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Pakistan has reported its lowest oil and gas output in over two decades. Data from fiscal year 2025 (FY25) reveals a significant double-digit decline in both crude oil and natural gas production.

Industry experts attribute the downturn to structural inefficiencies, regulatory challenges, and an oversupply of imported regasified liquefied natural gas (RLNG), which has displaced local production.

According to a report by Topline Securities, Pakistan’s hydrocarbon production saw a sharp decline in FY25. Crude oil output dropped by 12% year-on-year (YoY), while natural gas production fell by 8% YoY. The decline was even more pronounced in the final quarter, with oil production decreasing by 8% quarter-on-quarter (QoQ) and 15% YoY, and gas production contracting by 7% QoQ and 10% YoY.

The oversupply of RLNG played a significant role in this decline. A policy shift redirected industrial users from natural gas to the national power grid, while the government imposed an “off-grid levy” of Rs791 per million British thermal units (mmbtu) on captive gas consumption, raising the total cost to Rs4,291/mmbtu.

This made gas-based electricity generation more expensive than grid power, discouraging industrial gas use and reducing demand for domestic production.

Oil production averaged 62,400 barrels per day (bpd) in FY25, with major fields experiencing declines ranging from 3% to 46%. The Tal Block, which contributes around 17% of Pakistan’s total oil output, recorded a steep 22% YoY decline in the fourth quarter. Within the block, production from the Maramzai and Mardankhel fields plummeted by 54% and 52% YoY, respectively.

Natural gas production also faced significant challenges, averaging 2,886 million cubic feet per day (mmcfd) in FY25. Major fields like Qadirpur and Nashpa saw sharp contractions in the fourth quarter, with output falling by 36% and 34% YoY, respectively. Even the Sui field, Pakistan’s largest gas producer, reported consistent declines.

Topline Securities estimates that the increased reliance on imported fuels added over $1.2 billion to Pakistan’s foreign exchange burden in FY25. This reliance not only inflates the import bill but also leaves the country vulnerable to global fuel price volatility and potential supply disruptions.

The outlook for FY26 remains bleak, with oil production projected to hover between 58,000-60,000 bpd and gas output expected to range from 2,750-2,850 mmcfd. Without significant policy changes or new investments in exploration and production (E&P), FY26 could mark the third consecutive year of declining hydrocarbon output.

However, there is a glimmer of hope. The government is set to renegotiate its long-term RLNG supply agreement with Qatar in March 2026. Industry experts believe that more flexible contract terms could create room for domestic E&P companies to increase production, provided they prioritize field maintenance and capital investment. Balancing imported LNG with sustainable local production will be crucial for safeguarding Pakistan’s energy security and stabilizing its foreign exchange reserves.

At the upcoming State Fair of Texas, confectioner Stephen El Gidi will offer up his own Dubai chocolate-inspired dessert — a base of rich Belgian chocolate and a pistachio spread layered like a sweet lasagna over cheesecake in a cup.

It’s definitely a departure from the typical corn dogs and cotton candy.

From the West Coast to Middle America, dessert creators at state fairs are hawking their own confections based on Dubai chocolate, a milk chocolate shell filled with creamy pistachio, tahini and crispy kataifi, a Middle Eastern pastry.

The offerings derive inspiration from the original Dubai Chocolate, a bar created in 2021 by Sarah Hamouda, the founder of Fix Dessert Chocolatier, an online confectionary shop in Dubai.

The bar quickly went viral, with influencers touting its gooey, crunchy goodness and Hamouda saying she was selling 100 bars per minute. Now, Dubai chocolate-inspired desserts have hit the masses and are popping up at a handful of state fairs for the first time this year.

The Minnesota State Fair will offer a Dubai chocolate strawberry cup in late August. Wisconsin just featured its version of the Dubai chocolate bar. And the Orange County Fair in Southern California debuted a Dubai chocolate brownie last month. In May, the L.A. County Fair also sold a Dubai chocolate strawberry cup.

El Gidi, who owns Drizzle Cheesecakes based in a Dallas suburb, is originally from Libya and moved to the U.S. in 2021 in hopes of becoming a business owner. He said he aims to sell between 15,000 and 20,000 cups this year at the state fair.

“I became an entrepreneur because of my father, who is also a business owner. He inspired me to be my own boss,” El Gidi told NBC News.

Stores like Trader Joe’s, Costco and even mall kiosks have featured their own versions of the chocolate bars for prices of around $3.99 and up. There’s even a Dubai chocolate pistachio shake at some Shake Shack locations featuring pistachio frozen custard with kataifi and a dark chocolate shell for $11.04.

Currently, people in Dubai can order Hamouda’s bar, which she calls “Can’t get knafeh of it,” from her online shop or through a delivery service. It costs a little over $18 per bar. Additionally, chocolate aficionados can find the bar at Dubai International Airport’s duty-free store in Terminal 3.

In May, product sales in the Confectionary category of Dubai Duty Free reached $20.2 million, up 81% thanks in part to Dubai chocolate, according to a press release from the company.

The versions of Dubai chocolate people are buying in the U.S. are more a replication of the flavor profile than the real thing, says Kristie Hang, a food journalist based in Southern California’s San Gabriel Valley.

These products are more wallet-friendly, selling for around $15 at grocery stores, and they’re made using standard ingredients like milk chocolate, strawberries and nut butters.

True Dubai chocolate, Hang says, is an artisanal dessert that’s made in small batches.

“The pistachios are imported from Turkey and the chocolate is special chocolate with edible gold,” she said.

There’s an element of luxury and craftsmanship to authentic Dubai chocolate, Hang added, noting a Dubai chocolate-covered strawberry confection would have only the finest, perfectly shaped strawberries dipped in high-quality Belgian or dark chocolate, paired with kataifi bits and pistachio cream from finely ground pistachios.

“It’s definitely a mass fad at this point, but it’s far removed from what the original Dubai chocolate was intended for, which was an exclusive luxury item. Now, it’s being marketed as a very generic thing that anyone can get,” said Hang.

Texas-based food reviewer Zain Mohammed said he’s not a fan of the Dubai chocolate trend. Mohammed, who was born in Chicago, raised in Saudi Arabia and now reviews restaurants in Houston, said he thinks the proliferation of the dessert is glossing over the culture and the important role food plays in family.

“There’s more to Dubai than just Dubai chocolate. I grew up in Saudi Arabia, and the Arab culture is very family-oriented and Arab hospitality is very unmatched.”

He said he’s also worried about people benefiting from the trend without appreciating the culture. “I believe that there is cultural appropriation because of the fact that so many people are doing it — they are latching onto the trend and then advertising it as their own.”

Bianca Tamondong, a college student who tried the Dubai brownie dessert from the Mom’s Bakeshoppe stand at the OC Fair, said she thought it was a winning combo. “I’ve tried so many other Dubai chocolate desserts before, such as the actual chocolate bar, ice cream variations and Dubai chocolate-covered strawberries. Ten dollars honestly seemed like such a steal since many other Dubai chocolate desserts cost $15.”

“The pistachios balanced out the sweetness of the brownie perfectly,” she said.

Confection connoisseur Dominic Palmieri sells a Dubai chocolate strawberry cup at the OC Fair.

“It has all the components of the Dubai chocolate. However, we’re putting chocolate on top of the strawberries, and it’s got silky cream chocolate that doesn’t harden,” said Palmieri.

It took more than three months to get enough pistachio cream for the fairs he’s participating in due to a pistachio shortage and high demand for pistachio cream. He projects securing around 2,000 gallons of pistachio cream and over 10,000 pounds of raw chocolate this year.

It was rare for anyone to find Dubai chocolate in 2024. “You had to find it in specialty chocolate shops, sweet shops or different places that were doing dessert,” says Palmieri.

Now, it’s everywhere, he says.

“When you go to the fair, you’ll go get your corn dog, turkey leg, funnel cake, and you’ll get your Dubai chocolate strawberry cup. This one is quickly becoming a fan favorite,” Palmieri said.

The price of gold in Pakistan as well as global market dropped for the sixth consecutive day on Saturday, reported 24NewsHDTV Channel. 

According to the All Pakistan Sarafa Gems and Jewellers Association, the price of 24-karat gold per tola saw a drop of Rs900 on Saturday to reach Rs356,200. Similarly, the price of 10 grams of gold dropped by Rs771, taking it to Rs305,384. 

The global market also witnessed a minor change, with the price of gold per ounce declining by just $9 to reach $3,335. The silver prices in Pakistan also witnessed a decline, with the price of a tola of 24-karat silver dropping to Rs4,031, and the price for 10 grams of silver to Rs3,455. 

Identification of differentially expressed lipid metabolism related genes between periodontitis and healthy individuals

The workflow of this study is display in Fig. 1A. A total of 74 differentially expressed lipid metabolism-related genes (DELMRGs) were identified through differential expression analysis. Among these, 44 genes were upregulated, while 30 genes were downregulated (Fig. 1B). The hierarchical clustering heatmap shows the detailed expression patterns of these DELMRGs (Fig. 1C). We then investigated the biological functions of the upregulated and downregulated LMRGs using GO and KEGG enrichment analyses. GO enrichment analysis revealed that the upregulated LMRGs were primarily involved in the phospholipid metabolic process, lipid catabolic process, regulation of phosphatidylinositol 3-kinase activity, regulation of inflammatory response, and regulation of lipid kinase activity (Fig. 1D). In contrast, the downregulated LMRGs were mainly associated with membrane lipid metabolism, membrane lipid biosynthesis, sphingolipid metabolism and biosynthesis, and fatty acid metabolism (Fig. 1F). KEGG enrichment analysis indicated that the upregulated LMRGs were significantly involved in the chemokine signaling pathway, phospholipase D signaling pathway, cytokine-cytokine receptor interaction, NF-kappa B signaling pathway, and ether lipid metabolism (Fig. 1E). Meanwhile, the downregulated genes were primarily associated with arachidonic acid metabolism, fatty acid metabolism, and the biosynthesis of unsaturated fatty acids (Fig. 1G).

Hub lipid metabolism related genes identification by multiple machine learning approaches

Next, three machine learning algorithms were employed to identify the most relevant diagnostic LMRGs for periodontitis. Random Forest (RF) identified 10 LMRGs based on importance scores, including FABP4, PLEKHA1, CWH43, CLN8, PDGFD, NEU1, HMGCR, CYP24A1, and OSBPL6 (Fig. 2A, B). Using LASSO regression, 16 LMRGs were identified after selecting the appropriate penalty parameter (Fig. 2C, D), with the coefficients of these genes shown in Fig. 2E. In the XGBoost model, the top 10 important LMRGs were CWH43, RORA, HSD11B1, PLEKHA1, ADGRF5, CLN8, FABP4, LYN, ENPP2, and OSBPL6 (Fig. 2F). Ultimately, five hub LMRGs were identified across these three algorithms: FABP4, CWH43, CLN8, ADGRF5, and OSBPL6 (Fig. 2G). We then explored the expression levels of these genes between periodontitis and healthy samples in the GSE16134 dataset. ADGRF5 and FABP4 were significantly upregulated in periodontitis samples compared to healthy tissues (Fig. 2H). Conversely, the expression levels of CWH43, CLN8, and OSBPL6 were significantly higher in healthy tissues than in periodontitis samples (Fig. 2H).

Subsequently, ROC curves were used to evaluate the diagnostic ability of these LMRGs in distinguishing between periodontitis and healthy participants. The results demonstrated that these genes performed well in differentiating periodontitis from healthy individuals (Fig. 2I). The AUC value for CWH43 was 0.898 (0.848 − 0.947), followed by OSBPL6 (0.894 [0.849 − 0.939]), CLN8 (0.864 [0.810 − 0.918]), ADGRF5 (0.855 [0.808 − 0.903]), and FABP4 (0.850 [0.794 − 0.906]) (Fig. 2I). To further enhance the diagnostic ability of these genes for periodontitis, we developed an LMRGs score using the following formula: LMRGs score = 1.137 * FABP4 + (-1.113) * CWH43 + (-1.701) * CLN8 + 2.403 * ADGRF5 + (-1.642) * OSBPL6. As expected, periodontitis patients had higher LMRGs scores than healthy individuals (Fig. 2J). Integrating these genes into one index further improved the diagnostic ability, with the LMRGs score yielding an excellent discrimination ability between periodontitis and healthy samples, with an AUC of 0.954 (0.919 − 0.988) (Fig. 2K). We also validated the expression levels and diagnostic ability of the identified hub LMRGs and LMRGs score in an external validation cohort, with consistent results observed (Figure S1A-D). Ultimately, we also validated the protein expression level of these hub genes in healthy and periodontitis human samples through IHC staining. The results indicated that CLN8, CWH43, and OSBPL6 were upregulated in periodontitis tissues, while ADGRF5 and FABP4 were upregulated in healthy tissues (Figure S2). Taken together, these results indicate that these LMRGs and their integrated index, the LMRGs score, have promising diagnostic ability for periodontitis.

Immune cell infiltration landscape and immune function status in patients with periodontitis

Next, we investigated immune cell infiltration and immune function in periodontitis using ImmuCellAI and GSVA. In the GSE16134 cohort, we observed multiple immune cells, including CD4 + T cells, Th17, follicular helper T cell (Tfh), nTreg, CD4 naive T cells, iTreg, Th1, Th2, dendritic cells (DC), exhausted T cells, and CD8⁺ T cells (Fig. 3A). Consistent results were observed in the GSE10334 cohort (Fig. 3D). We then examined the association between LMRG scores and the infiltration levels of different immune cells in periodontitis and healthy tissues. A significant positive correlation was found between LMRG scores and the infiltration of CD4⁺ T cells, CD4 naive T cells, nTreg, Th1, Th17, Tfh, and exhausted T cells in periodontitis tissues in the GSE16134 dataset (Fig. 3B). In contrast, LMRG scores were significantly negatively correlated with the infiltration of macrophages and cytotoxic T cells (Fig. 3B). Similar correlation patterns were identified in healthy participants in the GSE16134 dataset (Fig. 3C) and all participants in the GSE10334 dataset (Fig. 3E, F). Boxplots demonstrated that periodontitis tissues had significantly higher infiltration levels of CD4⁺ T cells, Tfh, and Th17 than healthy tissues in both the GSE16134 and GSE10334 datasets (Fig. 3G, H). However, the infiltration levels of gamma delta T cells and macrophages in periodontitis were significantly decreased compared to healthy individuals (Fig. 3G, H).

GSVA was performed to investigate immune function in periodontitis. The results indicated that antigen presentation processes were significantly activated in periodontitis, as evidenced by increased scores for APC co-stimulation, HLA, and aDCs (Figures SA, B). This further triggered T cell activation and inflammation responses in periodontitis, as indicated by the increased scores of multiple T cell subsets and the inflammation-promoting score in GSVA (Figure S3A, B). GSEA was then employed to investigate significantly altered biological processes and pathways between periodontitis and healthy participants. Consistent with the GSVA analysis, multiple immune-related processes and inflammation response-related pathways were significantly activated in periodontitis, as evidenced by the activation of inflammatory response, TNF-α signaling via NF-κB, IL2-STAT5 signaling, IL6-JAK-STAT3 signaling, and chemokine signaling pathways (Fig. 3I and Figure S4A). Furthermore, epithelial-mesenchymal transition (EMT), angiogenesis, apoptosis, and metabolism pathways were also significantly activated in periodontitis (Fig. 3I and Figure S4A). Collectively, these results revealed that the immune microenvironment plays pivotal roles in periodontitis.

scRNA-seq analysis identifies cell subtypes and cell communication in periodontitis

Considering the limitations of bulk RNA-seq data in reflecting the immune microenvironment components in patients, we performed scRNA-seq analysis to decipher the immune microenvironment of periodontitis at single-cell resolution. After quality control and dimensionality reduction, we identified 12 distinct clusters in both periodontitis and healthy gingival tissue (Fig. 4A and Figure S4B). The top five marker genes of each cluster were visualized using bubble plots (Fig. 4B) and heatmaps (Figure S4C). Ultimately, using the SingleR algorithm, we identified eight main cell types among the 12 clusters: plasma cells, monocytes, multipotent progenitor cells (MPP), fibroblasts, keratinocytes, CD8⁺ effector memory T cells (Tem), microvascular (mv) endothelial cells, and class-switched memory B cells (Fig. 4C). We then validated the expression levels of key LMRGs at the single-cell level. Our analysis revealed that ADGRF5 and FABP4 were primarily expressed in fibroblasts and microvascular endothelial cells, while CLN8 was expressed across all single-cell types. In contrast, OSBPL6 was not expressed in CD8⁺ Tem cells, plasma cells, or monocytes (Fig. 4D and Figure S5A-C). Subsequently, we mapped the cell communication network among these cell types, identifying strong interactions mediated by multiple signaling pathways, including MHC-II signaling, CXCL signaling, and ADGRE5 signaling (Fig. 4E). These pathways are crucial in antigen processing and the inflammatory response. Further investigation of these pathways revealed that HLA and CD4 primarily contributed to signaling from various cells to MPP (Figure S6A), CXCL12 and CXCR4 contributed to signaling from plasma cells to other cells (Figure S6B), and ADGRE5 and CD55 contributed to signaling from diverse cells (Figure S6C).

Single-cell GSEA was performed to investigate the most relevant biological functions of different cell subtypes in periodontitis. The results suggested that monocytes and MPPs primarily contributed to the inflammatory response and immune reaction in periodontitis by activating pathways such as the inflammatory response, TNF-α signaling via NF-κB, IL6-JAK-STAT3 signaling, IL2-STAT5 signaling, complement, and reactive oxygen species pathways (Figure S6D). Fibroblasts were mainly involved in processes like EMT, myogenesis, and Wnt/β-catenin signaling (Figure S6D). Keratinocytes and mv endothelial cells were engaged in cell proliferation by activating the E2F targets, MYC targets V1, and MYC targets V2 pathways (Figure S6D). We further performed single-cell GSEA to investigate the significantly altered biological processes of these cell types in periodontitis. Consistent with previous findings, the results indicated that the IL6-JAK-STAT3 signaling, inflammatory response pathway, TNF-α signaling via NF-κB, and interferon gamma response pathways were significantly activated in monocytes (Fig. 4F and Figure S6D). Meanwhile, EMT was exclusively activated in fibroblasts. The process of adipogenesis was activated in multiple cell types in periodontitis, except for CD8⁺ Tem and class-switched memory B cells (Fig. 4F and Figure S6D). Taken together, these findings highlight the important role of monocytes in periodontitis through the activation of inflammatory response pathways.

Deciphering the heterogeneity among different monocytes clusters in periodontitis

Given the important role and heterogeneity of monocytes in periodontitis, we further investigated the role of different subsets of monocytes by clustering them into distinct groups. Using the t-SNE algorithm, we identified five monocyte clusters, with the top five marker genes for each cluster summarized in Fig. 5A. Based on the unique expression patterns of these marker genes (Figure S7), we annotated the monocyte clusters as follows: C1 (APOE⁺SELENOP⁺ monocytes), C2 (CD1E⁺ monocytes), C3 (GZMB⁺PTGDS⁺ monocytes), C4 (CLEC9A⁺ monocytes), and C5 (IGHA1⁺ monocytes) (Fig. 5B). Trajectory analysis revealed three differentiation states among these monocyte subtypes. C1 represented the earliest stage of differentiation, while C4 appeared at the terminal stage (Fig. 5C). Additionally, BEAM analysis was conducted to investigate differentially expressed genes (DEGs) before and after branch point 1. We identified the top 100 DEGs, categorizing them into three subtypes as shown in Fig. 5D.

GO enrichment analysis demonstrated that DEGs in cluster 1 were mainly involved in neutrophil activation, inflammatory response, and immune response. DEGs in cluster 2 were primarily associated with signal peptide processing, while DEGs in cluster 3 were linked to antigen presentation and T cell-mediated cytotoxicity (Fig. 5D). We further performed GSVA to explore the significantly altered biological processes in these monocyte subtypes. As illustrated in Fig. 5E, clusters C1 and C2 were predominantly involved in immune and inflammatory response pathways (Fig. 5E). Additionally, the EMT process was activated in C1 and C2 monocytes (Fig. 5E). In contrast, cell proliferation, adipogenesis, fatty acid metabolism, and oxidative phosphorylation were activated in C3, C4, and C5 monocytes (Fig. 5E). Single-cell GSEA was conducted to validate these findings, with consistent results observed (Figure S8). Taken together, different monocyte subtypes have distinct roles in periodontitis.

Investors are probably wondering, where do we go from here?

Policy sets the backdrop to climate action, yet regulatory treatment of carbon emissions is uneven around the world. Furthermore, carbon markets exist as mandatory (compliance) schemes as well as voluntary programs. Economists debate the merits of implementing quantity-based instruments (ETS), or price-based (fuel taxes) policies, without a strong consensus on an optimal approach for adoption in the current context. Green incentives and decarbonization subsidies, like tax credits for clean energy investment, have also been a major factor in shaping markets. As a result, investors face the challenge of investing in markets at various stages of developing their domestic policies for pricing carbon emissions. Are we in the midst of a “carbon carry trade” where capital is drawn to regions where emissions are relatively underpriced compared to regions where the cost of carbon is internalized by markets?

Even since this graphic was produced in May 2024, major markets have implemented pricing mechanisms. Brazil established the Brazilian Greenhouse Gas Emissions Trading System in November 2024, to be phased in over the next five to six years. Indonesia has been advancing cap-and-trade with plans to expand coverage to industrial sectors.

Investors are closely following developments in the European Union and its Emissions Trading System, which is set to decrease the quantity of free emissions allowances, leading to potentially higher prices over time. New mechanisms are arising, such as carbon tariffs, which require importers to pay the same carbon prices as domestic producers to reduce “carbon leakage”. The first carbon-tariff system, the EU Carbon Border Adjustment Mechanism (CBAM), takes effect in October 2023 for reporting purposes, and becomes chargeable in January 2026. CBAM has the potential to significantly influence market development as it serves as an incentive for other countries to price emissions. Why would a country let other governments collect revenues that they could be collecting on local production that is exported to the EU?

Understanding the barriers to evaluating the cost of carbon for portfolios and assets

FCLTGlobal convened members for a working group in Q4 2024, with asset owners and asset managers sharing their experiences on the theme of climate change and managing transition and physical risks in portfolios. Member CEOs met at FCLTGlobal’s annual Summit in January 2025, where the subject of addressing future climate costs featured in the agenda. CEOs were concerned with carbon pricing realities and how low carbon prices translate into weak investment signals. They also highlighted the benefits of scenario planning based on climate science fundamentals – actual levels of CO2 in the atmosphere and projections going forward. Several things stood out from these discussions, including:

• Uncertain financial impact – Regulatory uncertainty and the timing of policy development complicate short- and long-term capital allocation decisions. Individual company responses, as well as decarbonization pathways and how emissions cost will affect future earnings also contributes to financial uncertainty. An investor may determine little impact to future earnings, if, for instance, companies can pass through higher costs of carbon to consumers. Pass-through strongly depends on the market structure and the supply-demand equilibrium.
• Low confidence in financial assumptions – Uncertainty around the trajectory and level of carbon prices leads to lower confidence in risk/ return assumptions. Low confidence in assumptions means that information output from models, scenario analysis, and due diligence does not significantly impact or influence investment decision making. There is no singular quantitative model or approach that fully captures the risk of carbon in portfolios. For analyzing real estate investment like a building, it’s more straightforward. A business, with complex supply chain, shipping routes, and energy sources, is much more complicated.
• Focus on immediate financial performance – Some investors fall into the trap of prioritizing short-term financial performance. While carbon prices are comparably low in the current environment, assuming they will be low forever could expose portfolios to future shocks.
• Cost and complexity – While disclosures have been improving, data collection and management as well as the complexity of measuring emissions has been a challenge for investors. Not all companies globally report on their emissions, requiring estimation methods in some instances. Yet you could also say that there is too much data, which makes focusing on what is material and impactful to a business more difficult to process and interpret.

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STAYING AHEAD OF THE CURVE

Broadly speaking, derivatives aid in the allocation of risks and provide tools for companies and investors to manage risks, and they can lower the cost of diversifying portfolios. It is also a compliance tool for meeting emissions caps placed on regulated entities. Companies can lock-in prices on their future carbon emissions through derivatives markets. Investors can trade derivatives contracts, speculate on prices, search for arbitrage opportunities, or hedge portfolio exposures. Negative carbon convenience yields have attracted the attention of carbon traders interested in exploiting arbitrage opportunities between carbon spot and futures markets. Furthermore, Investors can use price signals from carbon derivatives to assess climate transition risk in their portfolios.

TOOLKIT: INDICATORS FOR SCENARIO ANALYSIS

This toolkit focuses on indicators that align with already established investment methodologies and concepts, essentially building on the “finance language” and expertise that committees and boards already possess, while applying it to climate concepts. These indicators are based on readily available data and disclosures and can complement climate scenario analysis using different assumptions on carbon price levels.

Market-implied cost of hedging carbon price exposure: Similar to insurance for physical climate exposure, this indicator asks what the cost is to insure against transition risks for asset classes and individual assets with exposure to carbon price risk. The estimated cost to hedge carbon exposure can be used as a “haircut” to prospective returns, in order to map transition risks in equity industries and investments in private equity and real assets. The implied cost to hedge risks can be computed for an individual company, or at the sector/aggregate level, using underlying assumptions of unabated emissions, or emissions exposed to price risk. One study has found that the cost of hedging tail risks using options, on average, amounts to 2 percent of portfolio assets per year.

NoCEBITDA – Net of Carbon Earnings Before Interest, Taxes, Depreciation, and Amortization: Carbon price risk can leave exposure to fluctuating costs, leading to volatile earnings. Estimations can also include sources of “green revenues” where companies are poised to benefit from higher carbon prices. Carbon adjusted EBITDA over multiple periods forms the basis of valuation models, which makes this tool well suited to analyze private equity, real estate, and infrastructure deals.

Carbon-adjusted financial indicators, EPS, ROI, IRR: Adjusting EBITDA for carbon-related costs and changes in revenue can serve as a basis for calculating other financial indicators, notably earnings per share (EPS), return on investment (ROI), and internal rates of return (IRR) with applications in both public and private markets.

TOOLKIT: CARBON BETA

Carbon beta is a tool designed to measure transition risk in individual stocks and portfolios of stocks. It is a forward-looking measure that determines the extent to which an asset’s price correlates with a carbon risk factor.

The beta of a stock is a traditional finance indicator of risk with a long academic history, and is a concept well understood in the finance world. It simply measures how the value of a stock or portfolio of stocks moves in relation to the market. For the purpose of computing carbon beta, the “market” is redefined as a group of high emitting stocks believed to possess high climate risk and face potential high costs for abating emissions. The carbon beta of an individual stock or portfolio is its valuation in relationship to the carbon risk portfolio. Huij et al. developed a methodology to estimate asset-level climate risk exposure by regressing stock returns on a pollutive-minus-clean portfolio. The authors find that, not surprisingly, climate risk is highest in energy and utility sectors, and lowest in healthcare and financials.

The methodology is relatively simple and straightforward, and investors can readily replicate it using existing historical market data and emissions and transition risk disclosures. Investors can even develop their own methodologies on how climate risk is defined in the “market” portfolio. For example, the pollutive-minus-clean portfolio assumes that all emissions are the same, while investors might assign greater weight to some emissions over others. For firms whose emissions occur in the production of goods that reduce emissions elsewhere (e.g. solar panels), or that operate in sectors for which abatement is expected to be easier, investors might perceive lower risk exposures. Interestingly, Huij et al. find that returns to stocks with high carbon betas are lower during months in which climate change is more frequently discussed in the news, during months in which temperatures are abnormally high, and during exceptionally dry months.

Carbon beta can also be a tool to identify firms that are investing in green technologies. Green innovation is largely driven by firms in the energy sector, yet paradoxically these firms are generally amongst the worst performers on environmental issues. Using green patents as an indicator, Huij et al. test the association of carbon beta with green innovation and find that green innovators are less exposed to climate risk, including firms in the energy sector.

Carbon beta is but one factor to analyze amongst many. It is an indicator that can be used in concert with other factors while recognizing how factors can interact.

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INTRODUCTION – WHY CLIMATE EXTERNALITIES CAN’T BE IGNORED

Despite the policy uncertainty and differences around the world, understanding climate-related risks and opportunities is essential to effective long-term capital allocation. Just as interest rates, inflation, and geopolitical risks shape investment decisions, so too do emissions costs, insurance premiums, and the growing financial toll of climate change., ,

The effects of climate have slowly manifested not only in transition risk (e.g. a grey premium for carbon-intensive sectors like oil and gas), but also in physical risk (e.g. depressed exit-multiples among real estate developments in climate-affected areas)., While some companies still view such risks as immaterial to their strategy, over a long-enough horizon, these previously insignificant costs, are set to hit the bottom line at an ever-consequential rate.

Climate externalities are the unpriced costs or benefits of business activities that impact the climate system, such as greenhouse gas emissions, water usage, or land degradation. These externalities vary by sector and jurisdiction but are increasingly prevalent worldwide. While some companies may currently view these risks as immaterial or choose to wait for regulatory developments, proactively addressing them (through, e.g. pricing carbon internally, hedging future climate risk, and scenario planning) can help manage uncertainty and enhance competitive positioning. Incorporating climate externalities into capital allocation decisions today—before they are fully reflected in regulations or markets—can enable companies to manage risks, seize opportunities, and build long-term resilience. While carbon emissions are among the most common and financially significant externalities, especially in emissions-intensive sectors, companies with inherently low carbon footprints—like those in software or professional services—may find other externalities more material. For instance, data centers operated by tech firms often consume substantial amounts of water for cooling purposes, which can strain local water resources, particularly in drought-prone regions.

Many of these climate externalities ultimately are internalized and show up in the economics of a project, whether through costs, pricing dynamics, or asset values. Whatever assumptions companies make about them will inevitably reshape the risk-return profile of their investments.

As such, identifying which climate externalities are material—when and where—and determining appropriate responses is a vital tool in helping companies stay ahead of the curve. By factoring future climate-related costs into investment decisions, companies can maximize long-term value.

In practice, the path to do so is complex. While some firms actively embed climate considerations into their capital allocation decisions, many still struggle to weigh long-term climate risks against short-term financial pressures—particularly when the costs of carbon remain uncertain or inconsistently applied.

Exhibit 6 (page 20) illustrates how emissions pricing might alter investment attractiveness between two hypothetical projects: when weighing two projects (A and B), their attractiveness to the company varies depending on whether the analysis factors in the future cost of carbon.

If emissions are priced for the long-term project A may make more sense over project B, and vice versa if emissions are free. In truth, many payoffs to these investments follow a “j-curve”: companies are faced with the decision to “pay now or pay later” when it comes to climate (and carbon especially).

This challenge is amplified by the fact that many of the tools and signals needed to inform long-term climate-adjusted decisions—such as internal carbon pricing, external regulatory coverage, and credible forward commitments—remain challenging to adopt or apply.

While still early days, a few key data points illustrate how this plays out across company practice, market coverage, and credibility:

1. 18% of MSCI ACWI companies reported using an internal carbon price as of 2024. Among those that do, the median price is $49 per ton.
2. 24% of global carbon emissions are currently covered by an emissions trading system (ETS) or carbon tax —up from 13% in 2014.
3. 26% of S&P Global BMI companies generated unpriced environmental costs that exceeded their net income—suggesting that externalities remain financially invisible in many corporate accounts.

Evidence suggests that leading firms are increasingly developing internal views on climate externalities— particularly carbon—and are actively incorporating them into investment and strategy decisions.

However, many others do not quantify or operationalize these externalities in capital allocation, especially in a time when carbon prices are uncertain, or policies are evolving. Deferring action altogether, adopting a “waitand-see” approach will lead companies to be behind the curve.

GETTING AHEAD OF THE CURVE IS EASIER SAID THAN DONE

Amid mounting short-term performance pressures and unclear financial payoffs, few companies meaningfully integrate climate considerations like carbon pricing into capital allocation decisions. , , ,

For the past 12 months, companies have been scaling back sustainability pledges, as many over-promised and under-delivered. Companies have been called out for “greenwashing” and underperformance.

Treating climate as a financial issue requires overcoming several key challenges that need to be overcome, as seen in the table below:

In such a challenging current environment, how can companies perform sufficiently now while keeping an eye toward the future, and what can investors do to support companies in their journeys, while fulfilling their mandates and fiduciary duty?

TURNING CLIMATE INSIGHTS INTO ACTION

As companies weigh near-term pressures against long-term climate strategy, many are seeking clearer direction—not in theory, but in practice. They want to know: Who is doing this well? What frameworks are working? And how can we make sound decisions amid evolving regulations and stakeholder expectations?

In our discussions with corporate and investor leaders, the message is consistent. Organizations are not just looking for metrics—they are looking for clarity. Clarity about how others are embedding climate into real capital allocation decisions. Clarity around how to navigate uncertainty across jurisdictions. And clarity about how to act decisively, without getting ahead of their boards or behind their peers.

The toolkits on the following pages aim to meet that demand. By showcasing leading company examples and practical tools—ranging from internal carbon pricing and marginal abatement cost curves to climate-adjusted financial metrics—we provide a set of forward-looking approaches for navigating uncertainty, aligning decisions with long-term value, and staying ahead of the curve.

CONCLUSION

To get ahead of the curve on climate, forward-thinking companies are integrating climate externalities into overall strategy and capital allocation.

Internalizing climate externalities like carbon is not a one-time fix—it requires ongoing recalibration of strategy, risk management, and investor communication. Organizations that begin now—by applying internal carbon pricing, stress-testing projects against future scenarios, and translating climate metrics into familiar financial language—will be better positioned to capture upside and avoid downside in a decarbonizing economy.

The future will reward those who align capital with long-term value creation. Getting ahead of the curve today means thriving in the economy of tomorrow.

Link to the full report can be found here. 

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Design

A cross-sectional design that follows the STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist was adopted. Figure 2 is a flowchart of the study design.

Study setting, participants, and recruitment

The study setting was Accra, Ghana, and the participants were community-dwelling middle-aged and older adults aged 50 years or older. Multistage sampling was utilised to select the participants. We first classified the neighbourhoods of Accra into four cardinal blocks (i.e., north, south, west, and east) and randomly selected a representative number of neighbourhoods from each block. The participants were selected randomly from all selected neighbourhoods based on three selection criteria: (1) being aged 50 years or older; (2) being a permanent resident of Accra, and (3) availability and willingness to participate in the study. We calculated the minimum sample size necessary with the Daniel Soper’s sample size calculator for structural equation modelling [37, 38] based on standard statistics (i.e., moderate effect size = 0.3; power = 0.8, and α = 0.05). The sample size reached was 823, but we increased this number by 10% to allow for attrition. Thus, the minimum sample size of this study was 905.

Variables, operationalization, and measures

WSH, the dependent variable, was measured with a 5-item scale with five descriptive anchors (i.e., 1 – strongly disagree, 2 – disagree, 3 – somewhat agree, 4 – agree, and 5 – strongly agree). This measure was adopted in whole from a previous study [9] and was developed based on our earlier definition of WSH. Some of its items are “Overall, my workplace supports me in living a healthier life” and “Most employees here have healthy habits”. It yielded satisfactory internal consistency in the form of Cronbach’s α ≥ 0.7 (overall α = 0.76; men’s α = 0.74; women’s α = 0.77), which is within the recommended cut-off point [9, 39]. Scores on this tool obtained by summing up its items range from 5 to 25, with larger scores indicating higher WSH.

Illness avoidance and functioning were measured with sub-scales from a previously validated successful ageing measure [11], associated with five descriptive anchors (i.e., 1 – strongly disagree, 2 – disagree, 3 – somewhat agree, 4 – agree, and 5 – strongly agree), and comprised 4 and 9 items respectively. Illness avoidance, the dependent variable, is a measure of overall health and the avoidance of medication as well as therapy. Some of its items are “I did not use medication or therapy” and “I was healthy enough to move around freely”. Functioning, the mediating variable, is a measure of cognitive functioning and how well one could perform physical and social tasks independently. Some of its items are “I had enough energy for daily life” and “When I tried to recall familiar names or words, it was not difficult for me to do so”. Illness avoidance (overall α = 0.72; men’s α = 0.76; women’s α = 0.81) and functioning [overall α = 0.84; men’s α = 0.87; women’s α = 0.80] produced Cronbach’s α ≥ 0.7. The ranges of scores on illness avoidance and functioning were 4–20 and 9–45 respectively, with larger scores indicating higher illness avoidance and functioning.

OHL, the moderating variable, was measured with a 12-item standard measure adopted in whole from a previous study [36]. The scale was associated with four descriptive anchors [i.e., 1 – strongly disagree, 2 – disagree, 3 – agree, and 4 – strongly agree] and produced satisfactory Cronbach’s α ≥ 0.7 (overall = 0.75; men’s = 0.82; women’s = 0.74). Its scores range from 12 to 48, with higher scores indicating higher OHL. Appendix 1 shows items used to measure WSH, illness avoidance, functioning, and OHL.

Eight potential covariates were measured following previous research [7, 34, 40, 41]. Chronic disease status was measured with a single question asking participants to report the number of chronic conditions they had, and the responses were coded into two groups (none – 1, and one of more – 2). Self-reported health was measured with a single question asking the participants to report whether their health was poor or good (poor – 1, and good – 2). Like chronic disease status and self-reported health, sex (men – 1, and women – 2), and marital status (not married – 1, and married – 2) were measured as categorical variables and coded into dummy-type variables. Job tenure, age, education, and income were measured as discrete variables. Job tenure was how long (in years) participants had worked in their current organization whereas age was a measure of chronological age. Education was measured as years of schooling whereas income was measured as the individual’s gross monthly earnings in Ghana cedis.

Instrumentation

Data were collected with a self-reported questionnaire comprising three sections. The first section presented a statement of the study’s aim, importance, ethical considerations, and general survey completion instructions. The second section presented measures on WSH, OHL, functioning, and illness avoidance, whereas the final part captured questions on the covariates and personal factors. We avoided or minimised Common Methods Bias (CMB) at the survey design stage by following recommendations in the literature [34, 42, 43] to structure sections and questions in the questionnaire. In this vein, specific instructions for completing each scale and section in the right context were provided. The general instructions provided guided the participants to avoid errors in their completion of the survey. Standard scales with concise and unambiguous items or questions were used. In the second stage, Harman’s one-factor approach, a statistical procedure, was followed to investigate the absence of CMB in the data.

This technique required the use of an exploratory and confirmatory factor analyses to assess the factor structures of the scales used. With this technique, the absence of CMB in the data is confirmed if two or more factors are produced on each scale, or variances extracted are less than 40% [42, 43]. In the exploratory factor analysis, each scale yielded at least two factors, and each factor accounted for less than 40% of the total variance. WSH produced two factors (factor loadings of items ≥ 0.5; variance explained by factor 1 = 31.12, and variance explained by factor 2 = 23.09). OHL (number of factors extracted = 4), functioning (number of factors extracted = 3), and illness avoidance (number of factors extracted = 2) yielded similar results. Confirmatory factor analysis produced consistent results, signifying the absence of CMB in the data.

Ethics and data collection

The study received ethical review and clearance from the ethics review board of the Africa Centre for Epidemiology (no. 005-10-2022-ACE) after the board reviewed the study protocol. All the participants provided written informed consent before participating in the study. We gathered data with three specially trained research assistants who administered questionnaires at designated centres. Some participants could not complete the questionnaire at the centres, so they were allowed to take the questionnaires home and return them over two weeks through a private courier hired by the researchers. Data were gathered over four weeks between July and August 2023. Out of 1501 questionnaires administered, 1015 were analysed, 465 were not returned by the participants, and 21 were discarded because at least 50% of their questions were not answered.

Statistical analysis

We utilised SPSS 28 (IBM Inc., New York, USA) to summarise the data and perform exploratory data analysis, including the first sensitivity analyses for the ultimate covariates. Amos 28 was used to test the moderated mediation model. Data were summarised with descriptive statistics [i.e., frequency, and mean], enabling us to identify missing data. Marital status was the only variable with 1% missing data, but we performed the exploratory data analysis with the missing values as they were less than 10% (of the data on marital status) and were randomly distributed [34, 41]. We found no outliers in the data after using box plots to visualize the distribution of the data on all continuous variables. Previous studies [7, 34, 40] were then followed to perform the first sensitivity analysis for the ultimate covariates. We utilised this analysis to ensure that only measured covariates likely to confound the primary relationships were incorporated into the moderated mediation model. After following some standard steps (see Appendix 2), none of the measured covariates qualified as the ultimate covariate.

Figure 3 shows the statistical moderated mediation model tested with Hayes’ Process Model [44, 45] through structural equation modelling. To create the interaction term (i.e., WSHxOHL), we centred the moderator (i.e., OHL) and multiplied it with WSH in harmony with Haye’s Process Model. The moderated mediation was fitted on the whole sample after computing the basic path coefficients (i.e., a, b, and c; see Fig. 3), Simple Slope (SS), Conditional Indirect Effect (CIE), and Index of Moderated Mediation (i.e., InModMed) using the “user-defined estimands” function in Amos 28. Appendix 3 shows the equations used to estimate the SS and CIE on the whole sample and sub-samples (i.e., men and women). The constant in each equation is the standard deviation of the moderator variable. The InModMed, SS, and CIE were estimated at different levels [i.e., low and high] of the moderator variable. The above parameters and their significance were based on 2000 biased corrected sampling iterations (bootstraps) with a 95% confidence interval.

In the second sensitivity analysis, the statistical model (see Fig. 3) was fitted for men and women, and the relevant parameters (i.e., direct effects, indirect effects, SS, CIE, and InModMed) were estimated for these samples. A minimum of p < 0.05 was used to detect the statistical significance of the effects. The moderation effect was visualized with figures depicting the effect of WSH on functioning at two levels (i.e., low, and high) of OHL. Multivariate normal distribution of the data was not achieved in fitting the models possibly owing to the relatively large sample used [46], but this violation of the assumption was corrected with our 2000 biased-corrected bootstraps [46].

In a world where medical advancements are rapidly extending lifespans, the conversation around quality of life posttreatment is becoming increasingly important. For reproductive-age cancer patients, a critical component of that conversation is fertility preservation. Katherine McDaniel, MD, a reproductive endocrinology and infertility specialist with the University of Southern California and HRC Fertility, shed light on the evolving landscape of this crucial field, emphasizing the urgency, challenges, and hope it offers.

What Fertility Preservation Entails

McDaniel explains that fertility preservation is a vital option for patients whose cancer treatments, such as chemotherapy, radiation therapy, or surgery, could impact their reproductive health. The primary focus for women is freezing eggs, which can also be combined with sperm to freeze embryos. For men, the core practice is freezing sperm.

She noted that while these are the main methods, there are advanced techniques for patients who aren’t candidates for traditional methods. “For instance, prepubertal girls aren’t candidates for egg freezing because they don’t have a mature connection between their brain and their ovaries,” McDaniel said. For these patients, a pioneering technique called ovarian tissue cryopreservation has emerged. “We actually remove a whole ovary, we freeze it, and then transplant it back into the patient when they’re ready to conceive, oftentimes decades down the road.”

A Shift in Awareness and Urgency

McDaniel has witnessed a significant change in the landscape of fertility preservation over the past decade, primarily in the form of increased awareness. “I think 10 or 15 years ago, many, many patients simply weren’t counseled on fertility preservation in the setting of their cancer diagnoses,” she said. “Understandably, because their teams wanted to focus on treating their cancer, saving their lives, which, of course, is extremely important.”

Today, however, she sees a greater understanding within the oncology community, leading to more referrals. A crucial part of this shift is the recognition of the urgency involved. McDaniel emphasized that she wants to see patients as soon as they receive their diagnosis, so they can focus on fertility preservation, “maybe even before their oncology team has a treatment plan.” This early intervention is key to ensuring a patient’s reproductive future isn’t overlooked in the rush to begin life-saving treatment.

The Barriers and the Hope for Change

Despite the growing awareness, significant barriers remain, with access to care being a major issue.

“So many of the treatments that we offer aren’t covered under insurance, so the cost is very prohibitive for a lot of patients,” McDaniel noted. She pointed to the variance in state-level mandates, highlighting the progressive step taken in California with Senate Bill 600, which mandates fertility preservation coverage. However, many states lack such mandates, leaving patients with substantial out-of-pocket costs.

“That access to care issue is really big and will continue to be an issue over the next few years and decades,” she said.

Additionally, as the age of cancer diagnoses decreases for certain cancers, a new challenge emerges: approaching young patients with sensitivity. McDaniel acknowledged that talking to teens about their reproductive future, especially those who may have had limited contact with the health care system, requires a delicate touch.

“It’s definitely something that we all need to remain sensitive to, and perhaps even more sensitive to as we have more and more young patients diagnosed with cancer,” McDaniel said.

A Call to Action for Oncology Colleagues

McDaniel has a clear message for her oncology colleagues: “Keeping the ASCO recommendations in mind that all reproductive age patients should be counseled on the impact of the cancer therapies on their fertility.” Her primary piece of advice is to refer patients as soon as possible. “As soon as that diagnosis hits your inbox in a reproductive-age patient, please call us, email us, refer us to your patients,” she said.

McDaniels highlighted that that fertility preservation treatments are time-sensitive and can be expedited to avoid treatment delays. “We are really sensitive to the expedited nature of treatment. We want to see these patients within 24 hours of them calling our clinics.”

She also highlighted the swift nature of the process itself, noting that treatment can often begin on the first day and be completed within 2 weeks. “We absolutely do not want to delay treatment in any way, shape, or form,” she affirmed. The message is clear: early, swift collaboration between oncology and reproductive endocrinology is the best path forward for patients facing a cancer diagnosis.

Within the U.S., discussion of Trump’s Nvidia deal has focused on what it means for China’s government’s and Chinese companies’ ability to get their hands on cutting-edge U.S. technology. But several other countries and companies are likely studying the deal closely to see if they might get an opening to sell to China as well. 

Trump’s Nvidia deal “tells you that [U.S.] national security is not really the issue, or has never been the issue” with export controls, says Mario Morales, who leads market research firm IDC’s work on semiconductors. Companies and countries will “probably have to revisit what their strategy has been, and in some cases, they’re going to break away from the U.S. administration’s policies.”

“If Nvidia and AMD are given special treatment because they’ve ‘paid to play’, why shouldn’t other companies be doing the same?” he adds.

Getting allies on their side

The Biden administration spent a lot of diplomatic energy to get its allies to agree to limit their semiconductor exports to China. First, Washington said that manufacturers like TSMC and Intel that wanted to tap billions in subsidies could not expand advanced chip production in China. Then, the U.S. pushed for its allies to impose their own sanctions on exports to China. 

“Export controls and other sanctions efforts are necessarily multilateral, yet are fraught with collective action problems,” says Jennifer Lind, an associate professor at Dartmouth College and international relations expert. “Other countries are often deeply unenthusiastic about telling their firms—which are positioned to bring in a lot of revenue, which they use for future innovation—that they cannot export to Country X or Country Y.”

This translates to “refusing to participate in export controls or to devoting little or no effort to ensuring that their firms are adhering to the controls,” she says.

 Paul Triolo, a partner at the DGA-Albright Stonebridge Group, points out that “Japanese and Dutch officials during the Biden administration resisted any serious alignment with U.S. controls,” and suggests that U.S allies “will be glad to see a major stepping back from controls.”

Ongoing trade negotiations between the U.S. and its trading partners could weaken export controls further. 

Chinese officials may demand a rollback of chip sanctions as part of a grand bargain between Washington and Beijing, similar to how the U.S. agreed to grant export licenses to Nvidia and AMD in exchange for China loosening its controls on rare earth magnets. 

Japan and South Korea may also bring up the chip controls as part of their own trade negotiations with Trump. 

‘Expect continuing diversions’

A separate issue are controls over the transfer of Nvidia GPUs. The U.S. has leaned on governments like Singapore, Malaysia and the United Arab Emirates to prevent advanced Nvidia processors from making their way to China.

Scrutiny picked up in the wake of DeepSeek’s surprise AI release earlier this year, amid allegations that the Hangzhou-based startup had trained its powerful models on Nvidia processors that were subject to export controls. (The startup claims that it acquired its processors before export controls came into effect).

As of now, the two chips allowed to be sold in China–Nvidia’s H20 and AMD’s MI308–are not the most powerful AI chips on the market. The leading-edge processors, like Nvidia’s Blackwell chip, cannot be sold to China. 

That means chip smuggling will continue to be a concern for the U.S. government. Yet “enforcement will be spotty,” Triolo says. “The Commerce Department lacks resources to track GPUs globally, hence expect continuing diversions of limited amounts of GPUs to China via Thailand, Malaysia, and other jurisdictions.”

Triolo is, instead, focused on another loophole in the export control regime: Chinese firms accessing AI chips based in overseas data centers. “There is no sign that the Trump Commerce Department is gearing up to try and close this gaping loophole in U.S. efforts to limit Chinese access to advanced compute,” he says. 

How much will the global supply chain change?

Not all analysts think we’ll see a complete unraveling of the export control regime.

“The controls involve a complex multinational coalition that all parties will be hesitant to disrupt, given how uncertain the results will be,” says Chris Miller, author of Chip War: The Fight for the World’s Most Critical Technology. He adds that many of these chipmakers and suppliers don’t have the same political heft as Nvidia, the world’s most valuable company. 

Yet while these companies may not be as politically savvy as Nvidia, they’re just as important. TSMC, for example, is the only company that can manufacture the newest generation of advanced chips; ASML is the only supplier of the extreme ultraviolet lithography machines used to make the smallest semiconductors. 

“I don’t believe it’s leverage that the Trump administration will easily give away,” says Ray Wang, a semiconductor researcher at the Futurum Group.

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YouTube has started using artificial intelligence (AI) to figure out when users are children pretending to be adults on the popular video-sharing platform amid pressure to protect minors from sensitive content.

The new safeguard is being rolled out in the United States as Google-owned YouTube and social media platforms such as Instagram and TikTok are under scrutiny to shield children from content geared for grown-ups.

A version of AI referred to as machine learning will be used to estimate the age of users based on a variety of factors, including the kinds of videos watched and account longevity, according to YouTube Youth director of product management, James Beser.

“This technology will allow us to infer a user’s age and then use that signal, regardless of the birthday in the account, to deliver our age-appropriate product experiences and protections,” Beser said.

“We’ve used this approach in other markets for some time, where it is working well.”

The age-estimation model enhances technology already in place to deduce user age, according to YouTube.

Users will be notified if YouTube believes them to be minors, giving them the option to verify their age with a credit card, selfie, or government ID, according to the tech firm.

Social media platforms are regularly accused of failing to protect the well-being of children.

Australia will soon use its landmark social media laws to ban children under 16 from YouTube, a top minister said late last month, stressing a need to shield them from “predatory algorithms”.

Communications Minister Anika Wells said four-in-ten Australian children had reported viewing harmful content on YouTube, one of the most visited websites in the world.

Australia announced last year it was drafting laws that will ban children from social media sites such as Facebook, TikTok and Instagram until they turn 16.

“Our position remains clear: YouTube is a video sharing platform with a library of free, high-quality content, increasingly viewed on TV screens,” the company said in a statement at the time. “It’s not social media.”

On paper, the ban is one of the strictest in the world. It is due to come into effect on December 10.

The legislation has been closely monitored by other countries, with many weighing whether to implement similar bans.