losses (with mitigation)	₹450-900	Reduced by 60-75% through mitigation
Insurance Recovery (40-50%)	₹180-450	Still applicable, lower losses
Net Loss After Insurance & Mitigation	₹270-450	Dramatically reduced
Effective Revenue After Mitigation & Losses	₹20,236-20,416	Revenue minus mitigation cost and net loss
Annual Cost to Organization	₹180-600	Mitigation investment
Net Outcome	₹20,236-20,416	Much better than Scenario A

None of the above means that BCCI doesn’t do risk mitigation at all. They must do. Matches are insured, security is coordinated with state authorities, schedules are adjusted, and contingency plans exist. But much of this risk management remains reactive, fragmented, and event-specific, rather than systematic.

The scale of Indian cricket has outgrown this approach. What is now a ₹20,000-crore ecosystem operates across volatile geopolitics, increasingly extreme climate conditions, aging infrastructure, fragile logistics, and intense public scrutiny. In such an environment, risk does not arrive as isolated shocks. It propagates. A fog-out affects scheduling, which affects logistics, which affects player welfare, which affects performance, which ultimately affects revenue and credibility. Treating each disruption as an unfortunate exception misses the underlying structure of the problem.

Active risk management does not promise certainty, nor does it eliminate risk. What it offers is clarity: an explicit understanding of working to anticipate risks in our cricket system so that most can simply be prevented, and those that cannot be prevented are mitigated. The IPL did not need to be part of India’s war theatre. After the Pahalgam attacks those matches could have been shifted to lower risk areas, such as away from the border, and we wouldn’t have had Ricky Ponting trying to persuade foreigners to stay back and play.¹⁸

Sources

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