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Understanding AI: The Science, Systems, and Industries Powering a $3.6 Trillion Future

Explore how artificial intelligence is transforming finance, automation, and industry — and what the $3.6 trillion AI boom means for our future

Jay Magiya

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AI jpeg
The global artificial intelligence market growing from $538 billion in 2023 to $3.68 trillion by 2034, with steady year-on-year increases and a projected 18.6% CAGR. Image credit: Satheesh Sankaran/Pixabay

Artificial Intelligence (AI) has become a major point of discussion and a defining technological frontier of our time. Experiencing remarkable growth in recent years, AI refers to computer systems capable of mimicking intelligent human cognitive abilities such as learning, problem-solving, critical decision-making, and creativity. Its ability to identify objects, understand human language, and act autonomously makes it increasingly feasible for industries like automotive manufacturing, financial services, and fraud detection.

As of 2024, the global AI market is valued at approximately USD 638.23 billion, marking an 18.6% increase from 2023, and is projected to reach USD 3,680.47 billion by 2034 (Precedence Research). North America leads this global growth with a 36.9% market share, dominated by the United States and Canada. The U.S. AI market alone is valued at around USD 146.09 billion in 2024, representing nearly 22% of global AI investments.

The Early Evolution of AI: From Reactive Machines to Learning Systems

Our understanding of AI has evolved through different models, each representing a step closer to mimicking human intelligence.

Reactive Machines: The First Generation

One of the earliest and most famous AI systems was IBM’s Deep Blue, the chess-playing computer that defeated world champion Garry Kasparov in 1997. Deep Blue was a reactive machine model, relying on brute-force algorithms that evaluated millions of possible moves per second. It could process current data and generate responses, but lacked memory or the ability to learn from past experiences.

Reactive machines are task-specific and cannot adapt to new or unexpected conditions. Despite these limitations, they remain integral to automation, where precision and repeatability are more important than learning—such as in manufacturing or assembly-line robotics.

Artificial intelligence is evolving from a niche technology to a global economic powerhouse. According to Precedence Research, the global AI market is expected to expand nearly sevenfold between 2023 and 2034, driven by applications in finance, healthcare, and manufacturing.

Limited Memory AI: Learning from Experience

To overcome the rigidity of reactive machines, researchers developed Limited Memory AI, a model that can store and recall past data to make more informed decisions. This model powers technologies such as self-driving cars, which constantly analyze road conditions, objects, and obstacles, using stored data to adjust their behaviour.

Limited Memory AI is also valuable in financial forecasting, where it uses historical market data to predict trends. However, its memory capacity is still finite, making it less suited for complex reasoning or tasks like Natural Language Processing (NLP) that require deeper contextual understanding.

Theoretical Models: Towards Human-Like Intelligence

Theory of Mind AI

The next conceptual step is Theory of Mind AI, a model designed to understand human emotions, beliefs, and intentions. This approach aims to enable AI systems to interact socially with humans, interpreting emotional cues and behavioral patterns.

Researchers like Neil Rabinowitz from Google DeepMind have developed early prototypes such as ToMnet, which attempts to simulate aspects of human reasoning. ToMnet uses artificial neural networks modeled after brain function to predict and interpret behavior. However, replicating the complexity of human mental states remains a distant goal, and these systems are still largely experimental.

Self-Aware AI: The Future Frontier

The ultimate ambition of AI research is self-aware AI — systems that possess conscious awareness and a sense of identity. While this remains speculative, the potential applications are vast. Self-aware AI could revolutionize fields like environmental management, creating bots capable of predicting ecosystem changes and implementing conservation strategies autonomously.

In education, self-aware systems could understand a student’s cognitive style and deliver personalized learning experiences, adapting dynamically to each learner.

However, replicating human self-awareness is extraordinarily complex. The human brain’s intricate memory, emotion, and decision-making systems remain only partially understood. Additionally, self-aware AI raises profound ethical and privacy concerns, as such systems would require massive amounts of sensitive data. Strict guidelines for data collection, storage, and usage would be essential before such systems could be deployed responsibly.

Artificial Intelligence in the Financial Services Industry

The financial sector has undergone a massive transformation powered by AI-driven analytics, automation, and predictive intelligence. AI enhances Corporate Performance Management (CPM) by improving speed and precision in financial planning, investment analysis, and risk management.

From financial services (38%) to healthcare (35%), sectors are integrating AI to improve efficiency, prediction, and decision-making. Manufacturing, retail, and education are close behind, showing that automation and data intelligence are reshaping every industry

Natural Language Processing and Automation

Leading financial firms such as JPMorgan Chase and Goldman Sachs employ Natural Language Processing (NLP) — AI systems that understand human language — to streamline customer interaction and analyze market information. NLP tools like chatbots handle millions of customer queries efficiently, while advanced systems process unstructured text data from financial reports and news sources to inform investment decisions.

Paired with Optical Character Recognition (OCR) and document parsing, NLP systems can convert scanned or image-based documents into machine-readable text, accelerating compliance checks, fraud detection, and financial forecasting.

However, the accuracy of NLP models depends on the quality and diversity of training data. Biased or incomplete data can lead to errors in analysis, potentially influencing high-stakes financial decisions.

Generative AI in Finance

Another major shift in finance comes from Generative AI, a branch of AI that creates new content — including text, images, videos, and even financial models — based on learned patterns. Using Large Language Models (LLMs) and Generative Adversarial Networks (GANs), these systems simulate complex financial scenarios, improving fraud detection and stress testing.

For instance, PayPal and American Express use generative AI to simulate fraudulent transaction patterns, strengthening their security systems. Transformers — deep learning architectures behind tools like OpenAI’s GPT — enable these models to understand and generate human-like language, allowing them to summarize extensive reports, produce research briefs, and assist analysts in decision-making.

Yet, Generative AI also presents challenges. It can be manipulated through adversarial attacks, producing misleading or biased outputs if trained on flawed data. Ensuring transparency and fairness in training datasets remains critical to prevent discriminatory outcomes, especially in credit scoring and loan assessment.

AI and Automation: Revolutionizing Industry Operations

Artificial Intelligence has become a cornerstone of intelligent automation (IA), reshaping business process management (BPM) and robotic process automation (RPA). Traditional RPA handled repetitive, rule-based tasks, but with AI integration, these systems can now manage complex workflows that require contextual decision-making.

AI-driven automation enhances productivity, reduces operational costs, and increases accuracy. For example, in manufacturing, AI-enabled systems perform predictive maintenance by analyzing sensor data to detect machinery issues before failures occur, minimizing downtime and extending equipment lifespan.

In the automotive sector, AI-powered machine vision systems inspect car components with higher accuracy than human inspectors, ensuring consistent quality and safety. These innovations make automation not only efficient but also economically advantageous for large-scale industries.

Machine Learning: The Engine of Artificial Intelligence

At the heart of AI lies Machine Learning (ML) — algorithms that allow computers to learn from data and improve over time without explicit programming. Three fundamental ML models underpin most modern AI applications: Decision Trees, Linear Regression, and Logistic Regression.

Decision Trees

Decision trees simplify complex decision-making processes into intuitive, branching structures. Each branch represents a decision rule, and each leaf node gives an outcome or prediction. This makes them powerful tools for disease diagnosis in healthcare and credit risk assessment in finance. They handle both numerical and categorical data, offering transparency and interpretability.

Linear Regression

Linear regression models relationships between one dependent and one or more independent variables, making it useful for predictive analytics such as stock price forecasting. It applies mathematical techniques like Ordinary Least Squares (OLS) or Gradient Descent to optimize prediction accuracy. Its simplicity, efficiency, and scalability make it ideal for large datasets.

Logistic Regression

While linear regression predicts continuous outcomes, logistic regression is used for classification problems — determining whether an instance belongs to a particular category (e.g., yes/no, fraud/genuine). It calculates probabilities between 0 and 1 using a sigmoid function, providing fast and interpretable results. Logistic regression is widely used in healthcare (disease prediction) and finance (loan default assessment).

Types of Machine Learning Algorithms

Machine Learning can be broadly classified into Supervised, Unsupervised, and Reinforcement Learning — each suited for different problem types.

Supervised Learning

In supervised learning, algorithms train on labelled datasets to identify patterns and make predictions. Once trained, they can generalize to new, unseen data. Applications include spam filtering, voice recognition, and image classification.

Supervised models handle both classification (categorical predictions) and regression (continuous predictions). Their strength lies in high accuracy and reliability when trained on quality data.

Unsupervised Learning

Unsupervised learning, in contrast, deals with unlabelled data. It identifies hidden patterns or groupings within datasets, commonly used in customer segmentation, market basket analysis, and anomaly detection.

By autonomously discovering relationships, unsupervised learning reduces human bias and is valuable in exploratory data analysis.

Reinforcement Learning (Optional Expansion)

While not yet as mainstream, reinforcement learning trains algorithms through trial and error, rewarding desired outcomes. It is foundational in robotics, autonomous systems, and game AI — including the systems that now outperform humans in complex strategic games like Go or StarCraft.

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Image credit: Gerd Altmann/Pixabay

Ethical and Societal Considerations of AI

Despite its transformative potential, AI raises significant ethical and privacy challenges. Issues such as algorithmic bias, data exploitation, and job displacement are increasingly at the forefront of public discourse.

Ethical AI demands transparent data practices, accountability in algorithm design, and equitable access to technology. Governments and academic institutions, including Capitol Technology University (captechu.edu), emphasize developing AI systems that align with social good, human rights, and sustainability.

Furthermore, the rise of generative AI has intensified debates about content authenticity, intellectual property, and deepfake misuse, underscoring the urgent need for comprehensive AI regulation.

A Technology Still in Transition

Artificial Intelligence stands at the intersection of opportunity and uncertainty. From Deep Blue’s deterministic algorithms to generative AI’s creative engines, the technology has redefined industries and continues to evolve at an unprecedented pace.

While self-aware AI and full cognitive autonomy remain theoretical, the rapid integration of AI across industries signals an irreversible shift toward machine-augmented intelligence. The challenge ahead is ensuring that this evolution remains ethical, inclusive, and sustainable — using AI to enhance human potential, not replace it.

References

  • Artificial Intelligence (AI) Market Size to Reach USD 3,680.47 Bn by 2034 – Precedence Research
  • What is AI, how does it work and what can it be used for? – BBC
  • 10 Ways Companies Are Using AI in the Financial Services Industry – OneStream
  • The Transformative Power of AI: Impact on 18 Vital Industries – LinkedIn
  • What Is Artificial Intelligence (AI)? – IBM
  • The Ethical Considerations of Artificial Intelligence – Capitol Technology University
  • What is Generative AI? – Examples, Definition & Models – GeeksforGeeks

Jay Magiya is a contributor for EdPublica, based in Abu Dhabi, and an A-Level student of Mathematics, Further Mathematics, Economics, and Physics with a deep interest in Artificial Intelligence and its applications in finance. His research explores how machine learning and generative AI models — including decision trees, regression, and GANs — are transforming financial systems and automation. He is keen to continue researching the intersection between the financial industry with theoretical models and their practical implementations of AI in the real world.

Society

When Pollinators Vanish, Children Go Hungry—Here’s the Proof

A landmark study has, for the first time, traced a direct line from the collapse of wild insect pollinators to the malnutrition and poverty of farming families — reframing biodiversity loss as a global public health emergency.

Dipin Damodharan

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Pollinator Decline Threatens Nutrition, Farm Incomes: Study
Image credit: Tom Timberlake

Two billion. That is how many people on this planet eat what smallholder farmers grow. Not what agri-industrial combines harvest, not what commodity markets trade — what families with small plots of land pull from the soil, season after season, with the tools and seeds and knowledge they have. Two billion people. And a significant share of what keeps those harvests coming, what puts vitamins into the food and income into the household, has no name on any payroll, files no tax return, and has never once been thanked.

It is insects. Wild insects — bees, hoverflies, moths, beetles — moving flower to flower across millions of smallholder fields, doing work that no machine replicates and no subsidy replaces. Pollinator decline is dismantling that system quietly, field by field, season by season. A study published today in Nature, led by researchers at the University of Bristol, has for the first time traced exactly what that loss costs — not in abstracted ecosystem valuations, but in the vitamin A missing from a child’s diet, in the folate a pregnant woman never gets, in the farm income that does not arrive at the end of a harvest. The number at the end of that calculation is not a projection or a model. It is a measurement. And it is arresting.

Insect pollinators, the study found, are responsible for 44% of the farming income of the households tracked, and contribute more than 20% of dietary intake of vitamin A, folate and vitamin E — three nutrients whose deficiency is already linked to stunted child growth, weakened immunity and higher rates of disease. When pollinators vanish, the families don’t just grow less food. They grow less nutritious food, earn less money and become more vulnerable to illness. The cycle reinforces itself, downward.

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Ten Villages, One Year, and a Chain of Evidence

The study centred on ten smallholder farming villages and their surrounding landscapes in Nepal. Over the course of a year, the research team — drawn from universities and non-governmental organisations across Nepal, the United Kingdom, the United States and Finland — tracked three things simultaneously: which insects were visiting which crops, what those crops yielded and how nutritious they were, and what the farming families were actually eating and earning.

The impact of pollinator decline on food production and nutrition is high
Nepal’s smallholder farming communities are highly dependent on diverse range of pollinator-dependent crops. Image credit: Tom Timberlake

It is, in structural terms, the kind of study that is very hard to pull off. Most research on pollinators stops at the field boundary — counting bee visits, measuring fruit set, estimating yield differentials. This one kept going, all the way to the dinner table and the household ledger. That continuity of evidence is what makes it significant.

why nepal

The picture that emerged was not abstract or statistical. It was human. Over half the children in the study villages were too short for their age — a condition that goes by the clinical name of stunting and signals not just poor growth but compromised brain development, reduced immunity and diminished life prospects. The underlying cause, as the researchers documented it, was diet. And that diet depended, in ways the families could not easily see or control, on the insects working their fields.

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Pollinator Decline: The Hidden Hunger Nobody Is Counting

There is a term in public health circles for the condition that the Nepal families illustrate: hidden hunger. It describes not the obvious, acute starvation that makes headlines, but the chronic, silent insufficiency of vitamins and minerals that undermines health even when enough calories are being consumed. A quarter of the global population currently suffers from it. It is, by most measures, one of the largest sources of preventable illness on the planet, and it is almost entirely invisible in the way society keeps score of environmental damage.

When a species goes extinct, when a forest is cleared, when an insect population crashes — the accounting of loss is typically measured in biodiversity metrics, in ecosystem service valuations, or in the emotional register of what is no longer there to see. It is almost never measured in folate deficiency, in children’s height-for-age charts, in the likelihood of a farming family falling into debt after a bad harvest.

That is what this study changes. It is not the first to establish that pollinator decline matters for nutrition in the abstract. But it is the first to demonstrate, with tracked data from real communities over a real year, the size and mechanism of the effect — and to show that the effect flows not just through calories but through the specific micronutrients that are hardest to replace.

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Biodiversity as Medicine

Planetary Health — the field Dr Myers directs at Johns Hopkins — proceeds from a deceptively simple premise: human health and ecological health are not separate subjects. They are the same subject, studied from different ends. The degradation of natural systems is not a background condition to human development; it is one of the primary mechanisms by which human health is undermined.

That claim has long had intuitive force. What the Bristol study on pollinator decline provides is something more demanding: empirical evidence at the household level. It is one thing to argue that biodiversity loss will eventually compromise food security in a generalised way. It is another to show, village by village, season by season, that the decline in the bee community visiting a particular set of crops reduces particular vitamins in particular families’ diets by a measurable amount.

Bee on a flowering crop showing the impact of pollinator decline on food production and nutrition
Image credit: Tom Timberlake

The phrasing matters. Biodiversity is not a luxury. In policy conversations, the language of luxury — or alternatively, of long-term concern — has frequently served to push ecological questions down the agenda. If the relationship between pollinator health and child health is as direct as this study finds, that framing becomes harder to sustain.

What Goes When the Bees Go

It is worth being specific about the nutritional stakes. Vitamin A deficiency impairs vision, particularly in low light, and compromises the immune system’s ability to fight infections that would otherwise be routine. Folate deficiency during pregnancy causes neural tube defects in developing foetuses, among other effects. Vitamin E is a key antioxidant, and its deficiency is associated with neurological damage and weakened immune function. These are not marginal health concerns. They sit near the top of the global burden of preventable disease.

The crops most dependent on animal pollination — fruits, many vegetables, pulses — are also, not coincidentally, among the most concentrated sources of these particular nutrients. A diet from which pollinator-dependent produce has been reduced or removed can look adequate in calorie terms while being profoundly inadequate in micronutrient terms. The families studied in Nepal were, in effect, already living that deficit, in a context where pollinator diversity is declining.

Globally, insect populations have been under sustained pressure for decades. Pesticide use, habitat loss, monoculture farming, climate change and artificial light at night have all been implicated in declines that researchers have called, in some cases, ecological collapse. The mechanisms are various; the direction of travel is consistent.

The Good News: Reversible by Design

The research is, in its implications, genuinely alarming. But the researchers are also at pains to emphasise something that is easy to miss in the headline findings: the relationship between pollinators and nutrition runs in both directions. If pollinator decline causes nutritional harm, pollinator recovery can produce nutritional gains. And the actions required are not exotic.

Planting wildflowers at field margins. Reducing pesticide inputs. Keeping native bee colonies. These are the kinds of changes that do not require new technology or large capital investment. They require farmers to understand what is happening in their fields at a level of detail most have not previously been given reason to consider. The researchers are already working on that — translating their findings into practical guidance and working with local organisations, government partners and farmers in Nepal to implement changes on the ground.

The approach is now informing Nepal’s emerging National Pollinator Strategy, an effort to make pollinator-friendly practices a standard part of everyday agriculture rather than a specialist conservation concern. The researchers report that farmers who have adopted even modest changes are already seeing improvements in crop yields, income and nutrition — a feedback loop that runs in the direction of health rather than away from it.

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A Framework That Travels

Nepal is not an isolated case. Two billion people around the world depend on smallholder farming. Many of them face the same combination of circumstances: high dependence on pollinator-sensitive crops, limited dietary alternatives, micronutrient deficiencies that are already entrenched and ecosystems under stress. The findings from ten Nepali villages do not translate automatically to every agricultural context, but the framework — the method of tracing connections from insects to income to nutrition — does.

Diets even in industrialised countries still depend on pollinators and the ecosystems that sustain global agriculture. The buffer of wealth — the ability to import, substitute, supplement — is larger in wealthy countries, but it is not unlimited, and it does not protect the most economically vulnerable people even within those countries.

The lesson from this research on pollinator decline is less a specific warning about Nepal and more a methodological call to arms: to start measuring the connections that have, until now, been assumed or asserted but rarely demonstrated. When those connections are demonstrated, the case for protecting what remains of insect diversity becomes something different — not a moral preference or an aesthetic value, but a documented precondition for human health.

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The Stakes

A quarter of the world’s people are living with hidden hunger. Over half the children in ten Nepali villages are stunted. Forty-four percent of the farming income in those communities flows, invisibly, through the wings of insects that nobody counted or protected until researchers started looking. The insects are in decline.

The study’s authors are careful, as scientists should be, to describe what they found and what it implies rather than what must be done. But the shape of the implication is not obscure. The fabric of life — the phrase Dr Myers uses — is not an abstraction. It is the thing that puts vitamins in a child’s diet and money in a family’s pocket. Tear large enough holes in it, and the consequences are not primarily ecological. They are medical. They are economic. They are, in the most direct sense, human. That’s why the new findings on pollinator decline matter.

The bees were always doing the work. We just weren’t watching closely enough to see it — or to understand what we stood to lose.

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Society

Lost in Your Twenties? You’re Not Behind—You’re Becoming

Feeling lost in your twenties? You’re not behind—you’re becoming. Here’s why confusion, doubt and delay are part of growth.

Glenda Fernandes & Dr. Aiswarya V R

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Quarter-Life Crisis? Why Feeling Lost in Your 20s Is Normal
Image: Sasha Freemind/Unsplash
Authors

The quarter-life crisis is one of the most widely felt yet least talked-about experiences of early adulthood. Two psychologists explain why the pressure to have everything figured out is making an already difficult decade harder – and how self-compassion could be the most important skill a young person develops.

In recent years, conversations about mental health have become more visible, yet one experience faced by many young adults often remains unspoken: the quarter-life crisis. Across universities, workplaces, and homes, many individuals in their twenties quietly struggle with feelings of uncertainty about their future. They may have completed their education, secured a job, or be actively searching for one, yet a persistent question lingers: Is this the life I really want?

What many describe as a quarter-life crisis is often this exact feeling—uncertainty, comparison, and the quiet fear of falling behind. It’s a phase increasingly common among young adults, where expectations collide with reality, leaving many questioning their choices, direction, and sense of purpose.

The twenties have long been viewed as a time of opportunity, exploration, and independence. However, for many young adults today, this stage is also marked by intense pressure. Decisions about career paths, financial stability, relationships, and personal identity often converge during this period. At the same time, social comparisons — particularly through social media — can create the impression that everyone else seems to have their lives perfectly planned.

What Is a Quarter-Life Crisis, Really?

A quarter-life crisis isn’t just “being dramatic.” It is a period of uncertainty and emotional stress marked by feeling stuck or directionless, comparing yourself constantly to others, doubting your choices, anxiety about the future, and the pressure to have it all figured out. In a world where everyone seems to be thriving online, it is easy to feel like you are the only one struggling. But behind those curated posts, many are just as confused.

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Psychologists describe this as a phase of emotional and psychological uncertainty that typically occurs in early adulthood. Unlike the widely discussed mid-life crisis, the quarter-life crisis often emerges when individuals are expected to transition into stable adult roles. The pressure to make the “right” decisions about career, relationships, and life direction can make this period particularly stressful. While these challenges can feel overwhelming, psychological research suggests that certain factors can help young adults navigate this phase more effectively.

Why Are We So Hard on Ourselves?

When things don’t go as planned, most of us turn inward with criticism.

“I should be doing better.” “I’m already behind.” “Everyone else has their life together.”

This inner voice can be harsh, unforgiving, and exhausting. And instead of helping, it makes the crisis feel heavier. That is where self-compassion comes in.

Self-Compassion: The Skill No One Taught Us

Self-compassion is not about being lazy or making excuses. It is about treating yourself with the same kindness you would offer a friend. Think about it: if your friend said they felt lost, would you tell them they were a failure? Probably not.

Psychologist Kristin Neff identifies three elements at the heart of self-compassion: self-kindness — being gentle with yourself instead of critical; common humanity — recognising that struggle is part of being human; and mindfulness — acknowledging your feelings without overreacting. It is not about ignoring your problems; it is about facing them without tearing yourself down.

What many call a quarter-life crisis—that overwhelming feeling of being lost in your twenties
Image: Toni Reed/Unsplash

How Self-Compassion Helps During a Crisis

When you practise self-compassion, something shifts. Instead of panicking, you pause. Instead of judging, you understand. Instead of spiralling, you ground yourself.

Research shows that people who are more self-compassionate experience lower anxiety and stress, better emotional resilience, greater clarity in decision-making, and improved overall wellbeing. Self-compassion does not solve a crisis overnight — but it changes how you go through it.

Small Ways to Be Kinder to Yourself

You do not need a complete life overhaul. Start small. Change your inner dialogue: replace “I’m failing” with “I’m figuring things out.” Take breaks without guilt — rest is productive too. Limit comparison; social media shows highlights, not reality. Celebrate small wins, because progress is not always loud. And ask for help. You do not have to do this alone.

A quarter-life crisis can feel like everything is falling apart. But sometimes, it is actually everything falling into place — just not in the way you expected. In the end, a quarter-life crisis is not a sign that you are failing. It is a sign that you are evolving, and with self-compassion, you can navigate this uncertainty with greater strength, clarity, and trust in your own journey.

Reference

>> Neff, K. (2003). Self-Compassion: An Alternative Conceptualization of a Healthy Attitude Toward Oneself. Self and Identity, 2(2), 85–101.

>> Robinson, O. C. (2019). A Longitudinal Mixed-Methods Case Study of Quarter-Life Crisis During the Post-university Transition: Locked-Out and Locked-In Forms in Combination. Emerging Adulthood, 7(3), 167–179. Scopus.

Glenda Fernandes is a researcher at Christ (Deemed to be University), Bangalore, with a focus on the psychological experiences of young adults, including quarter-life crisis, meaning in life, and self-compassion. Dr. Aiswarya V R is Assistant Professor at Christ (Deemed to be University), Bangalore, specialising in health and developmental psychology. She holds an MSc in Applied Psychology from the University of Calicut and a doctorate in Child Psychology from the University of Kerala.

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Sustainable Energy

IEA flags methane cuts as key to energy security amid global crisis

Dipin Damodharan

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IEA report says methane cuts could unlock 200 bcm gas yearly,
Image credit: Lachlan/Unsplash

Methane emissions from the global energy sector remain stubbornly high, with no clear signs of decline, even as countries ramp up climate commitments. A new report by the International Energy Agency warns that closing this gap could not only curb warming but also significantly ease global gas shortages.

Released as part of the Global Methane Tracker 2026, the analysis shows that tried-and-tested measures could unlock up to 200 billion cubic metres (bcm) of natural gas annually—a volume that could reshape supply dynamics during a time of geopolitical strain.

Methane emissions plateau despite rising commitments

Despite pledges now covering over half of global oil and gas production, methane emissions from fossil fuels remained near record highs in 2025. The report highlights a widening “implementation gap” between ambition and actual reductions.

Around 70% of emissions are concentrated in just 10 countries, underscoring how targeted action could deliver outsized results. At the same time, performance varies drastically, with the most efficient producers emitting over 100 times less methane than the worst performers.

Energy crisis sharpens urgency

The urgency is heightened by ongoing disruptions in global energy markets, particularly the near-closure of the Strait of Hormuz, which has cut close to 20% of global LNG supply.

The IEA estimates that 15 bcm of gas could be made available quickly through existing methane abatement measures in key exporting and importing countries. Over time, broader action could deliver nearly 100 bcm annually, with another 100 bcm unlocked by eliminating non-emergency gas flaring.

“This is not only a climate issue,” said Tim Gould. “There are also major energy security benefits that can come from tackling methane and flaring, especially at a time when the world is urgently looking for additional supply amid the current crisis.”

Low-cost solutions within reach

The report emphasises that around 70% of methane emissions—roughly 85 million tonnes—can be reduced using existing technologies. Notably, over 35 million tonnes could be avoided at no net cost, making methane abatement one of the most cost-effective climate actions available.

A major share of emissions—about 80% in oil and gas—comes from upstream operations, making this a critical focus area for policymakers.

Coal sector under scrutiny

Experts say the coal sector remains a blind spot in global methane mitigation efforts.

“Coal, one of the biggest methane culprits, is still being ignored,” said Sabina Assan of Ember. “There are cost-effective technologies available today, so this is a low-hanging fruit for tackling methane. We can’t let coal mines off the hook any longer.”

India and other major emitters need sharper focus

For countries like India, the report and accompanying expert commentary point to an urgent need to prioritise methane from coal mining—an area often overlooked in climate strategies.

“Methane emissions from coal mining have not received enough attention,” said Rajasekhar Modadugu. “Major coal mining countries, including India, should focus on existing technologies and the feasibility of capturing or eliminating these emissions.”

Satellites and policy frameworks gaining traction

The report also highlights the growing role of satellite monitoring in identifying large methane leaks, alongside new frameworks developed with international bodies to help governments respond more effectively.

With improved data transparency and emerging markets for low-methane fuels, the IEA suggests the groundwork is already in place. The challenge now lies in execution.

As Gould put it, “Setting targets is only a first step—real progress depends on policies, implementation plans and concrete action

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