Gujarat, 22 January 2026 – Balaji Wafers, one of India’s largest snack food brands, today announced that it has entered into a definitive agreement to receive a strategic investment from General Atlantic, a leading global investor. Financial terms of the transaction were not disclosed.
Founded in 1981 by the Virani family, Balaji Wafers has grown from a home-based enterprise into one of India’s largest packaged snack brands. The Company has built its brand on the promise of consistent high-quality, taste and availability, which is supported by its continuous investment in automation, supply chain, innovation, as well as its people and channel partners. Balaji Wafers offers a diverse portfolio across Namkeen, Western snacks, potato wafers, noodles, chikki, papad, and confectionery, all of which have been well received by consumers. Over the years, the Company has successfully scaled its operations from its home base in Gujarat to become a leading player across multiple states in India. In addition to its strong domestic presence, Balaji Wafers exports its products to around 25 countries worldwide.
With General Atlantic’s investment, Balaji Wafers will focus on further strengthening key corporate functions across the Company and accelerating innovation. Drawing on General Atlantic’s global expertise in the food and consumer sectors, the Company plans to accelerate its expansion across India.
Chandubhai Virani, Founder & Chairman at Balaji Wafers, said: “This partnership marks an important milestone in our journey. General Atlantic’s deep understanding of consumer businesses, track record of working with founder families and long-term approach to value creation, align well with our vision for Balaji Wafers.” Keyur Virani, Whole-time Director, added: “General Atlantic’s investment will support our efforts to establish and operate world-class facilities, invest in innovation and build a professional team to help drive the next phase of growth for the Company. We are excited to extend our footprint across India while staying true to the quality and taste that our consumers trust.”
Shantanu Rastogi, Managing Director and Head of India at General Atlantic, said: “Balaji Wafers is a true Indian success story. The Company has modernised its production capabilities while preserving the flavour and quality that its consumers have grown to love. We see significant growth potential in India’s packaged snacks market as households increasingly seek affordable, convenient and high-quality food products. Balaji Wafers is well positioned to capitalise on this opportunity, and we look forward to partnering with Chandubhai, Keyur and the entire Balaji team as the Company enters its next phase of growth.”
Intensive Fiscal Services Pvt. Ltd. acted as the exclusive advisor to Balaji Wafers. Under the leadership of Mr. D.K. Surana, Intensive Fiscal Services is a leading investment bank in the consumer sector.
The transaction is subject to customary regulatory approvals and is expected to close later in 2026.
About Balaji Wafers
Balaji Wafers is one of India’s largest snack food brands. Headquartered in Gujarat, the company is known for its wide portfolio of high-quality and affordable snack products. Supported by advanced manufacturing facilities, Balaji Wafers has established a robust and expansive retail footprint across multiple regions of India.
About General Atlantic
General Atlantic is a leading global investor with more than four and a half decades of experience providing capital and strategic support for over 830 companies throughout its history. Established in 1980, General Atlantic continues to be a dedicated partner to visionary founders and investors seeking to build dynamic businesses and create long term value. Guided by the conviction that entrepreneurs can be incredible agents of transformational change, the firm combines a collaborative global approach, sector specific expertise, a long-term investment horizon, and a deep understanding of growth drivers to partner with and scale innovative businesses around the world. The firm leverages its patient capital, operational expertise, and global platform to support a diversified investment platform spanning Growth Equity, Credit, Climate, and Sustainable Infrastructure strategies. General Atlantic manages approximately $118 billion in assets under management, inclusive of all strategies, as of September 30, 2025, with more than 900 professionals in 20 countries across five regions. For more information on General Atlantic, please visit: www.generalatlantic.com.
Australian shares shot higher on Thursday to recoup part of their recent losses, after Donald Trump dropped a tariff threat used against European allies amid his pressure campaign to gain control of Greenland.
The de-escalation fuelled a rally in global sharemarkets that flowed into Australia, sending the benchmark S&P/ASX 200 briefly above the 8,860 point mark, before a slight easing.
The US president’s retreat once again rewarded dip buyers, who have ridden the “Trump Always Chickens Out” – or TACO – trade strategy that relies on the American leader backing down from tariff threats after declaring victory.
Trump has said he has a “framework of a future deal” on Greenland, without elaborating.
But, in an interview with Sky News on Wednesday, a member of Denmark’s parliament, Sascha Faxe, has suggested that the deal Donald Trump claims to have struck with Nato over Greenland is “not real”.
“The thing is, there can’t be a deal without having Greenland as part of the negotiations, first of all,” Faxe said. Veteran financial markets commentator Michael McCarthy said while risk was “building up in the market, all the arrows are pointing up” after the latest easing of geopolitical tension.
“We’ve seen a number of things that in the past could have triggered a very significant correction in the equity market but investors have just shrugged it off,” said McCarthy, from online trading platform Moomoo.
McCarthy cites an outbreak of inflation, serious increase in geopolitical tensions, and risk of a sell off in US bonds as potential triggers for a sustained global equity correction.
A US bond sell off would signal investors have lost faith in US political and economic policies, with reverberations for global markets.
Chris Weston, the head of research at Melbourne-based financial firm Pepperstone, said investors will want to know more about Trump’s framework deal over Greenland’s future before completely discounting further risk in Europe.
“That said, it may not be entirely straightforward – many will want to see the devil in the detail of the deal and the finer details of any agreement, what is truly at stake, and how the deal is articulated from the European side,” Weston said.
Australia’s mineral-tinged share market has been helped by robust commodity prices, with iron ore demand proving resilient and gold and copper trading near record highs.
At the same time, sticky inflation and the growing prospect of a near-term interest rate hike has limited stock market gains.
The ASX momentum paused on Thursday after the release of an Australian jobs report that showed a surge in employment, fuelling the odds of a rate rise as early as next month.
Rising rates are generally bad for stocks, given borrowing costs increase and rival investments like bonds become more attractive.
Australia’s benchmark index was up about 0.6% in afternoon trading on Thursday, representing about $17bn in market value and recouping about half of its losses suffered over the past week.
The Australian dollar has hit a 15-month high against its US equivalent, trading near the US68c mark.
Sumitomo Chemical Receives Clarivate Top 100 Global Innovators 2026 Award Selected as One of the World’s Top 100 Innovative Companies and Institutions for Five Consecutive Years
Jan. 22, 2026
Sumitomo Chemical has been recognized as a Top 100 Global Innovator 2026 by Clarivate, a global technology information services company. This marks the fifth consecutive year since 2022 that Sumitomo Chemical has received this award. Only three companies from the chemical and materials industry in Japan were ranked among the top 100 this year.
This award is given to the top 100 innovative companies and organizations selected by Clarivate from among companies and organizations worldwide based on its own patent-related data and evaluation criteria. Clarivate bases its evaluation on four factors: influence, success rate, geographic investment, and rarity. Of these, Sumitomo Chemical received particularly high praise in the factor of rarity, which is an indicator of the combination of multiple technologies.
Sumitomo Chemical has strengthened its business competitiveness by combining its unique core technologies and development capabilities of products and applications in organic synthesis, catalyst design, production technology, analytical evaluation, quality control, which it has cultivated over many years. In addition, the Company has also created synergies between different technologies. This award recognizes these technological achievements, as well as the strong patent portfolio Sumitomo Chemical has built by steadily filing and obtaining rights for its achievements globally.
Sumitomo Chemical’s long-term vision is to become an Innovative Solution Provider. It aims to remain a company with a global presence through continuously creating innovative products and technological solutions for societal issues across the fields of food, ICT, healthcare, and the environment, and providing them to society at large. The Company will continue to position intellectual property as an important management resource, and will accelerate the development of new products and technologies that contribute to solving societal issues by further promoting research and development and intellectual property activities, which are the foundation for improving its corporate value.
References
Contact
Sumitomo Chemical Co., Ltd. Corporate Communications Dept. https://www.sumitomo-chem.co.jp/english/contact/public/
US President Donald Trump’s proposal to cap credit card costs would be “an economic disaster”, the boss of one of the world’s biggest banks has warned.
JPMorgan Chase chief executive Jamie Dimon said the plan would remove credit from the majority of Americans and hit restaurants, retailers, travel firms and schools.
Trump this month wrote on Truth Social that interest rates on credit cards should be limited to 10% for one year from 20 January.
The cap has yet to come into force and the president did not say how it might be introduced or whether such a move would be legally enforceable.
Asked about the cap at the World Economic Forum (WEF) in Davos, Dimon said: “It would be an economic disaster, and I’m not making that up because our business… we would survive it by the way.”
He said capping interest rates on credit cards at Trump’s suggested level of 10% would be “drastic” and cut access to credit for 80% of Americans, adding that it is “their back up credit”.
In a dig at senators Bernie Sanders and Elizabeth Warren, who have supported such a cap, he said if Trump did go ahead with the plan, it should be trialled in their respective states of Vermont and Massachusetts.
Dimon added: “The people crying the most won’t be the credit card companies, it will be the restaurants, the retailers, the travel companies, the schools, the municipalities because people will miss their water payments.”
Top JP Morgan executives including Dimon had previously warned that a 10% interest rate cap would severely hurt consumers, adding their voices to criticism of the proposal.
Trump doubled down on his suggestion on Wednesday, telling the business news channel CNBC: “I’ve had calls from credit card companies, people that are friends of mine, actually, and I treat them good.
“I respect them greatly, but they make a lot of money, they got to give people a break.”
US banking associations have said capping interest rates would make it harder for people to access credit and be “devastating” for millions of families and small businesses.
The average interest rate for credit cards in the US is roughly 20%.
In his statement on social media on 13 January, Trump called for a 10% limit, reviving an idea he put forward during his 2024 presidential election campaign.
“Effective January 20, 2026, I, as President of the United States, am calling for a one year cap on Credit Card Interest Rates of 10%,” he wrote. “Please be informed that we will no longer let the American Public be ‘ripped off’ by Credit Card Companies.”
The social media post spooked investors in credit card firms American Express, Visa and Mastercard, while UK bank Barclays also saw its shares dip.
The era of ‘hyperglobalisation’ that defined world trade in the early 21st century has given way to a more fractured trade policy landscape (Gros 2017), under the strain of actions enacted by the first and second Trump administrations. Starting in 2018, the first wave of US tariffs was a shock especially to US–China economic relations, with China being singled out for Section 301 actions on the grounds of redressing unfair trade practices. By late 2019, average US tariffs on goods from China had been raised by around 20 percentage points (Bown 2021, Chor and Li 2024). An even more sweeping wave of tariff announcements followed, shortly after Trump’s return as president in 2025. The “Liberation Day” tariffs, if fully implemented, threaten to increase tariffs for all US trade partners, with additional rates ranging from a minimum of ten to a maximum of more than 50 percentage points.
Global firms and country governments now face the unenviable task of navigating a trade policy environment where US tariff rates are increasingly fluid and mercurial. The stakes are considerable: sourcing decisions made by firms and policy responses enacted by countries today are poised to reshape global supply chain activity for years to come.
Already, the first wave of US tariffs in 2018-2019 set in motion a shift in US sourcing away from China, which we described as a ‘Great Reallocation’ with the potential to significantly disrupt established supply chain relationships around the world (Alfaro and Chor 2023a, 2023b). While we had (somewhat tentatively) characterised these trends as ‘looming’ in our previous work, it is fair to say that this adjective can now be dropped without qualifications. In Alfaro and Chor (2025), we provide an update on the scope, pace, and composition of this ‘Great Reallocation’, using US import data for detailed product codes from 2013-2025. With the benefit of a longer time span of data, what can we say about the short- to medium-run effects of the 2018-2019 wave of Trump tariffs? And what do the most recent months of data tell us about the early impact of the “Liberation Day” announcements?
Our work adds to a growing body of empirical evidence on the far-reaching effects of the US–China tariff war. These have documented the effects on bilateral trade flows (Fajgelbaum et al. 2020), as well as on trade diversion involving third countries (Fajgelbaum et al. 2024, Iyoha et al. 2024). More broadly, this work speaks to a debate over whether what we are witnessing is a fragmentation of world trade along geopolitical lines (Aiyar et al. 2023, Gopinath et al. 2025).
Six facts on the Great Reallocation
We summarise our findings in a series of six stylised facts.
1. Decoupling from China, but not (yet) from the world at large
Between 2017 and 2024, aggregate US merchandise imports grew at an annual average rate of 5.7% (in nominal terms), faster than the annual rate of 0.8% in the preceding four years. This was happening even amid an ongoing decline in US direct imports from China (see left panel in Figure 1). In China’s stead, there has been strong growth in US imports from other trade partners, notably Vietnam, Mexico, and in the last few years, Taiwan. What the data show is thus a selective decoupling from China, rather than a full US retreat from globalisation at least for now (Antràs 2021, Baldwin 2022, Goldberg and Reed 2023, Conteduca et al. 2025).
Figure 1 Changes in US imports by major trade partners, 2015-2025H1
Notes: Based on US Census Bureau data, half-yearly averages of nominal imports; index values with 2017H1 as the base (= 100). The selected trade partners illustrated in the right panel are the top seven as ranked by the increase seen between 2017-2024 in US import market share.
2. Limited diversification in the pool of US import partners
The US has diversified its sourcing away from China, with Mexico and Canada overtaking China in their share of direct US imports. Even so, the reshuffling of these US import market shares has occurred almost entirely among the US’ 20 largest import partners, as the combined share held by countries outside this ‘top 20’ has barely changed since 2017. During this time, only one economy (the Netherlands) broke into the ‘top 20’ list. The reallocation of US sourcing shares has thus been happening largely among its existing trade partners and established industrial clusters.
3. Continued slide in China’s direct import share
China’s share of US imports peaked at roughly 21% in 2017. By the end of 2024, this had fallen to around 13%, extending the slide in China’s direct import market share reported in earlier work (Alfaro and Chor 2023a, Freund et al. 2024, Grossman et al. 2024, Garred and Yuan 2025). On the other hand, Vietnam, Mexico, and Taiwan each gained around two percentage points of US import market share by 2024 (see Figure 2). Although some of this increase in partner country imports could reflect rerouting of goods that originate from China, the available estimates of this phenomenon indicate that pure rerouting is unlikely to account for the bulk of the increase in Vietnam’s and Mexico’s exports to the US (Iyoha et al. 2024, Freund 2025).
Figure 2 Changes in US import shares across trade partners since 2017
Notes: Based on US Census Bureau data; the first seven trade partners illustrated from the left are the top seven as ranked by the increase seen between 2017-2024 in US import market share.
In our paper (Alfaro and Chor 2025), we also estimate Jorda (2005) local linear projections to trace out the year-by-year, HS six-digit product-level impulse responses of US imports following the 2018-2019 tariffs on China, separately for several key trade partners. Figure 3 presents the associated findings for US imports from China. This confirms the large and persistent decline in product-level import shares with the onset of the tariffs (top-left panel). This drop reflects both the cessation of imports of some products (the extensive margin), as well as reduced volumes among continuing flows (the intensive margin). Using data on duty-inclusive unit values (bottom-right panel), we also estimated tariff pass-through to be substantial (around 0.7), albeit slightly smaller than the full pass-through obtained from monthly data in earlier studies (Amiti et al. 2019, Fajgelbaum et al. 2020, Cavallo et al. 2021).
Figure 3 Local projection responses: China’s imports in the US, 2013-2024
Notes: Based on Jorda (2005) local projections, with the additional US product-level tariff on imports from China, i.e. Δln(1 + τ), as the tariff shock variable. The sample here comprises all available HS six-digit products. We average the data over 2018-2019 and set this as the base year (h = 0); the regressions are otherwise run on annual data. For pre-periods (h < 0), the outcome variable is defined as yp,h − yp,h−1. Standard errors are clustered at the HS four-digit level, with 90% confidence intervals illustrated.
4. Most of the adjustment has occurred on the product-level intensive margin
Using a product-level accounting decomposition, we find that most of the reallocation – both China’s drop and other countries’ gains in US import market share – stem from changes in volumes for products that were already being traded. There were some notable exceptions where the extensive margin – (net) entry of exports in HS6 codes – did play a more prominent role, namely, Vietnam and India after 2021.
5. Reallocation eventually affects more ‘sticky’ supply chain relationships
The decrease in China’s share in US imports was concentrated in 2017-2020 among products that could plausibly be sourced from alternative locations at short notice, such as various computer units and associated parts, as well as apparel items. By 2021–2024, however, the shift away from China spread to products that are contract-intensive (i.e. whose production relies more on specialised inputs, as measured by Nunn 2007) and that are relationship-sticky (i.e. that tend to feature more long-lived buyer-supplier ties, as measured by Martin et al. 2023). For such products, it appears that once it was clear that the Trump tariffs would persist under the Biden administration, firms stopped taking a ‘wait and see’ approach and instead started incurring the sunk costs of reorganising and relocating their cross-border supply chains.
6. Reallocation has accelerated since Liberation Day
The data through August 2025 already reveal a striking acceleration in the pace of reallocation in US import patterns following the 2 April 2025 tariff announcements. China’s share of US imports fell sharply by about four percentage points between March and August 2025, so much so that by late 2025, this share stood at approximately 9% (see Figure 2), a level last seen when China joined the WTO in 2001. US trade policies in the past eight years have thus effectively unwound the preceding two decades of deepening US-China trade ties (see Figure 4).
Figure 4 China’s import share in the US since 1991
Notes: Based on US Census Bureau import data. The bars illustrate China’s share in US imports (left vertical axis), while the line illustrates the rise in US total real imports over time (right vertical axis). The 2025 data point is based on partial year information.
Moreover, Figure 5 reveals how the Liberation Day tariffs have tilted reallocation in favour of US trade partners facing lower announced tariff rates, notably Mexico and Canada. Economies faced with higher announced tariffs, particularly in East Asia, instead lost further import market share. This speed of adjustment suggests that firms had already pre-emptively lined up alternative sourcing arrangements as a contingency plan, which they were then able to activate quickly once the full extent of the protectionist goals of the second Trump administration was confirmed on Liberation Day.
Figure 5 Changes in US imports by major trade partners, March to August 2025
Notes: Based on US Census Bureau import data and the Liberation Day tariff rates announced on 2 April 2025. The vertical axis plots the change in each trade partner’s share of US imports between March and August 2025, demeaned by the average import share change over the same months during 2022–2024. Marker sizes are proportional to each partner’s total import volume in 2024. The best-fit line is from a linear regression, weighted by initial US imports from the trade partner in 2024.
Concluding thoughts
Given how swiftly the ‘Great Reallocation’ in US supply chains has unfolded, a natural question to ask is whether US–China trade, and the international trading system more broadly, can bounce back. On this front, much depends on how permanent firms and supply chain managers envision the current slate of US trade policy measures to be. Specifically, which tariffs are likely to remain in force – perhaps even beyond the second Trump administration – and which could be rolled back? A plausible hypothesis here is that protectionist measures will persist for the foreseeable future in US trade with China and in industries deemed vital for American jobs. This is because concerns about trade with China and about competition for workers have been repeatedly cited by the US public as reasons for favouring limits on imports (Alfaro et al. 2023c).
References
Aiyar, S, C H Ebeke, R Garcia-Saltos, T Gudmundsson, A Ilyina, A Kangur, T Kunaratskul, S L Rodriguez, M Ruta, T Schulze, G Soderberg and J P Trevino (2023), Geoeconomic Fragmentation and the Future of Multilateralism, Technical Report, International Monetary Fund.
Alfaro, L and Davin Chor (2023a), “Global Production Networks: The Looming ‘Great Reallocation’”, in Structural Shifts in the Global Economy, Jackson Hole Economic Policy Symposium, pp. 213–278.
Alfaro, L and D Chor (2023b), “A Perspective on the Great Reallocation of Global Supply Chains”, VoxEU.org, 28 September.
Alfaro, L, M X Chen and D Chor (2023c), “Can Evidence-Based Information Shift Preferences Towards Trade Policy?”, NBER Working Paper 31240.
Alfaro, L and D Chor (2025), “An Anatomy of the Great Reallocation in US Supply Chain Trade”, NBER Working Paper 34490.
Amiti, M, S J Redding and D E Weinstein (2019), “The Impact of the 2018 Tariffs on Prices and Welfare”, Journal of Economic Perspectives 33(4): 187–210.
Antràs, P (2021), “De-Globalisation? Global Value Chains in the Post-COVID-19 Age”, ECB Forum: Central Banks in a Shifting World Conference Proceedings.
Baldwin, R (2022), “The Peak Globalisation Myth”, VoxEU.org, 31 August.
Bown, C P (2021), “The US–China Trade War and Phase One Agreement”, Journal of Policy Modeling 43(4): 805–843.
Cavallo, A, G Gopinath, B Neiman and J Tang (2021), “Tariff Pass-Through at the Border and at the Store: Evidence from US Trade Policy”, American Economic Review: Insights 3(1): 19–34.
Chor, D and B Li (2024), “Illuminating the Effects of the US-China Tariff War on China’s Economy”, Journal of International Economics 150, 103926.
Conteduca, F P, S Giglioli, C Giordano, M Mancini and L Panon (2025), “Trade Fragmentation Unveiled: Five Facts on the Reconfiguration of Global, US and EU Trade”, Journal of Industrial and Business Economics 52: 535–557.
Fajgelbaum, P D, P K Goldberg, P J Kennedy and A K Khandelwal (2020), “The Return to Protectionism”, Quarterly Journal of Economics 135(1): 1–55.
Fajgelbaum, P, P Goldberg, P Kennedy, A Khandelwal and D Taglioni (2024), “The US-China Trade War and Global Reallocations”, American Economic Review: Insights 6(2): 295–312.
Freund, C, A Mattoo, A Mulabdic and M Ruta (2024), “Is US Trade Policy Reshaping Global Supply Chains?”, Journal of International Economics 152, 104011.
Freund, C (2025), “The China Wash: Tracking Products To Identify Tariff Evasion Through Transshipment”, mimeo.
Garred, J and S Yuan (2025), “Relocation from China (with Chinese Characteristics)”, Journal of Development Economics 176, 103510.
Goldberg, P K and T Reed (2023), “Is the Global Economy Deglobalizing? If So, Why? And What is Next?”, Brookings Papers on Economic Activity, Spring, 347–396.
Gopinath, G, P-O Gourinchas, A F Presbitero and P Topalova (2025), “Changing Global Linkages: A New Cold War?”, Journal of International Economics 153, 104042.
Gros, D (2017), “Globalisation: The hype, the reality, and the causes of the recent slowdown in global trade”, VoxEU.org, 30 June.
Grossman, G M, E Helpman and S J Redding (2024), “When Tariffs Disrupt Global Supply Chains”, American Economic Review 114(4): 988–1029.
Iyoha, E, E Malesky, J Wen, S-J Wu and B Feng (2024), “Exports in Disguise? Trade Rerouting during the US-China Trade War”, mimeo.
Jorda, O (2005), “Estimation and Inference of Impulse Responses by Local Projections”, American Economic Review 95(1): 161–182.
Martin, J, I Mejean and M Parenti (2023), “Relationship Stickiness, International Trade, and Economic Uncertainty”, Review of Economics and Statistics, forthcoming.
Nunn, N (2007), “Relationship-Specificity, Incomplete Contracts, and the Pattern of Trade”, Quarterly Journal of Economics 122(2): 569–600.
Lundin Mining Announces 2025 Production Results and 2026 Guidance
January 21, 2026
VANCOUVER, BC, Jan. 21, 2026 /CNW/ – (TSX: LUN) (Nasdaq Stockholm: LUMI) Lundin Mining Corporation (“Lundin Mining” or the “Company”) is pleased to announce production results for the year ended December 31, 2025. The Company achieved guidance on all metals for the year on a consolidated basis. This represents the third consecutive year that Lundin Mining has achieved consolidated production guidance, reinforcing the Company’s commitment to operational and financial consistency.
In addition, the Company is pleased to release production guidance for the three-year period from 2026 through 2028, as well as cash cost, capital and exploration expenditure guidance for 2026. Unless otherwise stated, all numbers are in US dollars.
Jack Lundin, President and CEO, commented “I am proud to report that Lundin Mining has delivered production in line with guidance for the third consecutive year, reflecting the accuracy of our planning cycle and our disciplined focus on operational consistency. In the fourth quarter, we produced over 87,000 tonnes of copper and over 34,000 ounces of gold, capping a strong year across our three Latin American operations. Notably, Caserones produced over 15,000 tonnes of copper in December 2025, marking the best monthly performance since Lundin Mining took ownership of the operation.
“Looking ahead, our three-year production and one year cost outlook remains firmly on track with previously disclosed forecasts. Mine sequencing optimizations are expected to increase copper production by 20,000 tonnes in 2027, while the midpoint of 2026 has been adjusted by 5,000 tonnes, resulting in a net increase of approximately 15,000 tonnes over the two-year period. Operationally, we remain focused on delivering consistent performance through disciplined planning, which we believe will translate into stronger financial returns in a robust commodity price environment.
“Brownfield growth initiatives across our operations continue to progress, and the advancement of the Vicuña project remains on track. With the RIGI PEELP application submitted in December, the integrated technical report results forthcoming this quarter, and continued progress on upsizing our credit facility, 2026 is shaping up to be a pivotal year as Vicuña moves into an exciting new phase of development.”
Highlights for 2025 Production and 2026 – 2028 Guidance
2025 Production Results
The Company beat original1 copper production guidance and was within the revised copper, gold and nickel production guidance for the year.
2025 full year production results (100% basis):
Copper production of 331,232 tonnes (t);
Gold production of 141,859 ounces (oz);
Nickel production at Eagle of 9,907 t;
During Q4 2025, Caserones achieved its highest quarterly consolidated copper production since the Company has owned the mine, producing 39,612 tonnes, driven by higher copper head grades and cathode production.
Guidance
Updated three-year production guidance remains in line with previous 2026 and 2027 guidance2. The company forecasts 2026 consolidated copper production of 310,000 to 335,000 tonnes and gold production of 134,000 to 149,000 ounces at a cash cost guidance3 of $1.90/lb to $2.10/lb.
Forecasted consolidated copper production of 315,000 to 340,000 tonnes in 2027 and 290,000 to 315,000 tonnes in 2028.
Sustaining capital expenditures4 of $550 million and expansionary capital expenditures4 of $445 million in 2026.
Exploration expenditures are forecast to be $53 million primarily for in-mine and near-mine targets in 2026.
____________________________________________
1 Guidance as announced by news release “Lundin Mining Announces Record Production Results for 2024 & Provides 2025 Guidance” dated January 16, 2025.
Summary of 2025 Production
The Company exceeded its original2 full year consolidated copper production guidance and was within the increased5 full year production guidance for 2025. Strong operational performance, particularly at Caserones drove production growth for the year.
Candelaria produced 145,471 tonnes of copper for the full year, benefiting from higher mill throughput due to softer ore.
Since Lundin Mining has owned the mine, Caserones achieved its highest quarterly copper production of 39,612 tonnes in the fourth quarter of 2025, supported by higher head grades and strong cathode production. For the full year, Caserones produced 132,881 tonnes surpassing original guidance.
Full year copper production at Chapada was 43,974 tonnes, which benefited from consistent grades and strong throughput during the year.
Q4 2025
Production
FY 2025
Production
2025 Original
Guidance2
2025 Revised
Guidance5
Copper (t)
Candelaria (100% basis)
34,272
145,471
140,000
–
150,000
143,000
–
149,000
Caserones (100% basis)
39,612
132,881
115,000
–
125,000
127,000
–
133,000
Chapada
11,191
43,974
40,000
–
45,000
40,000
–
45,000
Eagle
1,957
8,906
8,000
–
10,000
9,000
–
10,000
Total Copper
87,032
331,232
303,000
–
330,000
319,000
–
337,000
Gold (oz)
Candelaria (100% basis)6
19,055
80,528
78,000
–
88,000
78,000
–
84,000
Chapada
15,074
61,331
57,000
–
62,000
57,000
–
62,000
Total Gold
34,129
141,859
135,000
–
150,000
135,000
–
146,000
Nickel (t)
Eagle
2,174
9,907
8,000
–
11,000
9,000
–
11,000
Total Nickel
2,174
9,907
8,000
–
11,000
9,000
–
11,000
_________________________________________
2
Guidance as announced by news release “Lundin Mining Announces Record Production Results for 2024 & Provides 2025 Guidance” dated January 16, 2025.
3
This is a non-GAAP measure. For equivalent historical non-GAAP financial measure comparatives see the Historical Non-GAAP Measure Comparatives section of this press release. Please also see the Management’s Discussion and Analysis for the year ended December 31, 2024 and nine months ended September 30, 2025.
4
Sustaining capital expenditure is a supplementary financial measure and expansionary capital expenditure is a non-GAAP measure – see the Company’s Management’s Discussion and Analysis for the three and nine months ended September 30, 2025 and the Historical Non-GAAP Measure Comparatives at the end of this news release.
5
Guidance as most recently disclosed in the Company’s Management Discussion and Analysis for the three and nine months ended September 30, 2025.
Three-Year Production Guidance 2026 – 2028
Copper production is forecast to remain stable at approximately 310,000 to 335,000 tonnes annually in 2026, consistent with 2025 production, after accounting for the sale of the Eagle Mine to Talon Metals Corp. (see press release dated January 9, 2026 entitled “Lundin Mining Completes the Sale of the Eagle Mine and Humboldt Mill to Talon Metals”).
Mine plan updates at the Company’s Candelaria operation result in modifications to the previous 2026 guidance. At Candelaria, 2026 copper and gold production guidance reflects lower underground mining rates in the first half of the year as the Company insources the underground mining contract.
The outlook for consolidated copper production in 2027 increases compared to the previous 2027 guidance from higher forecast production at Candelaria, Caserones and Chapada resulting in an increase to the cumulative consolidated copper production over 2026 and 2027 by 15,000 tonnes when compared to the previous guidance during the same period and based on the midpoint of the guidance ranges, after accounting for the sale of the Eagle Mine.
Gold guidance for 2026 is forecast to be 134,000 to 149,000 ounces which reflects lower underground mining rates at Candelaria as mentioned previously. In 2027, gold production is expected to increase by approximately 10,000 ounces year-on-year, driven by higher production at Chapada, resulting in the cumulative consolidated gold production over 2026 and 2027 to remain essentially flat when compared to the previous guidance and based on the midpoint of the guidance ranges.
Production Guidance
2026
2027
2028
Copper (t)
Candelaria (100% basis)
135,000
–
145,000
157,000
–
167,000
135,000
–
145,000
Caserones (100% basis)
130,000
–
140,000
115,000
–
125,000
115,000
–
125,000
Chapada
45,000
–
50,000
43,000
–
48,000
40,000
–
45,000
Total Copper
310,000
–
335,000
315,000
–
340,000
290,000
–
315,000
Gold (oz)
Candelaria (100% basis) 6
77,000
–
87,000
85,000
–
95,000
75,000
–
85,000
Chapada
57,000
–
62,000
58,000
–
63,000
57,000
–
62,000
Total Gold
134,000
–
149,000
143,000
–
158,000
132,000
–
147,000
Candelaria: Annual fluctuations in copper and gold production forecasts for the next three years are primarily due to variations in the grade profile of Candelaria.
Revisions to Candelaria’s 2026 copper and gold production guidance incorporates lower underground mining rates in the first half of the year as the Company insources the underground mining contract. The production profile is forecast to be modestly weighted towards the second half of the year due to higher expected grades from Phase 12.
Higher copper production of approximately 7,000 tonnes in 2027 when compared to the previous 2027 guidance, results from revised ore sequencing and additional higher-grade ore from Phase 11.
Over the three-year guidance period, total mill throughput averages approximately 29 million tonnes per annum (“Mtpa”), slightly higher than previous years due to an improved ore hardness model that accounts for softer ore from Phase 11.
Caserones: Copper production in 2026 is modestly weighted toward the first half of the year due to the planned grade profile. As part of the mine sequencing, 2027 and 2028 production profiles reflect anticipated lower copper head-grades following the completion of Phase 6 in early 2027.
________________________________________
6
68% of Candelaria’s total gold and silver production is subject to a streaming agreement.
Caserones copper guidance in 2027 increases by ~10,000 tonnes to 115,000 to 125,000 tonnes when compared to the previous 2027 guidance, as a result of higher cathode production and increased mill throughput.
Over the guidance period, mill throughput is expected to rise to approximately 34–36 Mtpa, supported by ongoing Full Potential initiatives. Cathode production is expected to improve from optimization efforts implemented in 2025 and is forecast to be 26,000–28,000 tonnes per annum (“tpa”) over the period, an improvement of 6,000–8,000 tpa from prior levels.
Chapada: Copper production guidance increases by ~5,000 tonnes in 2026 to 45,000 to 50,000 tonnes and gold guidance is increased by approximately 10,000 ounces in 2027 as compared to the previous 2026 and 2027 guidance, respectively. Annual variations largely reflect mine sequencing and forecasted copper and gold grade profiles. An updated mine plan has reduced the proportion of stockpile material in mill feed from ~25% to ~10%, improving copper and gold recoveries over the three-year period.
2026 Cash Cost7 Guidance
Consolidated cash cost in 2026 is forecast to be within $1.90 to $2.10 per pound of copper, net of by-product credits. As part of the company’s Full Potential programs, the focus will continue to be on cost reductions and process improvements. Total cash cost guidance for 2026 is in line with 2025 guidance.
2026 cash cost guidance reflects higher by-product credits primarily from an increase in gold and molybdenum commodity price assumptions offset by a stronger Chilean Peso.
Cash Cost
20268
Copper
Candelaria9
$2.05/lb
–
$2.25/lb
Caserones
$2.05lb
–
$2.25/lb
Chapada10
$1.00/lb
–
$1.20/lb
Consolidated C1 Cash Cost
$1.90/lb
–
$2.10/lb
Candelaria: Cash cost guidance is forecast to be $2.05/lb – $2.25/lb of copper, after by-product credits. Lower production volumes and foreign exchange rates led to a slight increase in cash cost compared to 2025 guidance.
During the fourth quarter 2025, Lundin Mining successfully negotiated a new three-year labour agreement with the unions at Candelaria, a negotiation which originally was scheduled for 2026.
Caserones: Cash cost is expected to decline in 2026 (as compared to the revised 2025 guidance) and are forecast to be $2.05/lb – $2.25/lb of copper, after by-product credits, reflecting higher low-cost cathode production.
Chapada: Cash cost is forecast to be $1.00/lb – $1.20/lb of copper in 2026, after by-product credits, a slight increase from the prior year. This is the result of higher mining rates in 2026, partially offset by higher by-product credits.
_______________________________________
7
This is a non-GAAP measure. For equivalent historical non-GAAP financial measure comparatives see the Historical Non-GAAP Measure Comparatives section of this press release. Please also see the Management’s Discussion and Analysis for the year ended December 31, 2024 and nine months ended September 30, 2025.
8
2026 cash cost is based on various assumptions and estimates, including, but not limited to: production volumes, commodity prices (2026 – Mo: $20.00/lb, Au: $4,000/oz: Ag: $80.00/oz) foreign currency exchange rates (2025 – CLP/USD:900, USD/BRL:5.50) and operating costs.
9
68% of Candelaria’s total gold and silver production are subject to a streaming agreement and as such cash costs are calculated based on receipt of $437/oz and $4.36/oz, respectively, on gold and silver sales in the year.
10
Chapada’s cash cost is calculated on a by-product basis and does not include the effects of its copper stream agreements. Effects of the copper stream agreements are reflected in copper revenue and will impact realized price per pound.
2026 Capital Expenditure Guidance
Total sustaining capital expenditures11 are forecast to be $550 million, consistent with prior years’ guidance. Candelaria and Caserones account for approximately 80% of the sustaining capital budget, with the majority of expenditures directed to open pit waste stripping, underground mine development, tailings storage facility (“TSF”) and mining equipment. Expansionary capital expenditures11 are forecast to be $445 million and includes 50% of the expenditure related to the 50/50 joint arrangement between the Company and BHP for the Vicuña Project.
Capital Expenditures ($ millions)
202611,12
Sustaining Capital
Candelaria (100% basis)
$215
Caserones (100% basis)
$235
Chapada
$100
Total Sustaining Capital
$550
Expansionary Capital
$50
Vicuña (50% basis)
$395
Total Capital Expenditures
$995
Candelaria ($215 million): Capitalized waste stripping and underground mine development is forecast to be $60 million and TSF expenditures are forecast to be $40 million. Capital expenditure for mobile and mine equipment is forecast to be $20 million, and the remaining sustaining capital requirements are estimated at $95 million.
Expansionary capital is estimated to be $35 million, which includes approximately $25 million for pre-production stripping related to Phase 13.
Caserones ($235 million): Includes approximately $70 million for capitalized waste stripping, $50 million for TSF and water management projects, and $50 million for mine and mobile equipment. Sustaining capital requirements beyond these items are estimated at approximately $65 million.
Chapada ($100 million): Includes approximately $30 million for capitalized waste, $38 million for TSF and water management projects, and $16 million for mine and mobile equipment.
Vicuña ($395 million): Capital expenditures for the Vicuña project are forecast to total $395 million on a 50% basis for 2026. The 2026 workplan includes activities such as ongoing resource and infill drilling, equipment purchases for Josemaria to support earthworks, procurement of long lead equipment and the advancement of the Northern Access Road. Indirect activities, primarily related to Josemaria, include detailed engineering, construction management and preconstruction work associated with camp expansion, construction facilities, permitting and other owner’s costs to support continued project de-risking. Additional engineering studies will advance Filo leaching, Filo sulfides, desalinated water infrastructure and concentrate transportation to further refine project definition and support permitting activities.
A 50,000 metre (m) drill program is planned at Filo del Sol, the program will focus on resource conversion and growth, as well as drilling to support upcoming technical studies.
___________________________________________
11
Expansionary capital expenditure is a non-GAAP measure and sustaining capital expenditure is a supplementary financial measure. For historical comparatives see the Historical Non-GAAP Measure Comparatives section of this press release. Please also see the Management’s Discussion and Analysis for the year ended December 31, 2024 and nine months ended September 30, 2025 for discussion of non-GAAP measures.
12
Capital expenditures are based on various assumptions and estimates, including, but not limited to foreign currency exchange rates (2026 – CLP/USD:900, USD/BRL:5.50).
Vicuña is targeting completion of an integrated technical report in Q1 2026 which will outline the district’s development plan and include updated mineral resource estimates for both Filo del Sol and Josemaria.
2026 Exploration Investment Guidance
Exploration expenditures are planned to be $53 million in 2026, primarily for resource expansion at in-mine and near-mine targets at our operations. The largest portion of the planned expenditure will be at Caserones where drilling (39,800 m) and geophysical programs are planned. The drill program at Caserones will primarily focus on defining the size of the Angelica deposit, both in terms of leachable copper resources and the underlying copper/molybdenum sulphide mineralization, with a planned 26,900 m of drilling. Additional drilling at Caserones will be directed towards growing the size of the Caserones deposit laterally and testing at least two new district exploration targets (Centauro and Cordillera). Significant drilling programs are also planned at Candelaria (16,000 m), and Chapada (13,700 m) with the goal of growing resources. At Candelaria drilling is designed to continue expanding the underground resources, while also growing the shallow La Española deposit and neighboring La Portuguesa target area. At Chapada additional drilling at Saúva will continue to further define higher grade resources that will be incorporated into an updated resource estimate later this year.
About Lundin Mining
Lundin Mining is a Canadian mining company headquartered in Vancouver, Canada with three operating mines in Brazil and Chile. We produce commodities that support modern infrastructure and electrification. Our strategic vision is to become a top ten global copper producer. To get there, we are executing a clear growth strategy, which includes advancing one of the world’s largest copper, gold, and silver projects in the Vicuña District on the border of Argentina and Chile, where we hold a 50% interest. Lundin Mining has a proven track record of value creation through resource growth, operational excellence, and responsible development. The Company’s shares trade on the Toronto Stock Exchange (LUN) and Nasdaq Stockholm (LUMI). Learn more at www.lundinmining.com.
The information in this release is subject to the disclosure requirements of Lundin Mining under the EU Market Abuse Regulation. The information was submitted for publication, through the agency of the contact persons set out below on January 21, 2026 at 18:00 Eastern Time.
Other Information
The scientific and technical information in this press release has been prepared in accordance with the disclosure standards of National Instrument 43-101 – Standards of Disclosure for Mineral Projects (“NI 43-101”) and has been reviewed and approved by Eduardo A. Cortes, Vice President, Technical Services, a “Qualified Person” under NI 43-101. Mr. Cortes has verified the data disclosed in this release and no limitations were imposed on his verification process.
Historical Non-GAAP Measure Comparatives
Cash cost and sustaining and expansionary capital expenditures are non-GAAP financial measures and are not standardized financial measures under generally accepted accounting principles under IFRS and, therefore, amounts presented may not be comparable to similar data presented by other mining companies. These amounts are intended to provide additional information and should not be considered in isolation or as a substitute for measures of performance prepared in accordance with IFRS. Please refer to the section titled “Non-GAAP and Other Performance Measures” in Lundin Mining’s Management’s Discussion and Analysis for the year ended December 31, 2024 and for the three and nine months ended September 30, 2025, which are incorporated by reference herein and which are available on SEDAR+ at www.sedarplus.ca.
Cash Cost – Year Ended December 31, 2024
Operations
Candelaria
Caserones
Chapada
Eagle
Total – Continuing Operations
Neves- Corvo
Zinkgruvan
Total – Discontinued Operations
($ millions, unless otherwise noted)
(Cu)
(Cu)
(Cu)
(Ni)
(Cu)
(Zn)
Sales volumes (Contained metal):
Tonnes
158,017
113,867
39,615
5,662
26,721
68,086
Pounds (000s)
348,367
251,033
87,336
12,483
58,910
150,104
Production costs
1,898.6
445.2
Less: Royalties and other
(84.5)
(4.8)
1,814.1
440.4
Deduct: By- product credits2
(504.4)
(305.5)
Add: Treatment and refining
113.6
55.4
Cash cost
603.5
629.6
137.7
52.4
1,423.3
129.1
61.2
190.4
Cash cost per pound ($/lb)
1.73
2.51
1.58
4.20
2.19
0.41
Capital Expenditures – Year Ended December 31, 2024
($ millions)
Sustaining
Expansionary
Capitalized Interest
Total
Candelaria
275.7
—
—
275.7
Caserones
144.0
—
—
144.0
Chapada
107.8
—
—
107.8
Eagle
21.2
—
—
21.2
Josemaria
—
243.6
14.6
258.2
Other
0.4
—
—
0.4
Continuing Operations
549.1
243.6
14.6
807.3
Neves-Corvo
89.3
—
—
89.3
Zinkgruvan
65.7
—
—
65.7
Total
704.1
243.6
14.6
962.3
Sustaining capital expenditures is a supplementary financial measure and expansionary capital expenditures is a non-GAAP measure. See the Management’s Discussion and Analysis for the year ended December 31, 2024, for discussion of non-GAAP measures heading “Non-GAAP and Other Performance Measures” which is incorporated by reference herein.
Cash Cost – Nine Months Ended September 30, 2025
Continuing Operations
Candelaria
Caserones
Chapada
Consolidated
Eagle
Total – Continuing Operations
($ millions, unless otherwise noted)
(Cu)
(Cu)
(Cu)
(Cu)
(Ni)
Sales volumes (Contained metal):
Tonnes
107,618
93,153
32,627
233,398
5,895
Pounds (000s)
237,257
205,367
71,930
514,554
12,996
Production costs
557.3
607.2
234.9
1,399.4
112.7
1,514.0
Less: Royalties and other
(9.5)
(32.0)
(17.4)
(58.9)
(12.7)
(73.4)
547.8
575.2
217.5
1,340.5
100.0
1,440.6
Deduct: By-product credits2
(136.3)
(108.0)
(162.4)
(406.7)
(66.0)
(472.7)
Add: Treatment and refining
17.3
6.4
4.6
28.3
—
28.3
Cash cost
428.8
473.6
59.7
962.1
34.0
996.2
Cash cost per pound ($/lb)
1.81
2.31
0.83
1.87
2.62
Discontinued Operations1
Neves-Corvo
Zinkgruvan
Total – Discontinued Operations
($ millions, unless otherwise noted)
(Cu)
(Zn)
Sales volumes (Contained metal):
Tonnes
6,745
20,698
Pounds (000s)
14,870
45,631
Production costs
90.2
36.9
127.1
Less: Royalties and other
(1.3)
—
(1.3)
88.9
36.9
125.8
Deduct: By-product credits2
(67.0)
(23.3)
(90.3)
Add: Treatment and refining
5.4
7.2
12.6
Cash cost
27.3
20.8
48.1
Cash cost per pound ($/lb)
1.84
0.46
1 Discontinued operations results are to April 16, 2025
2 By-product credits are presented net of the associated treatment and refining charges.
Capital Expenditures – Nine Months Ended September 30, 2025
($ millions)
Total
Candelaria
21.7
Vicuña
126.0
Expansionary capital investment from continuing operations
147.7
Candelaria
144.9
Caserones
99.5
Chapada
75.7
Eagle
17.4
Other
0.1
Sustaining capital investment from continuing operations
337.6
Total capital expenditures from continuing operations
485.3
Sustaining capital expenditures is a supplementary financial measure and expansionary capital expenditures is a non-GAAP measure. See the Management’s Discussion and Analysis for the three and nine months ended September 30, 2025, for discussion of non-GAAP measures heading “Non-GAAP and Other Performance Measures” which is incorporated by reference herein.
Cautionary Statement on Forward-Looking Information
Certain of the statements made and information contained herein are “forward-looking information” within the meaning of applicable Canadian securities laws. All statements other than statements of historical facts included in this document constitute forward-looking information, including but not limited to statements regarding the Company’s plans, prospects, business strategies and strategic vision and aspirations, and their achievement and timing; the Company’s guidance on the timing and amount of future production and its expectations regarding operational performance and the results of operations; the Company’s guidance and expectations regarding financial performance, including estimated capital expenditures and other costs, expenditures and financial metrics; the Company’s growth and optimization initiatives and expansionary projects, and the potential costs, outcomes, results and impacts thereof; approval of the RIGI application in Argentina for the Vicuña Project and the timing thereof; the operation of Vicuña with BHP; the realization of synergies and economies of scale in the Vicuña district; the timing and expectations for studies and updated estimates; permitting requirements and timelines; timing and possible outcome of pending litigation; the results and timing of any Preliminary Economic Assessment, Pre-Feasibility Study, Feasibility Study, or Mineral Resource and Mineral Reserve estimations, life of mine estimates, and mine and mine closure plans; the step down of the gold stream at Candelaria and the impacts and timing thereof; anticipated market prices of metals, currency exchange rates, and interest rates; the Company’s ability to comply with contractual and permitting or other regulatory requirements; anticipated exploration and development activities, including potential outcomes, results, impacts and timing thereof; the Company’s integration of acquisitions and expansions and any anticipated benefits thereof; and expectations for other economic, business, and/or competitive factors. Words such as “believe”, “expect”, “anticipate”, “contemplate”, “target”, “plan”, “goal”, “aim”, “intend”, “continue”, “budget”, “estimate”, “may”, “will”, “can”, “could”, “should”, “schedule” and similar expressions identify forward-looking information.
Forward-looking information is necessarily based upon various estimates and assumptions including, without limitation, the expectations and beliefs of management, including that the Company can access financing, appropriate equipment and sufficient labour; assumed and future price of copper, gold, zinc, nickel and other metals; anticipated costs; currency exchange rates and interest rates; ability to achieve goals and identify and realize opportunities; the prompt and effective integration of acquisitions and the realization of synergies and economies of scale in connection therewith; that the political, economic, permitting and legal environment in which the Company operates will continue to support the development and operation of mining projects; timing and receipt of governmental, regulatory and third party approvals, consents, licenses and permits and their renewals; positive relations with local groups; the accuracy of Mineral Resource and Mineral Reserve estimates and related information, analyses and interpretations; and such other assumptions as set out herein as well as those related to the factors set forth below. While these factors and assumptions are considered reasonable by Lundin Mining as at the date of this document in light of management’s experience and perception of current conditions and expected developments, such information is inherently subject to significant business, economic, political, regulatory and competitive uncertainties and contingencies. Known and unknown factors could cause actual results to differ materially from those projected in the forward-looking information and undue reliance should not be placed on such information. Such factors include, but are not limited to: dependence on international market prices and demand for the metals that the Company produces; operational and financial projections, including estimates of future expenditures and cash costs, and estimates of future production may not be reliable; volatility and fluctuations in metal and commodity demand and prices; political, economic, and regulatory uncertainty in operating jurisdictions, including but not limited to those related to permitting and approvals, nationalization or expropriation without fair compensation, environmental and tailings management, labour, trade relations, and transportation; risks relating to mine closure and reclamation obligations; health and safety hazards; inherent risks of mining (including but not limited to risks to the environment, industrial accidents catastrophic equipment failures, unusual or unexpected geological formations or unstable ground conditions, and natural phenomena such as earthquakes, flooding or unusually severe weather), not all of which related risk events are insurable; risks relating to geotechnical incidents; risks relating to tailings and waste management facilities; risks relating to the Company’s indebtedness; challenges and conflicts that may arise in partnerships and joint operations; risks relating to joint ventures, joint arrangements and operations; risks relating to development projects, including Filo del Sol and Josemaria; project financing risks, liquidity risks and limited financial resources; risks that revenue may be significantly impacted in the event of any production stoppages or reputational damage in the jurisdictions in which the Company operates or elsewhere; the impact of global financial conditions, market volatility and inflation; availability and pricing of key supplies and services; business interruptions caused by critical infrastructure failures; unavailable or inaccessible infrastructure, infrastructure failures, and risks related to ageing infrastructure; challenges of effective water management; exposure to greater foreign exchange and capital controls, as well as political, social and economic risks as a result of the Company’s operation in emerging markets; risks relating to community or stakeholder opposition to continued operation, further development, or new development of the Company’s projects and mines; information technology and cybersecurity risks, including any breach or failure information systems; risks relating to reliance on estimates of future production; risks relating to disputes, litigation and administrative proceedings (including tax disputes) which the Company may be subject to from time to time; risks relating to acquisitions or business arrangements; risks relating to competition in the industry; failure to comply with existing or new laws or changes in laws; enforcing legal rights in foreign jurisdictions; challenges or defects in title or termination of mining or exploitation concessions; the exclusive jurisdiction of foreign courts; the outbreak of infectious diseases or viruses; risks relating to taxation changes; receipt of and ability to maintain and comply with all permits that are required for operation; minor elements contained in concentrate products; changes in the relationship with its employees and contractors; risks associated with the estimation of Mineral Resources and Mineral Reserves and the geology, grade and continuity of mineral deposits including but not limited to models relating thereto; actual ore mined and/or metal recoveries varying from Mineral Resource and Mineral Reserve estimates, estimates of grade, tonnage, dilution, mine plans and metallurgical and other characteristics; ore processing efficiency; the Company’s Mineral Reserves and Mineral Resources which are estimates only; uncertainties relating to inferred Mineral Resources being converted into Measured or Indicated Mineral Resources; payment of dividends in the future; compliance with environmental, health and safety laws and regulations, including changes to such laws or regulations; interests of significant shareholders of the Company; asset values being subject to impairment charges; potential for conflicts of interest and public association with other Lundin Group companies or entities; activist shareholders and proxy solicitation firms; reputation risks related to negative publicity with respect to the Company or the mining industry in general; risks associated with climate change; the Company’s common shares being subject to dilution; ability to attract and retain highly skilled employees; reliance on key personnel and reporting and oversight systems; risks relating to the Company’s internal controls; counterparty and customer concentration risk; risks associated with the use of derivatives; exchange rate fluctuations; risks associated with acquisitions and related integration efforts, including the ability to achieve anticipated benefits, unanticipated difficulties or expenditures relating to integration and diversion of management time on integration; the terms of the contingent payments in respect of the completion of the sale of the Company’s European assets and expectations related thereto; and other risks and uncertainties, including but not limited to those described in the “Risks and Uncertainties” section of the Company’s MD&A for the three and nine months ended September 30, 2025, the “Risks and Uncertainties” section of the Company’s MD&A for the year ended December 31, 2024, and the “Risks and Uncertainties” section of the Company’s Annual Information Form for the year ended December 31, 2024, which are available on SEDAR+ at www.sedarplus.ca under the Company’s profile.
All of the forward-looking information in this document are qualified by these cautionary statements. Although the Company has attempted to identify important factors that could cause actual results to differ materially from those contained in forward-looking information, there may be other factors that cause results not to be as anticipated, estimated, forecasted or intended and readers are cautioned that the foregoing list is not exhaustive of all factors and assumptions which may have been used. Should one or more of these risks and uncertainties materialize, or should underlying assumptions prove incorrect, actual results may vary materially from those described in forward-looking information. Accordingly, there can be no assurance that forward-looking information will prove to be accurate and forward-looking information is not a guarantee of future performance. Readers are advised not to place undue reliance on forward-looking information. The forward-looking information contained herein speaks only as of the date of this document. The Company disclaims any intention or obligation to update or revise forward‐looking information or to explain any material difference between such and subsequent actual events, except as required by applicable law.
SOURCE Lundin Mining Corporation
For Further Information, Please Contact: Stephen Williams, Vice President, Investor Relations: +1 604 806 3074, Robert Eriksson, Investor Relations Sweden: +46 8 440 54 50
Extensive research has investigated the “exposure” of occupations to artificial intelligence (AI). While definitions vary, studies using exposure measures seek to estimate the extent to which AI systems can help complete the work tasks of different jobs. But these measures are not predictions of job displacement. Rather, they provide signals about where AI’s complex labor market effects are most likely to emerge first.
However, most exposure-focused analyses overlook a critical dimension: workers’ ability to adapt if job loss does occur.
Capacity to adapt after job loss is not evenly distributed across the workforce. Financial security, age, skills, union membership, and the state of local labor markets are just some of the many factors that can influence the real-life consequences of job loss. For that reason, forecasts of AI exposure, disruption, and potential work dislocation would benefit from incorporating such factors.
This is the purpose of new research explained here and in a new paper for the National Bureau of Economic Research (NBER) by research colleagues Sam Manning and Tomás Aguirre.
To address the heterogeneity of how AI-induced job loss may impact workers, the new analysis combines estimates of AI exposure with a novel measure of “adaptive capacity” that takes into account workers’ varied individual characteristics. Along these lines, the new work supplements occupation-level exposure analysis with relevant measures of workers’ savings, age, labor market density, and skill transferability in order to assess their varied capacity to weather job displacement and transition to new work. As such, the new approach provides a useful way of distinguishing between highly AI-exposed workers with relatively strong means to adjust to potential AI-driven job loss and those with more limited adaptive capacity.
As such, our analysis finds that overall, there is both broad resilience and concentrated pockets of potential vulnerability in the U.S. labor market when it comes to AI job displacement.
Of the 37.1 million U.S. workers in the top quartile of occupational AI exposure, 26.5 million also have above-median adaptive capacity, meaning they are among those best positioned to make a job transition if displacement occurs. However, the analysis also documents that some 6.1 million workers (4.2% of the workforce in the sample) will likely contend with both high AI exposure and low adaptive capacity. These workers tend to be concentrated in clerical and administrative roles, and about 86% are women (gender shares are calculated using Lightcast data). Geographically, these workers are concentrated in smaller metropolitan areas, particularly university towns and midsized markets in the Mountain West and Midwest.
In short, the new analysis asks: If AI does cause job displacement, who is best positioned to adapt, and who will struggle most? In asking those questions, this analysis intends to help policymakers focused on AI’s labor market impacts better target their attention and resources.
Background: Why AI exposure alone does not account for workers’ varied ability to adjust to a changing labor market
To identify which workers have the most and least capacity to weather AI-driven job displacement, the inquiry discussed here links analysis that forecasts AI’s labor market impacts with research examining how workers adjust to job displacement. As Manning and Aguirre write in their NBER report, “By bridging the two literatures, we move beyond identifying which jobs face potential AI exposure to understanding which workers might face the greatest or least adjustment costs if disruption leads to displacement.”
Research examining workers’ exposure to AI has frequently mapped descriptions of worker tasks to AI capabilities in order to estimate the potential for AI-driven disruption in different occupations. Studies by Brynjolfsson and others (2018); Webb (2020); Muro and others (2019); Felten and others (2023); Eloundou and others (2024); Kinder and others (2024); and Hampole and others (2025) all find that higher-income, white collar occupations requiring postsecondary education show the highest exposure to AI capabilities.
High exposure estimates for highly educated, high-income workers have led many to assume that these workers will bear the greatest burden of AI disruption. Yet such exposure measures fail to capture core non-technological factors that influence which workers would experience the most severe welfare costs if AI were to eventually be a cause of job displacement.
Along these lines, several factors are known to shape worker vulnerability to harms from job displacement (for more detail see the underlying NBER paper).
Liquid financial resources: Workers with greater savings weather economic storms more effectively. Chetty (2008) shows individuals with greater liquid savings are less financially distressed after job loss and take longer to find better-matching jobs, while low-wealth individuals are forced into lower-quality employment.
Age: Age significantly influences job displacement costs. Farber (2017) shows that workers aged 55 to 64 who experienced job loss during the Great Recession were about 16 percentage points less likely than those aged 35 to 44 to find employment afterward. Older workers are less likely to retrain, relocate, or switch occupations compared to their younger counterparts, and a range of studies has found that job loss for older workers leads to greater earnings losses and lower reemployment rates.
Geographic density: Where a worker lives can affect their displacement experience and recovery prospects. Bleakley and Lin (2012) show that workers in more densely populated areas face lower costs to make work transitions compared to those in low-density areas.
Skill transferability: Transferable skills—those that can be applied across many different jobs—offer more occupational mobility than highly specialized skills. Nawakitphaitoon and Ormiston (2016) show that skill transferability is associated with smaller earnings losses following displacement.
Other factors such as income, the routine-task intensity of one’s job, and union representation may also influence outcomes, but are excluded from the core capacity analysis due to data limitations or ambiguity about their unique contributions to adaptive capacity.
In sum, linking exposure measures with these indicators of adaptive capacity provides a more complete picture of who is likely to experience the greatest costs if AI exposure translates into job loss. More specifically, such an approach can suggest, on one hand, how the AI disruptions that may befall higher-income, white collar workers may be partly mitigated by those workers’ savings, skills, and networks; while on the other hand, downside risks for less adaptive workers may be harder to manage.
Approach: Measuring adaptive capacity alongside AI exposure
Expanding prior research on AI exposure by complementing Eloundou and others’ occupational exposure estimates with various measures of workers’ adaptive capacity allows for a sharper picture of risk and resilience in the labor market. The aim is to identify which occupations are likely to be impacted by large language models (LLMs) as well as which workers are the most and least able to weather a job transition if one becomes necessary.
To develop this picture, the NBER report combine six primary datasets to create a composite measure of adaptive capacity by occupation. The Survey of Income and Program Participation (SIPP) provides data on workers’ net liquid wealth; the American Community Survey (ACS) offers age distributions; the Occupational Employment and Wage Statistics (OEWS) program contributes wage and employment figures; Lightcast provides employment shares by county and metro area; Bureau of Labor Statistics employment projections provide estimated employment growth rates by occupation; and O*NET details the range of skills required in each occupation. Together, these sources cover the vast majority of the U.S. workforce. (See the following Appendix and the underlying paper here.)
From this combined data, the NBER report calculates an occupation-level adaptive capacity index based on four standardized components (net liquid wealth, growth-weighted skill transferability, geographic density, and age), which capture financial, skill-based, geographic, and age-related adjustment capacity. This index is then presented alongside AI exposure measures from Eloundou and others to identify how adaptive capacity varies across highly exposed occupations.
Importantly, the adaptive capacity index focuses on factors influencing workers’ ability to find new jobs and their earnings after reemployment—not the full range of welfare costs that job displacement can impose, such as job insecurity or the loss of meaning and identity that work provides.
Findings: On average, highly AI-exposed workers appear well-equipped to handle job transitions relative to the rest of the workforce, yet 6.1 million workers still face both high exposure and low adaptive capacity
Combining AI exposure measures with the new adaptive capacity index paints a new picture of AI’s potential impacts on the workforce.
To begin, the analysis here shows that the workers with the highest AI exposure rates possess characteristics that give them higher capacity to navigate job transitions successfully—finding new employment quickly and minimizing earnings losses after job displacement. That is, the most exposed workers may have the most resilience if AI automation or another cause leads to job loss.
Figure 1 shows a large group of 26.5 million workers concentrated to the upper right of the bubble chart. Across this cluster of occupations, many high-exposure occupations such as software developers, financial managers, lawyers, and other professionals benefit from strong pay, financial buffers, diverse skills, and deep professional networks. Given that, these well-positioned workers—who observers often cite as being highly threatened by AI automation—likely possess relatively strong means to adjust to AI-driven dislocation if it were to occur (though of course few such transitions are easy, or come without costs to a worker’s well-being).
Figure 1
By contrast, roughly 6.1 million workers (see Appendix) face both high exposure to LLMs and low adaptive capacity to manage a job transition. Concentrated in jobs located in the lower-right quadrant of Figure 1, these potentially more vulnerable workers are employed in occupations with both top-quartile AI exposure and bottom-quartile adaptive capacity. Many of these workers occupy administrative and clerical jobs where savings are modest, workers’ skill transferability is limited, and reemployment prospects are narrower. As such, if faced with an AI-related job loss, workers in these roles are likely among the most at risk of lower reemployment rates, longer job searches, and more significant relative earnings losses compared to other workers.
Looking more closely, the interplay of adaptive capacity scores with AI exposure scores reveals a positive correlation: As exposure increases, adaptive capacity generally increases as well. This reflects the fact that many highly exposed roles are held by financially secure, skilled, and well-networked workers—often in larger cities—who may have more opportunities to find continued employment. In that fashion, numerous workers in managerial and technical occupations are highly exposed to AI yet are nevertheless relatively well positioned to adapt (see Table 1).
Table 1
At the same time, it’s clear that the collection of occupations characterized by high AI-exposure levels and low adaptive capacity encompasses numerous routine office jobs, which are often held by workers who may struggle to adapt to disruption (see Table 2). Door-to-door sales workers and news and street vendors show the least adaptive capacity among the occupations in the top quartile of AI exposure, followed by a number of clerking and administrative occupations, such as court, municipal, and license clerks; secretaries and administrative assistants; and payroll and timekeeping clerks. In terms of these occupations’ size, office clerks (2.5 million workers); secretaries and administrative assistants (1.7 million); receptionists and information clerks (965,000); and medical secretaries and administrative assistants (831,000) stand out as some of the largest occupations in the list. The combination of employment size, potentially elevated automation impacts, and precarious worker traits highlights occupations where policymakers may benefit from greater visibility into AI’s workforce effects.
Table 2
Shifting focus to the geographical incidence of AI exposure and adaptive capacity, the analysis here shows concentrations of highly exposed and highly adaptive workers are greatest in tech hubs such as San Jose, Calif., and Seattle. Conversely, the share of workers in highly exposed but low-adaptive-capacity occupations ranges from 2.4% to 6.9% in the nation’s metro areas, with a national average of 3.9%. The concentration of exposed and vulnerable workers is greatest in smaller metro areas and college towns, particularly in the Mountain West and Midwest—reflecting such areas’ elevated presence of administrative and clerical workers. Key metro areas with elevated shares of potentially vulnerable workers (those with high exposure but low adaptive capacity) include college towns such as Laramie Wyo., Huntsville, Texas, and Stillwater, Okla.; state capitals such as Springfield, Ill., Carson City, Nev., and Frankfort, Ky.; and small towns in New Mexico and Oklahoma.
Map 1
Overall, the figures, charts, and map here suggest that supplementing AI exposure with measures of worker characteristics yields a different (and potentially more useful) level of insight into potential worker resilience and vulnerability.
Limitations: Significant uncertainty surrounds the question of how AI will impact labor markets, and occupation-level measures cannot tell the whole story
This analysis is not without limitations, and despite the new evidence generated here, there remains significant uncertainty about both the extent to which AI will impact labor markets as well as the differential burdens and opportunities that AI can bring for affected workers. The full NBER paper includes a more complete description of potential limitations. We briefly discuss several here.
First, the adaptive capacity index is computed at the occupation level, but the adaptive capacity of different workers within the same occupation can vary substantially. For example, even though computer network architects score highly on the index, a 30-year-old computer network architect with a diverse range of past industry experience living in San Francisco may be better positioned to manage a job transition than a 56-year-old worker who shares the same job title but has worked at one small IT company in a smaller market for their entire career. Similarly, two software developers may have very different levels of liquid savings to help weather an income shock, and two office clerks may work in labor markets that offer very different sets of alternative work opportunities if displaced.
Additionally, there are numerous ways one could compose a measure of adaptive capacity to displacement. The approach taken here represents an initial attempt to introduce this concept. However, there are dozens of confounding individual, firm, occupation, and local labor market factors that will ultimately shape a worker’s ability to navigate technological displacement, and that evade measurement in this index. The result that AI exposure is positively correlated with measures of adaptive capacity appears robust across many alternative ways of computing the index, but individual occupation-level results will be more sensitive to different approaches. More data from the U.S. context on other factors and their relative importance for shaping post-displacement outcomes would help expand the utility of any adaptive capacity measure.
Finally, the evidence underlying the adaptive capacity estimates here is derived primarily from observed effects in localized displacement events, rather than from large-scale employment shifts across occupations. As a result, the index may be most informative when displacement is relatively isolated—for example, when a worker loses their job but related occupations remain stable. In scenarios in which AI affects clusters of related occupations simultaneously, structural job availability may matter more than individual-level characteristics. Moreover, if AI fundamentally transforms the economy on a scale comparable to the industrial revolution (as some experts have suggested could be possible), it could make entire skill sets redundant across several occupations simultaneously.
How the economy will react to structural changes AI may bring is difficult to predict, and any occupation-level adaptive capacity measure could drastically change as AI impacts skill demands and helps create new jobs and industries. The measure discussed here represents one snapshot in time based on available data on the drivers of adaptive capacity.
Conclusion: Adaptability analysis can help reveal who may be most in need of support to weather AI-driven job transitions
Overall, this analysis offers a more nuanced picture of AI’s possible impacts on workers than AI exposure measures can on their own.
Specifically, the analysis focuses on understanding the degree to which workers in different highly exposed occupations could manage a job transition after involuntary displacement. In doing so, it makes clear the existence of both large zones of strong resilience to job loss across the workforce as well as concentrated pockets of heightened vulnerability if displacement were to occur.
Given this, the report likely has practical use for workforce and employment development practitioners because understanding where workers are most and least resilient to AI-driven labor market change may help inform the optimal use of public funding for workforce adjustment programs.
Such information can also be used to inform efforts to track labor market impacts. For example, policymakers concerned about potential negative impacts from AI-induced displacement may be able to use adaptive capacity measures to target investment in new data collection on groups of workers with lower estimated adaptive capacity. Additionally, such measures could be considered to target and streamline eligibility for particular workforce transition assistance programs.
In sum, as AI continues to spread across the economy, adaptability analysis can provide a starting point for policymakers to better understand who may be most in need of better support to weather job transitions.
Appendix
Complete list of high-vulnerability occupations
Geographic distribution of high-vulnerability occupations
State-level geographic patterns
Data sources
The authors combine data from seven sources:
Survey of Income and Program Participation (SIPP) 2022-2024 Panels: Detailed information on workers’ income, savings, and demographic characteristics used for constructing occupation-level measures of median net liquid wealth.
American Community Survey (ACS) 2024: Microdata on workers’ age distributions across occupations used for calculating the share of workers aged 55 and older.
Occupational Employment and Wage Statistics (OEWS) 2024: Occupation-level wage and employment data used for cross-dataset harmonization of weights and income measures.
Bureau of Labor Statistics Employment Projections: Data on projected employment growth rates by occupation (2024 to 2034) used to calculate growth-weighted skill transferability.
Lightcast 2023: Occupation-level employment data by county and metropolitan statistical area.
O*NET Database 30.1 (2025): Skill importance ratings to measure skill transferability across occupations.
AI exposure data: Measures of occupational exposure to LLMs from Eloundou et al. (2024), specifically their E1+0.5E2 measure.
For smooth data integration, the authors first harmonize occupation codes across datasets to create a common occupational taxonomy. This includes modifications (such as weighted averages, etc.) to group certain occupations differently classified between data sources. More details are available in the full paper.
O*NET > SOC > OEWS > Modified SIPP
Census > SIPP > Modified SIPP
Only occupations meeting strict data quality thresholds (e.g., ≥15 SIPP respondents) are included. The final dataset covers 95.9% of the U.S. workforce (356 occupations) based on OEWS data.
See Online Appendix available here for more detail.
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Daytime sleepiness is an important, yet understudied, dimension of adolescents’ sleep health []. Its prevalence varies widely across countries, ranging from 7.8% to 55.8% [], and it is notably higher in adolescence than in adulthood []. Importantly, daytime sleepiness plays a central role in mediating the adverse effects of sleep impairment on adolescent health and well-being []. Studies have linked it to lower health-related quality of life [], depressive symptoms, anxiety [], heightened risk of mood disorders [], and lower educational achievement []. Given its central role in linking sleep impairment to adverse outcomes, understanding the factors and processes contributing to daytime sleepiness in adolescence warrants greater scholarly attention.
Daytime sleepiness arises from an interplay of intrinsic (eg, brain maturation and sleep disorders) and extrinsic (eg, early school start times and poor sleep hygiene) factors []. Among these, insufficient sleep and late bedtimes on schooldays have been identified as the most direct contributors to daytime sleepiness among adolescents []. Digital media use is an important extrinsic factor known to affect sleep duration and bedtime timing; yet, most research on this association has been cross-sectional, limiting causal interpretations and leaving the direction of effects unclear [,].
Although the number of longitudinal studies is increasing [], the vast majority do not distinguish between-person from within-person associations, which can lead to misleading conclusions about causal effects [,]. Studies that do separate these effects typically focus on short-term dynamics, such as day-to-day changes [-], often in small convenience samples, which limits their relevance for understanding longer-term processes.
To address these gaps in prior research, this study is the first to examine the longitudinal, reciprocal associations among screen time, bedtime, and daytime sleepiness, accounting for both stable between-person differences and within-person processes.
The study also tests whether restricting screen time before sleep moderates these associations. Clarifying whether daytime sleepiness emerges primarily from stable between-person differences, dynamic within-person processes, or both can advance theoretical understanding of how digital media use and adolescent sleep health influence each other. It may also help determine whether interventions should target stable behavioral patterns—such as sleep-related lifestyle habits, household routines, or family norms around screen use—or instead focus on longer-term individual trajectories—such as gradual increases in screen time or seasonal shifts in bedtime habits—or integrate both approaches.
Prior Work
Associations Among Screentime, Bedtime, and Daytime Sleepiness
Cross-sectional research consistently demonstrates positive associations between various screen-based activities—such as television watching, internet use, video gaming, and phone use—and both delayed bedtimes and increased daytime sleepiness [,]. Whereas this evidence cannot be a basis for causal interpretations, it suggests that, for some adolescents, higher screen time, later bedtimes, and greater daytime sleepiness tend to co-occur. This pattern likely reflects stable between-person differences that may be linked to external factors such as individual traits (eg, social anxiety), lifestyle demands (eg, extracurricular commitments), and family environment characteristics (eg, parenting style and household rules) [-].
The recent synthesis of evidence suggests that the causal link between screen time and sleep health is bidirectional, involving 2 potential pathways []. The screen-time-affecting-sleep pathway posits that media use, in particular before or after bedtime, contributes to shorter sleep duration and poorer sleep quality. Four explanatory mechanisms have been proposed: melatonin suppression due to blue light exposure, psychological arousal, displacement of sleep time, and sleep interruptions [-]. Of these, only displacement—that is, delayed bedtime due to screen time—and nighttime interruptions from notifications appear to have a substantial impact on sleep [].
Conversely, the impaired-sleep-affecting-screen-time pathway posits that changes in sleep can contribute to increased media use. Three mechanisms explain this effect. Circadian phase shifts in puberty result in extended evening free time for media use [,]. Adolescents may use digital media to cope with sleep difficulties [,]. Daytime sleepiness is associated with more sedentary behavior, including prolonged screen time [].
Longitudinal evidence supporting the 2 pathways is mixed. Some adolescent studies support the screen-time-affecting-sleep pathway (eg, meta-analysis by Pagano et al []), others report reciprocal associations [,], and some find minimal or no effects [-]. Evidence for the sleep-impairment-affecting-screen-time pathway exists, but in young adult samples []. There are also some longitudinal studies that found little or no support for either pathway or only marginal effects [-]. Such mixed findings may partly stem from conflating between-person and within-person associations in prior longitudinal studies.
To date, only 2 longitudinal studies have investigated the within-person associations between electronic media use and sleep-related outcomes—one focusing on daytime sleepiness [] and the other on bedtime []. The former did not find significant within-person associations between the frequency of social media use and daytime sleepiness in Dutch adolescents (aged 11-15 years), but did find between-person associations []. The latter, a 5-wave study of Finnish adolescents (aged 13-14 years at baseline), found no lagged effects and limited evidence of concurrent within-person associations—higher-than-usual social media use coincided with later-than-usual bedtime, but only in wave 1 []. These sparse findings suggest that the link between media use and sleepiness may arise from stable individual differences rather than changes over time.
A Moderating Role of Screen Time Restriction Before Sleep
Not all screen use is equally adverse for sleep health. In particular, evening screen time is considered detrimental to adolescent sleep [], and restricting it is a common sleep hygiene recommendation []. Among adolescents, presleep screen restriction often results from parent-set technology rules, which cross-sectional studies have linked to less screen use, an earlier bedtime, and longer sleep duration []. While many adolescents do not follow their parents’ technology rules and recommendations [], research synthesis suggests that interventions aimed at reducing prebedtime screen use lead to modest improvements in bedtime and sleep duration []. Although this evidence suggests a potentially protective effect of reducing evening screen use, evidence on whether presleep screen restrictions moderate the longitudinal relationship between adolescents’ screen time and sleep health is largely missing.
Covariates
Both screen use and sleep health vary by age and sex and therefore are important to consider when interpreting associations among adolescents’ screen time and sleep health. In particular, older adolescents sleep less, go to bed later, and spend more time on screens, and younger adolescents are more likely to limit evening screen use [-]. Findings on sex differences in sleep are mixed. Some studies report no substantial differences [], while others show girls sleep more than boys [], or the reverse []. Daytime sleepiness findings are also inconsistent []. Sex differences in screen time are clearer. Boys exceed screen time limits more often [], and sleep-disrupting screen activities differ—girls’ sleep is more affected by social media, while boys’ is impacted by video games []. Together, these patterns indicate that age and sex are important individual factors in understanding variation in adolescents’ screen use and sleep health.
This Study
Prior longitudinal studies have rarely distinguished between stable between-person differences and within-person fluctuations in digital media use and sleep, leaving uncertainty about whether observed associations reflect enduring individual characteristics or dynamic changes over time []. The few adolescent studies that applied this distinction produced inconclusive results, with limited evidence for lagged or concurrent within-person effects [,]. To address this gap, this study extends prior work by examining the reciprocal longitudinal associations among adolescents’ screen time, bedtime, and daytime sleepiness while separating between- and within-person processes. This allows us to clarify whether screen time and sleep co-vary because they influence each other over time or because of stable individual differences among adolescents. Furthermore, testing the moderating role of screen time restriction before sleep provides evidence on whether this common sleep hygiene recommendation mitigates longer-term effects of screen use on sleep health.
Specifically, we hypothesize that adolescents with higher overall screen time go to bed later and experience greater daytime sleepiness (Hypothesis 1); that increases in screen time are associated with a corresponding delay in bedtime and an increase in daytime sleepiness at the subsequent wave (Hypothesis 2), as well as within the same wave (Hypothesis 3); that delayed bedtime and increased daytime sleepiness are each associated with a subsequent increase in screen time (Hypothesis 4). The within-person effects expected in Hypotheses 2-4 reflect changes relative to a person’s typical patterns. Finally, we hypothesize that within-person associations are weaker among adolescents who restrict their screen use before sleep (Hypothesis 5).
Methods
Ethical Considerations
The study was approved by the Research Ethics Committee at Masaryk University (EKV-2018-068). Before participation, respondents were informed about the nature and purpose of the survey, their right to decline involvement, and their ability to skip any questions by selecting the “I prefer not to say” option available for all items. Informed consent was obtained from both adolescents and parents. Parents were instructed not to be present during the adolescent survey to protect privacy. Adolescents were asked to indicate if an adult had observed or intervened. Although most caregivers appeared to comply, this could not be independently verified. All data were fully deidentified prior to analysis, and no identifying information was collected or stored. No identification of individual participants in any images of the manuscript or supplementary material is possible.
Participants received reward points equivalent to approximately US $4, added to the panelist’s account and redeemable as cash or for charity donations.
Study Design and Setting
A longitudinal observational design was used. This 3-wave prospective panel study was a part of a larger multifocal study examining various aspects of adolescents’ use of information and computer technologies and their impact on well-being. The first wave of data collection took place in June 2021, the second in November and December 2021, and the third in May and June 2022, with approximately 6 months between each wave. This study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines []; the completed STROBE checklist is provided in [].
Participants
This study was conducted on a sample of 2500 Czech adolescents aged 11-16 years (mean age 13.43, SD 1.70 years; 1250/2500, 50% girls). Data were collected in the Czech Republic by an external research agency that recruited participants from existing online panels using face-to-face interviews, computer-assisted telephone interviewing, and online methods. Eligible participants were Czech households with at least 1 adolescent aged 11-16 years and a caregiver, enabling data collection from adolescent-parent dyads within the same household. Quota sampling was used to ensure equal representation of gender, age, and their combination and to ensure that the sample reflected the distribution of Czech households with children based on households’ socioeconomic status (head of the household’s education level) and place of residence (Nomenclature of Territorial Units for Statistics, level 3, municipality size, European Commission, 2020). Out of 2500 participants initially recruited at Wave 1, a total of 1654 completed Wave 2, corresponding to an attrition rate of 33.8% (846/2500). At Wave 3, a total of 1102 participants remained in the study. The overall attrition rate from Wave 1 to Wave 3 was 44.1% (1102/2500), with an incremental attrition rate of 33.4% (552/1654) between Wave 2 and Wave 3.
Measures
Screen Time
Screen time was assessed with 3 items, each starting with the question: “How much time (hours and minutes) do you spend doing the following activities during a typical school day?” The three items were: (1) “using a computer (PC or notebook),” (2) “using a cell phone or tablet,” and (3) “watching TV, including various videos on TV (eg, DVD, Netflix).” In response to these items, respondents picked hours and minutes using a time spinner. The screen time score was then computed by adding up the scores of each item.
Bedtime
Bedtime was measured with 1 item: “When do you usually go to bed before school days?” In response to this item, respondents picked hours and minutes using a time spinner.
Daytime Sleepiness
Daytime sleepiness was measured using 4 items from the Pediatric Daytime Sleepiness Scale, which contains 8 items assessing the frequency of specific daytime sleepiness symptoms []. The 4 items were “You get sleepy or drowsy while doing your homework,” “You have trouble getting out of bed in the morning,” “You tell yourself that you need more sleep,” and “You are tired and grumpy during the day.” The items were rated on a 5-point scale: (1) “never,”(2) “rarely,” (3) “sometimes,” (4) “often,” and (5) “very often.” For each measurement occasion, a composite score was calculated as the mean of the items measuring the construct. A higher score indicates higher daytime sleepiness. Cronbach α was computed to assess the reliability of the scale across 3 waves. Reliability estimates were: α=0.77 for Wave 1, α=0.81 for Wave 2, and α=0.82 for Wave 3. These results indicate that the scale has acceptable internal consistency over time. Mean scores of the observed items were used for daytime sleepiness in analyses due to convergence issues when the latent variable was incorporated into the trivariate random intercept cross-lagged panel model (RI-CLPM).
Screen Time Restriction Within 1 Hour Before Sleep
Screen time restriction within 1 hour before sleep was measured at Wave 1. First, respondents were asked: “How long before going to sleep do you usually stop using all devices with a screen, ie, phone, tablet, computer, television?” Respondents picked hours and minutes using a time spinner in response to this item. Then, these data were transformed into a binary variable with values of 0 for adolescents who reported less than 60 minutes and 1 for adolescents who reported 60 minutes or more.
Covariates
Sex and age at baseline were self-reported at Wave 1 and were both included as time-invariant covariates in the analysis. Sex was coded as 0 for girls and 1 for boys, and age was grouped into 11-13 years (0) and 14-16 years (1).
Statistical Analysis
To examine the associations between screen time, bedtime, and daytime sleepiness over time while accounting for both between- and within-person sources of variance, we used RI-CLPMs fitted in lavaan (version 0.6-18) in R (version 4.4.1; R Core Team), allowing unbiased estimation of within-person effects net of stable individual differences []. The robust maximum likelihood estimator (MLR) was used, as it adjusts standard errors and chi-square statistics to accommodate nonnormal data (Section 3: “Testing normality assumptions” in supplementary materials provided by Tkaczyk et al []), yielding more accurate parameter estimates []. The proportion of missing data for the key time-varying variables ranged from 0.0% to 7.3% across waves. Little’s Missing Completely at Random (MCAR) test indicated that the data were not completely missing at random (χ²377=787.8; P<.001; normed χ²377=2.1), suggesting a small to moderate deviation from MCAR. Given the low proportion of missing data (<8% per variable), full information maximum likelihood (FIML) estimation was used to handle missing values. For a detailed breakdown of percentages of missingness for each variable and wave, and results of logistic regressions testing the relationship between key analytical variables, demographics, and dropouts are provided in Section 1: “Attrition analysis” in supplementary materials provided by Tkaczyk et al [].
To obtain more robust estimates, nonparametric bootstrapping with 2000 resamples was used to estimate 95% CIs for both unstandardized and standardized effects. Standardized coefficients represent the SD change in outcomes per 1 SD change in exposure. Chi-square difference tests were used to compare the fit of a nested model with constraints to the fit of the unconstrained model unless otherwise specified. The modeling approach was adapted from Mulder and Hamaker []. In the first step, the unconstrained RI-CLPM was compared to a model where all random intercept variances and covariances were set to zero (statistically equivalent to cross-lagged panel model [CLPM]) to test for stable between-unit differences, using the chi-bar-square test []. The comparison showed that the RI-CLPM fit the data better (Δχ26=286.8; P<.001). In addition, random intercepts of all 3 constructs had significant variance, indicating that there were some stable between-person differences in screen time, bedtime, and daytime sleepiness over time. Second, to assess population-level changes in observed variables, we fixed grand means over time and compared this model to the unconstrained version. The comparison showed that the model without the constraints fit data better (Δχ26=74.1; P<.001), which implies that, on average, there was some change over time in all 3 variables. Third, to test whether the associations between screen time, bedtime, and daytime sleepiness were time-invariant, we constrained the autoregressive and cross-lagged paths, as well as the residual covariances. The model-building procedure indicated the fully unconstrained model as the best-fitting model . At this point, covariates (age and sex) were added to the model. The final model showed an adequate fit (χ215=46.7; P<.001; Comparative Fit Index [CFI]=0.994; Tucker-Lewis Index [TLI]=0.977; root-mean-square error of approximation [RMSEA]=0.029, 90% CI 0.020-0.039; standardized root-mean-square residual [SRMR]=0.020). Fourth, moderation by screen time restriction before bed was tested using a multiple-group extension to RI-CLPM [].
Table 1. Model fit indices for random intercept cross-lagged panel models (RI-CLPMs) examining longitudinal associations between screen time, bedtime, and daytime sleepiness across 3 waves in a longitudinal study of adolescents (aged 11-16 years). Data were collected in the Czech Republic between June 2021 and June 2022.
Model
χ²(df)
CFIa
SRMRb
RMSEAc
TLId
AICe
BICf
M0g
7.3 (3)
0.999
0.010
0.024
0.989
45591.676
45888.702
M1h
294.0 (9)
0.938
0.044
0.113
0.750
45866.448
46128.530
M2i
81.4 (9)
0.984
0.026
0.057
0.937
45653.804
45915.886
M3j
42.7 (18)
0.995
0.023
0.029
0.989
45597.131
45806.796
M3 + Covsk
46.7 (15)
0.994
0.020
0.029
0.977
45301.270
45633.241
aCFI: Comparative Fit Index.
bSRMR: standardized root-mean-square residual.
cRMSEA: root-mean-square error of approximation.
dTLI: Tucker-Lewis Index.
eAIC: Akaike information criterion.
fBIC: Bayesian information criterion.
gM0: fully unconstrained RI-CLPM.
hM1: cross-lagged panel model [CLPM].
iM2: RI-CLPM with grand means constrained over time.
jM3: RI-CLPM with constraint over time imposed on auto-regressive paths, cross-lagged paths, and residual (co)variances.
kM3 + Covs: M0 with covariates (age and sex).
Results
Descriptive Analysis
displays pairwise correlations for time-varying variables across waves, along with their descriptive statistics, skewness, and kurtosis. The means of daytime sleepiness are close to “sometimes” (Wave 1: 2.81, SD 0.80; Wave 2: 2.82, SD 0.82; Wave 3: 2.84, SD 0.82). At Wave 1, approximately every third (788/2500, 32%) participant reported having trouble getting out of bed in the morning often or very often. Getting sleepy or drowsy while doing homework was the least frequent symptom—at Wave 1, approximately every sixth (400/2494, 16%) participant reported experiencing it often or very often.
Table 2. Pearson correlations and descriptive statistics for screen time, bedtime, and daytime sleepiness across 3 waves in a longitudinal study of adolescents (aged 11-16 years). Data were collected in the Czech Republic between June 2021 and June 2022. All correlation coefficients (r) are significant at P<.001.
Variable
STa (W1b)
ST (W2c)
ST (W3d)
BTe (W1)
BT (W2)
BT (W3)
DSf (W1)
DS (W2)
DS (W3)
ST (W1), r
1.00
ST (W2), r
0.63
1.00
ST (W3), r
0.59
0.62
1.00
BT (W1), r
0.23
0.16
0.14
1.00
BT (W2), r
0.17
0.20
0.21
0.61
1.00
BT (W3), r
0.13
0.11
0.18
0.56
0.63
1.00
DS (W1), r
0.13
0.09
0.09
0.22
0.17
0.13
1.00
DS (W2), r
0.15
0.15
0.13
0.20
0.23
0.18
0.57
1.00
DS (W3), r
0.14
0.13
0.16
0.21
0.19
0.20
0.55
0.64
1.00
Mean (SD), hh:mm or scale
06:23 (02:40)
06:11 (02:36)
06:02 (02:37)
09:48 00:56
09:47 (00:56)
09:57 (00:58)
2.81 (0.80)
2.82 (0.82)
2.84 (0.82)
Skewness
0.38
0.51
0.59
0.04
0.31
0.26
0.15
0.13
0.10
Kurtosis
−0.54
−0.29
−0.23
0.28
0.83
0.58
0.14
0.11
0.09
aST: screen time (hh:mm).
bW1: Wave 1.
cW2: Wave 2.
dW3: Wave 3.
eBT: bedtime (hh:mm PM).
fDS: daytime sleepiness (1-5).
Average bedtimes at each wave were before 10 PM (Wave 1: 9:48, SD 00:56; Wave 2: 9:47, SD 00:56; Wave 3: 9:57, SD 00:58). At Wave 1, a total of 14% (353/2465) of participants reported bedtime at 11:00 PM or later (213/1621, 13% at Wave 2 and 190/1093, 17% at Wave 3). Average total daily screen times were close to 6 hours at each wave and showed a decreasing tendency across time (Wave 1: 06:23, SD 02:40; Wave 2: 06:11, SD 02:36; Wave 3: 06:02, SD 02:37).
Intraclass correlation coefficients (ICCs) revealed that between-person differences accounted for approximately 64% of the variance in screen time, 60% in bedtime, and 58% in daytime sleepiness, indicating a smaller but substantial proportion of variance due to within-person changes over time. All variables showed statistically significant and positive correlations both within and across waves.
Between-Person Associations Among Screen Time, Bedtime, and Daytime Sleepiness
Standardized path coefficients of the final RI-CLPM are presented in .
The analysis revealed significant positive associations between the random intercepts of screen time and bedtime (r=0.23, 95% CI 0.15-0.31; P<.001), screen time and daytime sleepiness (r=0.25, 95% CI 0.16-0.34; P<.001), and bedtime and daytime sleepiness (r=0.31, 95% CI 0.22-0.41; P<.001). Consistent with Hypothesis 1, these correlations indicate that adolescents who typically use screens more also tend to go to bed later and experience higher daytime sleepiness. Additionally, those with later bedtimes tend to experience higher daytime sleepiness.
Figure 1. Standardized path coefficients of the final random intercept cross-lagged panel model testing between- and within-person associations among screen time, bedtime, and daily sleepiness across 3 measurement waves in a longitudinal study of adolescents (aged 11-16 years) conducted in the Czech Republic between June 2021 and June 2022. The model controls for the effects of age (at Wave 1 [W1]) and sex on the random intercepts of the time-varying variables. Solid black lines represent significant paths, and dashed lines represent nonsignificant paths. Solid gray paths were fixed to 1. *P<.05; **P<.01; ***P<.001.
Within-Person Associations Among Screen Time, Bedtime, and Daytime Sleepiness
The analysis identified 2 significant cross-lagged effects. Consistent with Hypothesis 2, elevated screen time at Wave 1 relative to a person’s usual patterns, was associated with elevated bedtime at Wave 2 (β=.14, 95% CI 0.01-0.27; P=.02). Similarly, in line with Hypothesis 4, elevated bedtime at Wave 2, relative to a person’s usual patterns, was associated with increased screen time at Wave 3 (β=.24, 95% CI 0.11-0.36; P<.001). No evidence was found for the remaining cross-lagged paths hypothesized in Hypothesis 2 or Hypothesis 4.
Consistent with Hypothesis 3, the analysis revealed consistent concurrent associations between the within-person components of screen time and bedtime (Wave 1: β=.16, 95% CI 0.04-0.27; P=.007; Wave 2: β=.23, 95% CI 0.010-0.36; P<.001; Wave 3: β=.09, 95% CI 0.01-0.19; P=.049), indicating that an increase in screen time—relative to a person’s usual patterns—was associated with a corresponding delay in bedtime with the same wave. No evidence was found to support Hypothesis 3. Additionally, a significant concurrent association between bedtime and daytime sleepiness was observed at Wave 2 (β=.13, 95% CI 0.01-0.26; P=.045) and Wave 3 (β=.08, 95% CI 0.00-0.17; P=.04). This indicates that, within these waves, a delay in bedtime was associated with elevated daytime sleepiness relative to a person’s usual level of sleepiness.
The analysis also revealed autoregressive effects. Elevated bedtime at Wave 2, relative to a person’s usual patterns, was associated with elevated bedtime at Wave 3 (β=.20, 95% CI 0.05-0.36; P<.001), indicating that a delay in bedtime—relative to a person’s usual patterns—tends to carry over time. A similar autoregressive effect was observed for daytime sleepiness, with elevated sleepiness at Wave 2 associated with elevated sleepiness at Wave 3 (β=.24, 95% CI 0.12-0.37; P<.001).
The Role of Covariates
Age significantly predicted the intercepts of screen time (β=.21, 95% CI 0.16-0.25; P<.001), bedtime (β=.36, 95% CI 0.32-0.41; P<.001), and daytime sleepiness (β=.16, 95% CI 0.11-0.21; P<.001), indicating that older adolescents (aged 14-16 years) typically spent more time using screen media, have later bedtimes, and experience higher daytime sleepiness compared with younger adolescents (aged 11-13 years). Sex (boy=1) significantly predicted the intercept of daytime sleepiness (β=.14, 95% CI 0.10-0.19; P<.001), indicating that typical levels of daytime sleepiness are higher for boys than for girls ().
Table 3. Estimated parameters of the random intercept cross-lagged panel model (RI-CLPM) testing between- and within-person associations of screen time, bedtime, and daytime sleepiness across 3 measurement waves in a longitudinal study of adolescents (aged 11-16 years) conducted in the Czech Republic between June 2021 and June 2022. The model controls for the effects of age (at Wave 1) and sex on the random intercepts of the time-varying variables.
Parameter
B
SE
95% CI
P value
β
Between-person associations
Correlations
STia ↔ BTib
0.314
0.062
0.184 to 0.436
<.001
.229
STi ↔ DSic
0.296
0.050
0.177 to 0.407
<.001
.250
BTi ↔ DSi
0.123
0.019
0.084 to 0.159
<.001
.312
Covariates
Age → STi
0.859
0.100
0.658 to 1.05
<.001
.206
Sex → STi
0.050
0.099
−0.138 to 0.247
.62
.012
Age → BTi
0.523
0.033
0.457 to 0.591
<.001
.362
Sex → BTi
0.054
0.033
−0.012 to 0.112
.08
.038
Age → DSi
0.191
0.029
0.135 to 0.248
<.001
.160
Sex → DSi
0.172
0.028
0.113 to 0.228
<.001
.144
Within-person associations
Autoregressive paths
STd (W1e) → ST (W2f)
0.100
0.070
−0.049 to 0.235
.15
.100
BTg (W1) → BT (W2)
0.047
0.081
−0.145 to 0.233
.56
.045
DSh (W1) → DS (W2)
0.027
0.074
−0.147 to 0.183
.72
.025
ST (W2) → ST (W3i)
0.056
0.068
−0.103 to 0.201
.41
.056
BT (W2) → BT (W3)
0.224
0.059
0.04 to 0.387
<.001
.200
DS (W2) → DS (W3)
0.245
0.051
0.112 to 0.362
<.001
.242
Cross-lagged paths
ST (W1) → BT (W2)
0.051
0.023
0.002 to 0.099
.03
.139
ST (W1) → DS (W2)
0.008
0.020
−0.037 to 0.051
.71
.023
BT (W1) → ST (W2)
0.131
0.171
−0.238 to 0.463
.44
.046
BT (W1) → DS (W2)
0.006
0.057
−0.124 to 0.133
.92
.006
DS (W1) → ST (W2)
0.001
0.186
−0.411 to 0.398
>.99
.000
DS (W1) → BT (W2)
-0.018
0.072
−0.178 to 0.125
.81
−.015
ST (W2) → BT (W3)
-0.009
0.022
−0.057 to 0.037
.69
−.021
ST (W2) → DS (W3)
0.008
0.018
−0.037 to 0.051
.66
.024
BT (W2) → ST (W3)
0.635
0.162
0.309 to 0.976
<.001
.235
BT (W2) → DS (W3)
-0.010
0.044
−0.113 to 0.084
.83
−.011
DS (W2) → ST (W3)
0.116
0.173
−0.25 to 0.481
.50
.038
DS (W2) → BT (W3)
0.102
0.061
−0.025 to 0.23
.09
.080
Residual covariances
ST (W1) ↔ BT (W1)
0.156
0.058
0.04 to 0.277
.007
.158
ST (W1)↔ DS (W1)
0.043
0.047
−0.059 to 0.156
.05
.050
BT (W1) ↔ DS (W1)
0.025
0.018
−0.011 to 0.063
.15
.084
ST (W2) ↔ BT (W2)
0.229
0.066
0.087 to 0.364
<.001
.225
ST (W2) ↔ DS (W2)
0.098
0.057
−0.021 to 0.213
.09
.108
BT (W2) ↔ DS (W2)
0.042
0.021
−0.005 to 0.088
.045
.127
ST (W3) ↔ BT (W3)
0.099
0.050
−0.008 to 0.209
.049
.091
ST (W3) ↔ DS (W3)
0.077
0.040
−0.006 to 0.159
.06
.090
BT (W3) ↔ DS (W3)
0.030
0.014
−0.001 to 0.061
.04
.083
aSTi: screen time latent intercept.
bBTi: bedtime latent intercept.
cDSi: daytime sleepiness latent intercept.
dST: screen time.
eW1: Wave 1.
fW2: Wave 2.
gBT: bedtime.
hDS: daytime sleepiness.
iW3: Wave 3.
The Moderating Role of Screen Time Restriction Before Bed
Against Hypothesis 5, comparisons of multiple group RI-CLPMs with and without constraints across groups showed no differences in correlations between random intercepts (Δχ23=6.0; P=.11), residual covariances (Δχ26=3.5; P=.74), or cross-lagged associations (Δχ26=5.3; P=.51) across adolescents who restricted screen time 1 hour before bed at Wave 1 and those who did not. However, some significant differences between those groups were found (Δχ22=32.0; P<.001). Adolescents who restricted their screen time before bed reported, on average, shorter screen time (by 27 minutes and 28 seconds), earlier bedtime (22 minutes and 12 seconds), and lower daytime sleepiness (Δ=0.159; ).
Discussion
Principal Results
This 3-wave prospective panel study examined bidirectional relationships between screen time, bedtime, and daytime sleepiness in a large representative sample of early to midadolescents in the Czech Republic. Findings at the between-person level showed that higher screen time, later bedtimes, and increased daytime sleepiness tend to co-occur among adolescents. At the within-person level, results revealed a bidirectional, transactional association between screen time and bedtime, suggesting mutual reinforcement over time. Additionally, temporary, wave-specific deviations in screen time and bedtime—relative to a person’s usual patterns—were positively correlated, suggesting that increases in screen time and delays in bedtime tend to co-occur within individuals at the same wave. Finally, while restricting screen time before sleep did not modify these associations, adolescents who restricted screen time had lower typical screen time, earlier bedtimes, and less daytime sleepiness on average.
Between-Person Associations Among Screen Time, Bedtime, and Daytime Sleepiness
Consistent with Hypothesis 1, the analysis revealed small to medium positive correlations between screen time, bedtime, and daytime sleepiness at the between-person level, aligning with findings from cross-sectional studies [,,,]. However, previous RI-CLPM studies reported mixed correlation patterns. For instance, Maksniemi et al [] found no significant between-person correlations between active social media use and bedtime, whereas 2 other studies reported medium positive correlations between social media use and daytime sleepiness [] and between media multitasking and sleep problems []. Such inconsistencies may reflect differences across studies in how media use was conceptualized and defined (eg, active vs general social media use).
The between-person associations observed in this study indicate that higher screen time and poorer sleep co‐occur as relatively stable individual tendencies, likely shaped by other stable factors. For example, late chronotype may predispose some adolescents to later bedtimes and heavier evening media use []. Prior work has shown that modifiable factors—such as parenting style [], parental sleep [], media habits [], and household rules [,]—also influence both adolescent media habits and sleep. To guide better-targeted interventions, future longitudinal RI-CLPM studies should investigate how various modifiable family and lifestyle factors influence the media–sleep association over time.
Within-Person Associations Among Screen Time, Bedtime, and Daytime Sleepiness Over Time
Consistent with Hypothesis 2, increased screen time was associated with delayed bedtime 6 months later, but only between Waves 1 and 2. According to the interpretation guidelines proposed by Orth et al [], the effect is considered large. The association, although not consistent across all waves, aligns with prior longitudinal research, including a 6-wave study based on data from the ABCD study among adolescents aged 11‑14 years [] and a 2-wave study among adolescents aged 13-14 years [], both of which link media use to later bedtimes over time. The present findings extend the prior evidence by demonstrating the association even when controlling for stable between‐person differences. Other RI-CLPM studies did not find cross-lagged effects; for instance, Maksniemi et al [] found no association between active social media use and bedtime. Such discrepancies may reflect differences in conceptualizing media use—overall screen time versus active social media use—which involve distinct pathways linking media to sleep. Whereas active social media use mainly disrupts sleep through presleep arousal [], total screen time is more closely related to blue light exposure and sleep displacement, the latter showing stronger and substantial associations with reduced sleep duration [].
Contrary to Hypothesis 2, this study found no evidence of a direct within-person association between screen time and daytime sleepiness in the long term. This result is consistent with 2 previous RI-CLPM studies. Van Der Schuur et al [] also found no evidence of a long-term within-person association between social media use and daytime sleepiness, aside from a small effect of social media stress among girls. Van der Schuur et al [] found no direct path from media multitasking to sleep problems (including daytime sleepiness), except for a marginally significant effect of media multitasking among girls. Although direct effects were absent, indirect pathways remain plausible. Daytime sleepiness may occur when screen use results in later bedtimes []. Although bedtime was not formally tested as a mediator in this study, which should be considered a limitation, future longitudinal studies might examine bedtime delay as a pathway linking screen time to daytime sleepiness.
Consistent with Hypothesis 3, temporary increases in screen time coincided with temporary delays in bedtime across all 3 waves, indicating concurrent within-person associations between the two. Similar results were found by Maksniemi et al [] in a single wave, whereas other RI-CLPM studies did not examine concurrent associations [,]. This pattern likely reflects the mutually exclusive nature of screen use and sleep within daily time allocation []; yet, the association manifests itself in period-specific, typical patterns of behavior—during periods when bedtime is delayed, adolescents have more opportunities for screen use, and conversely, during periods with greater screen use, they have less time available for sleep. Findings further indicate that bedtime remains sensitive to short-term, period-specific changes in screen time (and vice versa) and that both may share common contextual drivers.
Against Hypothesis 3, this study found no evidence of a correlated change between screen time and daytime sleepiness, suggesting that short-term increases in screen time do not directly coincide with increased sleepiness. Similarly, a diary study on smartphone use and next-day sleepiness found no such effects []. Delayed bedtimes in Waves 2 and 3 were concurrently linked to increased daytime sleepiness, likely due to shorter sleep duration []. Overall, the pattern of longitudinal associations found in this study suggests that while screen time and daytime sleepiness are not directly linked at the within-person level, an indirect path is possible, whereby delayed bedtime may mediate the association between technology use and daytime sleepiness.
Consistent with Hypothesis 4, this study found a within-person cross-lagged effect of bedtime on screen time in the subsequent wave: a later-than-usual bedtime predicted increased screen time 6 months later, but only between Waves 2 and 3. This finding aligns with prior longitudinal research showing reciprocal effects between poorer sleep and greater media use [,]. The RI-CLPM study by Van der Schuur et al [] provided partial evidence for the opposite direction, with increased daytime sleepiness predicting decreased social media use over time among boys. Unlike earlier RI-CLPM studies, this study supports the sleep-impairment-affecting-screen-time pathway, demonstrating a substantial effect even after accounting for stable between-person differences. Although this effect was not consistent across all waves, it suggests that adolescents may extend screen use to fill additional evening hours, which likely arises from circadian shifts or related factors [].
Overall, discrepancies in cross-lagged effects across RI-CLPM studies may partly reflect differences in the time intervals between measurements. This study used a 6-month interval, whereas Van der Schuur et al [,] used a 3- to 4-month interval, and Maksniemi et al [] used a 1-year interval. The absence of cross-lagged effects in some cases suggests that these intervals may not have been optimal for capturing the underlying dynamics []. Future research could benefit from greater use of different temporal designs, such as shortitudinals, to identify optimal temporal windows for detecting within-person effects and the temporal dynamics through which media use influences sleep across adolescence.
Taken together, the cross-lagged pattern (screen time → bedtime between Waves 1 and 2; bedtime → screen time between Waves 2 and 3) suggests a reinforcing cycle between increased screen time and delayed bedtime over time. While previous research identified bidirectional links between screen time and sleep [], this study extends prior work by being the first to demonstrate this reinforcing pattern longitudinally using an RI-CLPM that accounts for stable between-person differences. The autoregressive effects further indicate that delayed bedtimes tend to carry over across waves—a finding also reported in other RI-CLPM studies [], which may reflect adolescent circadian shifts or habitual delays associated with greater autonomy or increased school demands []. Considering that delayed bedtimes were concurrently linked to greater daytime sleepiness and prospectively to higher screen time, interventions that promote earlier and more consistent sleep schedules, rather than solely limiting screen use, may be more effective for improving adolescent sleep health.
Effects of Screen Time Restriction Before Sleep
Contrary to Hypothesis 5, this study found no evidence that restricting screen time before sleep affected within-person associations between screen time and sleep, particularly regarding the development of sleep displacement over time. Prior findings are mixed—while experimental studies have shown improvements in sleep outcomes [,], observational studies often report no adverse effects of prebedtime smartphone use [,], with inconsistent adherence to parent-set rules frequently cited as a limiting factor []. These discrepancies likely reflect differences in study design, sampling strategies, and time frames (eg, short- vs long-term). It should also be noted that the comparison groups were defined based on screen time restriction assessed at Wave 1 only. However, this behavior was not stable over time—among those who reported limiting their screen use at Wave 1, only 40% (284/710) did so across all 3 waves, and 65% (460/710) did so at least once thereafter. Future research should account for this temporal variability when examining the long-term effects of screen time restriction.
Adolescents who reported restricting screen use before sleep also tended to report lower overall screen exposure, earlier bedtimes, and less daytime sleepiness than their peers. Although these between-person differences may indicate a protective role of screen time restriction, they could also reflect other stable characteristics such as family environment (eg, parenting style), chronotype, or self-regulation. Prior research has linked adverse parenting styles to poorer sleep quality and greater daytime sleepiness [], and greater sleepiness to lower self-regulation and eveningness chronotype []. Future longitudinal studies should account for these factors and examine their potential moderating roles in the relationship between screen use and sleep outcomes.
Limitations
Several limitations should be considered when interpreting these findings. First, the study relied on self-reported measures of screen time and sleep, which may be prone to inaccuracy [,]. Because overall screen time was calculated by summing reported use across multiple devices that could have been used simultaneously, average values may overestimate actual exposure. Future research should integrate digital trace data [] and wrist-worn accelerometers data [] for more accurate measurements.
Second, measurement simplifications—using total screen time and an abbreviated version of the Pediatric Daytime Sleepiness Scale []—may have reduced precision and obscured associations with sleep []. Future studies should use more detailed measures that account for media functions, content, and context of use [,].
Third, with only three waves spaced 6 months apart, the design was insufficient for modeling longer-term developmental trajectories [,] or accounting for seasonal variability in screen time and sleep [,]. Longer follow-up and more frequent measurement occasions would allow finer modeling of these changes.
Fourth, attrition was higher than in comparable school-based studies [-], likely because data were collected through an online panel and required the agreement of both adolescent and parent or caregiver. Online panels typically exhibit higher attrition rates due to the sustained participant burden and email-based recontact [,], and similar rates have been reported in other adolescent panel studies []. Notably, attrition remained high despite offering substantially increased incentives (160% in Waves 1 and 2; 280% in Wave 3). Dropouts reported slightly higher baseline screen time (Tables S1 and S2 in supplementary materials provided by Tkaczyk et al []), which may limit generalizability to heavy screen users.
Finally, data collection partially overlapped with COVID-19 social distancing measures, which were associated with increased screen time and later bedtimes among adolescents [,]. The stringency of restrictions varied across waves: Wave 1 (June 2021) coincided with the strictest measures, Wave 2 (November-December 2021) with moderate restrictions, and Wave 3 (May-June 2022) after their removal []. This variation may partly explain the observed decrease in screen time and the stability of bedtime between Waves 1 and 2.
Conclusion
This study is the first to test reciprocal longitudinal associations among adolescents’ screen time, bedtime, and daytime sleepiness while separating between- and within-person processes, thereby addressing bias common in prior cross-lagged panel studies. The findings refine theoretical understanding by showing a complex, bidirectional, and mutually reinforcing interplay between screen time and bedtime over time, even after accounting for stable individual differences. Between-person associations revealed that adolescents with higher screen use had poorer sleep, likely reflecting the influence of relatively stable individual and environmental factors. Although specific cross-lagged effects varied across waves, the overall pattern supports both the screen-time-affecting-sleep and sleep-impairment-affecting-screen-time pathways, whereas daytime sleepiness was not affected by this dynamic. Negatively correlated within-person fluctuations further indicate that screen time and bedtime are partly mutually exclusive and may share contextual drivers.
Screen time restriction before sleep did not moderate within-person effects. However, at the between-person level, adolescents who practiced it reported lower screen use, earlier bedtimes, and less daytime sleepiness. Taken together, these findings suggest that interventions emphasizing consistent sleep schedules and supportive family routines—rather than focusing solely on limiting screen use—may be most effective for promoting adolescent sleep health. Future research should incorporate objective measurements on multiple time scales and relevant moderators.
The authors thank Dr David Lacko, Dr Vojtěch Mýlek, and Martin Tancoš for their thoughtful consultations during the preparation of this manuscript. During the preparation of this work, we used the generative artificial intelligence (AI) tool ChatGPT by OpenAI [] to improve language clarity and readability. After using this service, we reviewed and edited the content as needed and take full responsibility for the publication’s content.
This work has been funded by a grant from the Programme Johannes Amos Comenius under the Ministry of Education, Youth and Sports of the Czech Republic from the project “Research of Excellence on Digital Technologies and Wellbeing CZ.02.01.01/00/22_008/0004583” which is co-financed by the European Union. The work of AJK was supported from Operational Programme Johannes Amos Comenius—Project MSCAfellow5_MUNI (No. CZ.02.01.01/00/22_010/0003229). The funding sources were not involved in any research decisions.
The data used in this study are openly available on the Open Science Framework (OSF) [].
None declared.
Edited by S Brini; submitted 13.Jun.2025; peer-reviewed by R Taylor, T Poulain; comments to author 08.Sep.2025; revised version received 01.Dec.2025; accepted 02.Dec.2025; published 21.Jan.2026.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
