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By:

Sagari Gupta

24 March 2026 at 7:46:04 pm

The Cost of Staying Vulnerable

Nepal’s $5 billion reconstruction bill exposes a wider economic failure: when governments repeatedly restore vulnerable assets instead of investing in resilience, natural hazards become recurring fiscal shocks. Nepal needs $4 billion to $5 billion to rebuild after floods triggered by a glacier collapse killed more than 600 people in Nepal and Tibet on August 26. That bill equals nearly a tenth of the country’s economy. The bigger question is what this bill represents. $5 billion is the cost...

The Cost of Staying Vulnerable

Nepal’s $5 billion reconstruction bill exposes a wider economic failure: when governments repeatedly restore vulnerable assets instead of investing in resilience, natural hazards become recurring fiscal shocks. Nepal needs $4 billion to $5 billion to rebuild after floods triggered by a glacier collapse killed more than 600 people in Nepal and Tibet on August 26. That bill equals nearly a tenth of the country’s economy. The bigger question is what this bill represents. $5 billion is the cost of replacing assets that already existed and were expected to generate value for years - roads that connected markets, bridges that carried trade, power stations that supported industry and homes that held household wealth. Nepal will spend billions restoring what it already had, rather than investing that money in what comes next. The scale of the economic damage depends on where people live, what gets built there, how rivers are managed, whether warning systems reach households and whether people have enough savings to survive an interruption in income. Assam offers the clearest lesson in this respect. Floods this year killed at least 99 people and displaced hundreds of thousands. Researchers from India, Sweden, the Netherlands, the UK and the US examined the rainfall data and found that it remained within historical norms. The floods intensified because of rapid urbanisation, deforestation, degraded wetlands and poor drainage, rather than because of a stronger monsoon. Then again, two districts can receive the same rainfall and still end up with very different losses. One may have functioning drainage, early-warning systems and insurance, while the other has none of these protections. For the second, a flood becomes an economic shock rather than simply a weather event. A farmer does not read a government estimate of aggregate losses. He or she loses a crop, still owes an agricultural loan and may go without income for weeks. A shopkeeper loses inventory, while a daily-wage worker loses earnings because there is no work. A family may have to sell a buffalo to buy food, and a child may miss school. Misleading Picture The GDP can also give a misleading picture of recovery. When a bridge collapses, rebuilding it creates construction activity as the government hires workers and purchases cement and steel, while public spending rises and GDP records that activity as growth. But the country, in fact, has spent money replacing an asset it already had. Nepal shows the scale of this trap. Its 2015 earthquake required an estimated $9 billion in reconstruction, close to half the country’s GDP at the time, according to Nepal’s Post Disaster Recovery Framework. Eleven years later, the country faces another multi-billion-dollar rebuild. A country that repeatedly reconstructs the same assets never accumulates new ones. The insurance gap shows who ultimately pays. Munich Re estimates that natural disasters caused close to $112 billion in economic losses worldwide in the first half of 2026, of which only $44 billion was insured, leaving a gap of 60 percent. Those uninsured losses simply move from one balance sheet to another. A wealthy household may lose a home but still have insurance, savings and access to credit with which to rebuild. A low-income family that loses the same physical asset may also lose its main store of wealth. The same flood therefore produces very different outcomes depending on income, making disaster risk a distributional issue as much as an environmental one. In India’s case, the problem is ‘disaster repetition.’ Assam already knows that it floods every monsoon, and that is the uncomfortable part policymakers continue to sidestep after every disaster season. The state spends heavily every year repairing the same roads, while agricultural income disappears at scale after each flood and households repeatedly borrow to recover. Public money therefore goes towards restoring assets that remain exposed to the same risks instead of redesigning them. A road that repeatedly washes away is a public-investment problem, not only a disaster-response problem. An embankment that repeatedly fails is a planning problem, while a household that falls into debt after every flood is a social-protection problem. Case for Prevention Prevention needs a stronger political case because adaptation still competes for a small share of development budgets. Nepal’s reconstruction bill, worth close to a tenth of its economy, makes that allocation harder to defend. Adaptation is infrastructure investment and fiscal planning but it is also poverty prevention. A bridge that does not collapse creates no headline, while a flood avoided through better drainage earns no minister political credit. That imbalance pushes governments towards visible responses over invisible prevention, even when prevention costs less. Not every disaster can be traced to climate change. The World Weather Attribution study on Assam rules it out directly for that event. The stronger, less comfortable conclusion is that physical hazards are colliding with development decisions: more people and more valuable assets are living in exposed areas, infrastructure is becoming more expensive and interconnected, poor households have limited savings and little insurance, and governments operate with finite fiscal space. The real cost of a disaster lies in the present and future ravages to the economy - the crop never harvested, the wages never earned, the unpaid loans and the public project that gets rebuilt instead of the next one getting built. The real test of policy is not how quickly a government announces relief but whether the same community has to again bear the brunt of the next flood. A disaster becomes a policy failure when governments keep paying for the same vulnerability instead of changing it. (The writer is an independent public policy researcher. Views personal.)

AI in Sperm Sorting: An Unbiased Decision for A Better Outcome

Artificial Intelligence or AI is revolutionising fertility treatments of the future. The inclusion of AI enhances the accuracy, efficiency, and objectivity of sperm selection, hence potentially improving fertility outcomes by leaps and bounds. Traditionally, sperm sorting through manual methods is subjective to judgments. Processes like centrifugation and swim-up methods are used to separate sperm based on motility and morphology. Although they are effective, they have their limitations, leading to human errors that affect the success rates of fertility treatment. For instance, studies have shown that traditional sperm sorting techniques can have variability in success rates, with reported live birth rates ranging between 15 per cent to 25 per cent per cycle depending on the method and quality of sperm. Hence the introduction of AI helps in maintaining consistency in evaluations of sperm, using the same data set for every sample which leads to better judgments.


Automation and Standardisation- Automation of sperm selection and also introduction of AI in the process have improved the results in ART. AI-assisted sperm selection improves the accuracy in choosing high-quality sperm for fertilisation purposes, and also, pregnancy and live birth rates might be improved. Technologies like Intracytoplasmic Morphologically Selected Sperm Injection along with AI ensure the chances of pregnancies increase by about 10-20 per cent compared to the standard procedures. AI and Automation will decrease time taken to analyze sperm and increase opportunities to select better sperm with DNA integrity for better development and higher success rates in embryo selection. These processes ensure that the sperm selection process follows consistent criteria, reducing variability in outcomes caused by human error.


Analysing Complex Data for Better Outcomes- AI plays a crucial in improving IVF outcomes by analysing complex data and providing tailored recommendations. AI-driven tools and models such as those on SpOvum.ai point towards an opportunity to optimise ovarian stimulation decisions by assessing patient characteristics and follicle growth patterns. A study revealed that the use of AI in IVF improved egg yield and reduced medication costs. AI enables fertility specialists to make data-driven choices, improving overall IVF success rates and streamlining treatment processes.


Reducing Human Error- AI models can continuously learn and refine their performance by being trained on newer data. This adaptability ensures the technology remains unbiased and up-to-date with the latest scientific insights into sperm quality and fertility success rates. Studies have shown that AI-driven sperm sorting can decrease human-related errors by up to 25 per cent, improving sperm selection quality in terms of morphology and motility.


Reduction of Sperm Damage- The new AI-driven sperm sorting techniques also include microfluidic systems that are known to exhibit several advantages over the most commonly used conventional method, which is centrifugation. Traditional centrifugation methods, such as density gradient centrifugation, also cause severe oxidative stress and DNA fragmentation of the sperm because of the very high mechanical forces involved. The AI-infused microfluidic sorting minimises this damage significantly by involving gentler processes that mimic the natural pathway of sperm selection. The studies show that the process of microfluidic sorting decreases DNA fragmentation in sperm, which gives improved opportunities for success for IVF. For example, DNA fragmentation is 20 percent lower in sperm sorted using microfluidic processes than in traditional processing methods.


AI is bound to play an increasingly definitive role in fertility treatments, which will improve the outcomes for couples experiencing infertility.


(The author is a Co-Founder & CEO at SpOvum® Technologies. Views personal.)

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