Category: 3. Business

  • How tech giants with data centers to power AI can reduce consumers’ electricity bills – NPR

    How tech giants with data centers to power AI can reduce consumers’ electricity bills – NPR

    1. How tech giants with data centers to power AI can reduce consumers’ electricity bills  NPR
    2. Demand for AI Data Centers Sends Prospectors Hunting for Land and Power  The New York Times
    3. Crusoe Showcases Cloud Platform and Data Center Expertise at Tech Show London  TipRanks
    4. The Energy Infrastructure Behind the AI Economy  Mexico Business News
    5. Experts: AI data centers leading surge in electricity demand  The Lewiston Tribune

    Continue Reading

  • Key Issues for Companies and Activist Investors Heading into the 2026 Proxy Season – The Harvard Law School Forum on Corporate Governance

    1. Key Issues for Companies and Activist Investors Heading into the 2026 Proxy Season  The Harvard Law School Forum on Corporate Governance
    2. Reporting Season Alert: Five Key Considerations for Proxy Season  Wilson Sonsini
    3. AllianceBernstein | Proxy Voting Outlook: Spotlight Turns to Governance in Transition Year  ACCESS Newswire
    4. 2026 Proxy Season Preview: Fewer Proposals, Less SEC Mediation, and Greater Uncertainty  PR Newswire

    Continue Reading

  • CBP Used Online Ad Data to Track Phone Locations

    CBP Used Online Ad Data to Track Phone Locations

    The United States and Israel launched a war in Iran last week that has already killed more than 1,200 Iranians and spilled out across the Middle East. There are many unknowns about US president Donald Trump’s goals as the conflict enters its second week and the situation seems poised to trigger an energy crisis with reverberations around the world.

    Iran is in a nationwide internet shutdown with only the country’s regime-built intranet available, plunging Iranians into digital darkness and making it difficult for humanitarian aid workers, journalists, and others to disseminate information both inside and outside the country. As strikes on Tehran began last weekend, an apparently hacked prayer app sent messages saying “surrender” and “help is on the way” to Iranians around the country.

    Meanwhile, GPS attacks like jamming—not to mention physical threats—are on the rise in the Strait of Hormuz, threatening shipping vessels. Security camera hacking has emerged as part of the playbook of war. And missile-intercept systems across the Middle East are under strain—and in some cases being destroyed in strikes.

    Trump ousted Department of Homeland Security secretary Kristi Noem this week. Her tenure was marked by aggressive anti-immigration tactics and ICE and CBP’s killing of two US protesters. A highly sophisticated iPhone hacking tool kit that was likely originally built for the US government is in the hands of multiple other nations as well as scammers who have likely used the tools to infect tens of thousands of phones or more. Some US lawmakers are calling for an investigation into the threat of the decades-old side-channel hacking technique. And WIRED went inside how music streaming CEO Elie Habib built the open-source global threat map World Monitor in his spare time.

    And there’s more. Each week, we round up the security and privacy news we didn’t cover in depth ourselves. Click the headlines to read the full stories. And stay safe out there.

    United States Customs and Border Protection has, for the first time, admitted it purchased phone location data from the sprawling, surveillance-heavy online advertising industry. The agency’s acknowledgement was included in a document, called a Privacy Threshold Analysis, obtained by 404 Media through a Freedom of Information Act request. The document relates to a trial that CBP ran between 2019 and 2021.

    The publication reports that CBP purchased data linked to real-time bidding processes. When you see ads online or in apps, they have often been shown to you after automated, instantaneous, auctions take place where advertisers bid to show you that specific ad. The murkiest parts of the advertising industry can collect data from your device, including your phone’s identifying details and location data; this is then repackaged and sold to companies and entities. The data has been called a “gold mine” for tracking people’s daily activities.

    CBP did not respond to 404 Media’s request for comment on whether it is still buying the data; however, ICE has reportedly planned to purchase access to another system, called Webloc, that allows whole neighborhoods to be monitored for mobile phone movements.

    The FBI was able to identify a protester in Atlanta after ultimately obtaining information from Swiss encrypted email service Proton Mail, court documents have revealed this week. A court document reviewed by 404 Media shows that payment information linked to a Proton email address was provided to US law enforcement by Swiss authorities after a request was made under an Mutual Legal Assistance Treaty (MLAT), which allows agencies to share data internationally.

    Swiss officials made a request for the data under Swiss laws to Proton for payment information linked to the email address defendtheatlantaforest@protonmail.com, which was associated with protests in Atlanta. This information was then provided to US law enforcement officials under the international agreements, and they were able to identify an individual linked to the account.

    Continue Reading

  • Hearthstone Esports Kicks Off in 2026 with the Winter Playoffs

    Hearthstone Esports Kicks Off in 2026 with the Winter Playoffs

    After two months of fierce competition, the Hearthstone Esports season kicks off in 2026 with the Winter Playoffs! On Sunday, March 15, nearly 200 of the best players from Asia-Pacific, EMEA, and the Americas will battle for their spot in the Winter Championship. You can watch all the action live, so read on for everything you need to know about this jam-packed day of competitive Hearthstone.

    What you need to know

    All times Pacific.

    • Date: Sunday, March 15

      • Broadcast begins at 12:00 a.m.

    • Format: 4-deck, Best-of-5 Conquest with one ban

      • Friday & Saturday (Not Broadcast): Swiss for all three regions
      • Sunday (Broadcast): Top 8 for all three regions

    • Qualification: Top 4 players from each region will qualify for the Winter Championship in April.
    • Players: Each region’s competitors consist of:

      • The top 50 Competitive Point earners from the Winter Competitive Season (January – February)
      • The top 4 players from each Open Qualifier during the Winter Competitive Season
      • The winner of each Winter Blitz Weekend tournament

    Want to compete yourself? The Spring Qualification Period (March – April) is happening right now. Climb the ladder or join our competitive Discord server for full details on upcoming Open Qualifier tournaments. Maybe we’ll see you in the next Playoffs!

    • Drops: Earn a CATACLYSM Pack by watching on Twitch for one hour.
    • Co-Streamers: Interested in co-streaming the Winter Playoffs with your audience? Apply here. Selected streamers will be able to co-stream the tournament on their channel.
    • Casters: Edelweiss, Lorinda, PocketTrain, Raven, and Sottle
    • Streams:

    Keep up with the latest Hearthstone Esports news on our social media, and join the competitive Discord server to connect with players, share strategies, and learn how you can become a competitor yourself.

    We’ll see you in the Tavern!

    Continue Reading

  • Anandan, R., Gopalakrishnan, S., Pal, S. & Zaman, N. (eds) Industrial internet of things (IIoT): intelligent analytics for predictive maintenance (Wiley, 2022).

  • Ong, K. S. H., Wang, W., Hieu, N. Q., Niyato, D. & Friedrichs, T. Predictive maintenance model for IIoT-based manufacturing: A transferable deep reinforcement learning approach. IEEE Internet Things J. 9 (17), 15725–15741 (2022).

    Google Scholar 

  • Rieger, T., Regier, S., Stengel, I. & Clarke, N. Fast predictive maintenance in industrial internet of things (iiot) with deep learning (dl): A review. In CEUR workshop proceedings (Vol. 2348, p. 69). (2019).

  • Hore, U. W. & Wakde, D. G. Intelligent Predictive Maintenance for Industrial Internet of Things (IIoT) Using Machine Learning Approach. In International Conference on Intelligent Cyber Physical Systems and Internet of Things (pp. 897–913). Cham: Springer International Publishing. (2022), August.

  • Wang, H., Zhang, W., Yang, D. & Xiang, Y. Deep-learning-enabled predictive maintenance in industrial internet of things: Methods, applications, and challenges. IEEE Syst. J. 17(2), 2602–2615 (2022).

    Google Scholar 

  • Alhuqayl, S. O., Alenazi, A. T., Alabduljabbar, H. A. & Haq, M. A. Improving predictive maintenance in industrial environments via IIoT and machine learning. International J. Adv. Comput. Sci. & Applications, 15(4). (2024).

  • Zheng, H., Paiva, A. R. & Gurciullo, C. S. Advancing from predictive maintenance to intelligent maintenance with ai and iiot. arXiv preprint arXiv:2009.00351. (2020).

  • Alabadi, M., Habbal, A. & Guizani, M. An innovative decentralized and distributed deep learning framework for predictive maintenance in the industrial Internet of Things. IEEE Internet Things Journal (2024).

  • Rajawat, A. S. et al. Cognitive adaptive systems for industrial internet of things using reinforcement algorithm. Electronics 12 (1), 217 (2023).

    Google Scholar 

  • Jia, Z. & Ren, L. A cloud-edge adaptive framework for equipment predictive maintenance in IIoT. In IECON 2024-50th Annual Conference of the IEEE Industrial Electronics Society (pp. 1–6). IEEE. (2024), November.

  • Bellavista, P., Della Penna, R., Foschini, L. & Scotece, D. Machine learning for predictive diagnostics at the edge: An IIoT practical example. In ICC 2020–2020 IEEE International Conference On Communications (ICC) (pp. 1–7). IEEE. (2020), June.

  • Hafez, L., Elakkad, E. & Gamil, M. A Study on the Impact of Logistics Service Quality on the Satisfaction and Loyalty of E-Shoppers in Egypt. Open J. Bus. Manag. 9, 2464–2478. https://doi.org/10.4236/ojbm.2021.95133 (2021).

  • Rosati, R. et al. From knowledge-based to big data analytic model: A novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0. J. Intell. Manuf. 34(1), 107–121 (2023).

    Google Scholar 

  • Ong, K. S. H., Wang, W., Niyato, D. & Friedrichs, T. Deep-reinforcement-learning-based predictive maintenance model for effective resource management in industrial IoT. IEEE Internet Things J. 9 (7), 5173–5188 (2021).

    Google Scholar 

  • Gomide, I. et al. Experimental Study on the Behavior of a Solar-Assisted Heat Pump Operating Under Different Water Demand Conditions. Proceedings of COBEM 2025 https://doi.org/10.26678/ABCM.COBEM2025.COB2025-0463 (2025).

  • Xiong, D. D. et al. Nitidine chloride inhibits the progression of hepatocellular carcinoma by suppressing IGF2BP3 and modulates metabolic pathways in an m6A-dependent manner. Mol. Med. 31, 4747 (2025).

  • Youssef, M. H. et al. Relationship Between Obesity, Stress, and Age of Menarche Among Adolescent Female Students in Al-Madinah, Saudi Arabia. Cureus 17, e93739. https://doi.org/10.7759/cureus.93739 (2025).

  • Al-Anzi, F. S., Al-Anzi, A. F. & Sarath, S. Predictive maintenance in industrial IoT (IIoT). In International Conference on Medical Imaging, Electronic Imaging, Information Technologies, and Sensors (MIEITS 2024) (Vol. 13188, pp. 96–105). SPIE. (2024), June.

  • Manikandan, S., Elakiya, E., Rajheshwari, K. C. & Sivakumar, K. Efficient energy consumption in hybrid cloud environment using adaptive backtracking virtual machine consolidation. Sci. Rep. 14, 22869 (2024).

    Google Scholar 

  • Manivannan, R., Manikandan, S., Vadivel, R. & Sophana Jennifer, S. Location based access privileges and controlling the clustering in sustainable 5G challenges. Salud, Ciencia y Tecnología – Serie de Conferencias [Internet]. 3 [cited 2024 Mar. 21];3:402 (2024) Jan.

  • El-kenawy, E.-S.M., Alhussan, A. A., Mattar, E. A. & Radwan, M. Feature selection and hyperparameter tuning in transformer-based deep learning models for photovoltaic power forecasting using the Swordfish Movement Optimization Algorithm (SMOA). Int. J. Electr. Power Energy Syst. https://doi.org/10.1016/j.ijepes.2025.111509 (2026).

    Google Scholar 

  • El-Sayed, M. et al. Smart City electricity load forecasting using greylag goose optimization-enhanced time series analysis (Arabian Journal for Science and Engineering, 2025).

Continue Reading

  • Amid European energy fears, coal creeps back into favour – Financial Times

    Amid European energy fears, coal creeps back into favour – Financial Times

    1. Amid European energy fears, coal creeps back into favour  Financial Times
    2. Coal prices jump as utilities seek alternative to gas  Financial Times
    3. Textile processors feel the heat of rising coal prices  The Times of India
    4. West Asia conflict hits Surat textile sector, shipping costs surge 400%  The Economic Times
    5. Coal prices hit two-year high amid Iran conflict and rising gas costs  UA.NEWS

    Continue Reading

  • Magnetic flux imaging in a 3D superconductor integrated circuit

  • Tolpygo, S. K. Superconductor digital electronics: Scalability and energy efficiency issues. Low Temp. Phys. 42, 361 (2016).

    Google Scholar 

  • West, J. T. et al. Wafer-scale characterization of a superconductor integrated circuit fabrication process, using a cryogenic wafer prober. IEEE Trans. Appl. Supercond 32, 9500712 (1922).

    Google Scholar 

  • Bairamkulov, R. & De Micheli, G. Superconductive electronics: A 25-year review. IEEE Circuits Syst. Mag. 24, 16 (2024).

    Google Scholar 

  • Mansour, R. R. Microwave and millimeter-wave low-temperature superconductor devices. IEEE Microw. Mag 26, 65 (2025).

    Google Scholar 

  • Golden, E. B., Parmar, N. A., Semenov, V. K. & Tolpygo, S. K. Characterization of flux trapping in and fabrication of large-scale superconductor circuits using AC-biased shift registers with 108 500 Josephson junctions. IEEE Trans. Appl. Supercond 35, 1301116 (2025).

    Google Scholar 

  • Schindler, L., Ayala, C. L., Takeuchi, N. & Yoshikawa, N. The effect of quantised flux on AQFP circuits for a double-active-layered niobium fabrication process. IEEE Trans. Appl. Supercond. 34, 1100908 (2024).

    Google Scholar 

  • Tolpygo, S. K. et al. Characterization of adiabatic quantum-flux-parametrons in the MIT LL SFQ5ee+ process. IEEE Trans. Appl. Supercond 35, 1 (2025).

    Google Scholar 

  • Semenov, V. K., Polyakov, Y. A. & Tolpygo, S. K. AC-biased shift registers as fabrication process benchmark circuits and flux trapping diagnostic tool. IEEE Trans. Appl. Supercond 27, 1 (2017).

    Google Scholar 

  • Tolpygo, S. K. et al. Advanced fabrication processes for superconducting very large-scale integrated circuits. IEEE Trans. Appl. Supercond. 26, 1 (2016).

    Google Scholar 

  • Vlasko-Vlasov, V. K., Crabtree, G. W., Welp, U. & Nikitenko, V. I. Magneto-optical studies of magnetization processes in high-Tc superconductors. NATO ASI Ser. E 356, 205 (1999).

    Google Scholar 

  • Tamegai, T. et al. Critical states and thermomagnetic instabilities in three-dimensional nanostructured superconductors. Phys. C. 533, 74 (2017).

    Google Scholar 

  • Burger, L. et al. Numerical investigation of critical states in superposed superconducting films. Supercond Sci. Technol. 32, 125010 (2019).

    Google Scholar 

  • Brandt, E. H. & Indenbom, M. Type-II-superconductor strip with current in a perpendicular magnetic field. Phys. Rev. B. 48, 12893 (1993).

    Google Scholar 

  • Zeldov, E., Clem, J. R., McElfresh, M. & Darwin, M. Magnetization and transport currents in thin superconducting films. Phys. Rev. B. 49, 9802 (1994).

    Google Scholar 

  • Brandt, E. H. Square and rectangular thin superconductors in a transverse magnetic field. Phys. Rev. Lett. 74, 3025 (1995).

    Google Scholar 

  • Silhanek, A. V., Jiang, L., Xue, C. & Vanderheyden, B. Impact of border defects on the magnetic flux penetration in superconducting films. Appl. Phys. Rev. 12, 041324 (2025).

    Google Scholar 

  • Aranson, I. S. et al. Dendritic flux avalanches and nonlocal electrodynamics in thin superconducting films. Phys. Rev. Lett. 94, 037002 (2005).

    Google Scholar 

  • Denisov, D. V., Rakhmanov, A. L., Shantsev, D. V., Galperin, Y. M. & Johansen, T. H. Dendritic and uniform flux jumps in superconducting films. Phys. Rev. B. 73, 014512 (2006).

    Google Scholar 

  • Clem, J. R. & Berggren, K. K. Geometry-dependent critical currents in superconducting nanocircuits. Phys. Rev. B. 84, 174510 (2011).

    Google Scholar 

  • Clem, J. R., Mawatari, Y., Berdiyorov, G. R. & Peeters, F. M. Predicted field-dependent increase of critical currents in asymmetric superconducting nanocircuits. Phys. Rev. B. 85, 144511 (2012).

    Google Scholar 

  • Gurevich, A. & Friesen, M. Nonlinear transport current flow in superconductors with planar obstacles. Phys. Rev. B. 62, 4004 (2000).

    Google Scholar 

  • Friesen, M. & Gurevich, A. Nonlinear current flow in superconductors with restricted geometries. Phys. Rev. B. 63, 064521 (2001).

    Google Scholar 

  • Tamegai, T. et al. Preferential diagonal penetration of vortices into square superconducting networks. Phys. C. 470, 734 (2010).

    Google Scholar 

  • Tsuchiya, Y., Nakajima, Y., Tamegai, T., Nagasawa, S. & Hidaka, M. Thickness and hole-shape dependence of flux penetration into square superconducting networks. Phys. C. 471, 808 (2011).

    Google Scholar 

  • Tsuchiya, Y., Nakajima, Y., Tamegai, T., Nagasawa, S. & Hidaka, M. Anisotropic flux penetration into Nb square superconducting networks. Supercond Sci. Technol. 27, 055008 (2014).

    Google Scholar 

  • Mawatari, Y. Critical state of periodically arranged superconducting-strip lines in perpendicular fields. Phys. Rev. B. 54, 13215 (1996).

    Google Scholar 

  • Mawatari, Y. & Clem, J. R. Geometrical edge barriers and magnetization in superconducting strips with slits. Phys. Rev. B. 68, 024505 (2003).

    Google Scholar 

  • Mawatari, Y., Navau, C. & Sanchez, A. Two-dimensional arrays of superconducting strips as dc magnetic metamaterials. Phys. Rev. B. 85, 134524 (2012).

    Google Scholar 

  • Khapaev, M. M., Kupriyanov, M. Y., Goldobin, E. & Siegel, M. Current distribution simulation for superconducting multi-layered structures. Supercond Sci. Technol. 16, 24 (2003).

    Google Scholar 

  • Shamiul, A., Shafayat, H. M., Rangachar, S. S. & Ahmedullah, A. Cryogenic memory technologies. Nat. Electron. 6, 185 (2023).

    Google Scholar 

  • Glenn, J. et al. PRIMA mission concept. J. Astronom Telesc Instr &Syst. 11, 031628 (2025).

    Google Scholar 

  • Bonavolontà, C., Vettoliere, A., Sorrentino, P. & Granata, C. Superconducting quantum magnetometers for brain investigation. Sensors 25, 4625 (2025).

    Google Scholar 

  • Yang, W., Ju, J., Zhang, H. & Shang, Zh. Design of a dual-band superconducting filter for 5G mobile communication. IEEE Trans. Appl. Supercond 35, 3500608 (2025).

    Google Scholar 

  • Hugot, A. et al. Approaching optimal microwave–acoustic transduction on lithium niobate using superconducting quantum interference device arrays. Nat. Electron. https://doi.org/10.1038/s41928-025-01548-2(2026) (2026).

    Google Scholar 

  • Krasnok, A. et al. Superconducting microwave cavities and qubits for quantum information systems. Appl. Phys. Rev. 11, 011302 (2024).

    Google Scholar 

  • Continue Reading

  • Global Semiconductor Sales Increase 3.7% Month-to-Month in January

    Global Semiconductor Sales Increase 3.7% Month-to-Month in January

    Friday, Mar 06, 2026, 5:00pm

    by Semiconductor Industry Association

    Worldwide chip sales increase 46.1% year-to-year

    WASHINGTON—March 6, 2025—The Semiconductor Industry Association (SIA) today announced global semiconductor sales were $82.5 billion during the month of January 2026, an increase of 3.7% compared to the December 2025 total of $79.6 billion and 46.1% more than the January 2025 total of $56.5 billion. Monthly sales are compiled by the World Semiconductor Trade Statistics (WSTS) organization and represent a three-month moving average. SIA represents 99% of the U.S. semiconductor industry by revenue and nearly two-thirds of non-U.S. chip firms.

    “Following the semiconductor industry’s highest-ever sales total in 2025, the global chip market continued to grow in January of this year, topping December’s results and far outpacing sales from January of last year,” said John Neuffer, SIA president and CEO. “Sales into the Asia Pacific region and China were major drivers of year-to-year growth, and global sales are projected to reach roughly $1 trillion in 2026.”

    Regionally, year-to-year sales in January were up in Asia Pacific/All Other (82.4%), China (47.0%), the Americas (34.9%), and Europe (26.1%), but declined in Japan (-6.2%). Month-to-month sales in January increased in China (5.8%), Asia Pacific/All Other (5.0%), Europe (5.3%), and the Americas (1.2%), but were down in Japan (1.7%).

    For comprehensive monthly semiconductor sales data and detailed WSTS forecasts, consider purchasing the WSTS Subscription Package. For detailed historical information about the global semiconductor industry and market, consider ordering the SIA Databook.

    [January 2026 chart and graph]

    ###

    Media Contact
    Dylan Peterson
    Semiconductor Industry Association
    812-679-8952
    [email protected] 
    About SIA
    The Semiconductor Industry Association (SIA) is the voice of the semiconductor industry, one of America’s top export industries and a key driver of America’s economic strength, national security, and global competitiveness. SIA represents 99% of the U.S. semiconductor industry by revenue and nearly two-thirds of non-U.S. chip firms. Through this coalition, SIA seeks to strengthen leadership of semiconductor manufacturing, design, and research by working with Congress, the Administration, and key industry stakeholders around the world to encourage policies that fuel innovation, propel business, and drive international competition. Learn more at 
    www.semiconductors.org.
    About WSTS
    World Semiconductor Trade Statistics (WSTS) is an independent non-profit organization representing the vast majority of the world semiconductor industry. The mission of WSTS is to be the respected source of semiconductor market data and forecasts. Founded in 1986, WSTS is the singular source for monthly industry shipment statistics.

    Continue Reading

  • Artificial intelligence, greening of occupational structure and total factor energy efficiency

  • Acemoglu D (2018) Artificial intelligence, automation and work. National Bureau of Economic Research Working Paper, No. w24196

  • Acemoglu D, Restrepo P (2019) Automation and new tasks: How technology displaces and reinstates labor. J Econ Perspect 33(2):3–30

    Google Scholar 

  • Acemoglu D, Restrepo P (2020) Robots and jobs: Evidence from US labor markets. J Political Econ 128(6):2188–2244

    Google Scholar 

  • Acemoglu D (2021) Harms of AI. National Bureau of Economic Research Working Paper, No. w29247

  • Acemoglu D, Autor D, Dorn D, Hanson GH, Price B (2016) Import competition and the great US employment sag of the 2000s. J Labor Econ 34(S1):S141–S198

    Google Scholar 

  • Acemoglu D, Autor D, Hazell J, Restrepo P (2022) Artificial intelligence and jobs: Evidence from online vacancies. J Labor Econ 40(S1):S293 S340

    Google Scholar 

  • Altonji JG, Elder TE, Taber CR (2005) Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools. J Political Econ 113(1):151–184

    Google Scholar 

  • Antonopoulos I, Robu V, Couraud B, Kirli D, Norbu S, Kiprakis A, Wattam S (2020) Artificial intelligence and machine learning approaches to energy demand side response: A systematic review. Renew Sustain Energy Rev 130:109899

    Google Scholar 

  • Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: An empirical exploration. Q J Econ 118(4):1279–1333

    Google Scholar 

  • Autor DH, Dorn D (2013) The growth of low-skill service jobs and the polarization of the US labor market. Am Econ Rev 103(5):1553–1597

  • Babina T, Fedyk A, He AX, Hodson J (2023) Firm investments in artificial intelligence technologies and changes in workforcecomposition. National Bureau of Economic Research Working Paper, No. w31325

  • Barbieri N, Marzucchi A, Rizzo U (2023) Green technologies, interdependencies, and policy. J Environ Econ Manag 118:102791

    Google Scholar 

  • Berrone P, Fosfuri A, Gelabert L, Gomez Mejia LR (2013) Necessity as the mother of ‘green’ inventions: Institutional pressures and environmental innovations. Strategic Manag J 34(8):891–909

    Google Scholar 

  • Bocken NMP, de Pauw I, Bakker C, van der Grinten B (2016) Product design and business model strategies for a circular economy. J Ind Prod Eng 33(5):308–320

  • Bose BK (2017) Artificial intelligence techniques in smart grid and renewable energy systems some example applications. Proc IEEE 105(11):2262–2273

    Google Scholar 

  • Bowen A, Kuralbayeva K, Tipoe EL (2018) Characterising green employment: The impacts of ‘greening’on workforce composition. Energy Econ 72:263–275

    Google Scholar 

  • Cainelli G, D’Amato A, Mazzanti M (2020) Resource efficient eco innovations for a circular economy: Evidence from EU firms. Res Policy 49(1):103827

    Google Scholar 

  • Centobelli P, Cerchione R, Chiaroni D, Del Vecchio P, Urbinati A (2022) Blockchain technology for bridging trust, traceability and transparency in circular supply chain. Inf Manag 59(3):103508

    Google Scholar 

  • Chen M, Wang X, Zhang Z (2024) How can the digital economy reduce carbon emissions? Empirical evidence from China. PLoS One 19(6):e0303582

    Google Scholar 

  • Consoli D, Marin G, Marzucchi A, Vona F (2016) Do green jobs differ from non green jobs in terms of skills and human capital? Res Policy 45(5):1046–1060

    Google Scholar 

  • Curtis EM (2018) Who loses under cap and trade programs? The labor market effects of the NOx budget trading program. Rev Econ Stat 100(1):151–166

    Google Scholar 

  • Curtis EM, O’Kane L, Park RJ (2024) Workers and the green energy transition: Evidence from 300 million job transitions. Environ Energy Policy Econ 5(1):127–161

    Google Scholar 

  • Darendeli A, Law KKF, Shen M (2022) Green new hiring. Rev Acc Stud 27(3):986–1037

    Google Scholar 

  • Dauth W, Findeisen S, Suedekum J, Woessner N (2021) The adjustment of labor markets to robots. J Eur Econ Assoc 19(6):3104–3153

    Google Scholar 

  • De Jesus A, Mendonça S (2018) Lost in transition? Drivers and barriers in the eco innovation road to the circular economy. Ecol Econ 145:75–89

    Google Scholar 

  • Deming DJ (2017) The growing importance of social skills in the labor market. Q J Econ 132(4):1593–1640

    Google Scholar 

  • Elliott RJR, Kuai W, Maddison D, Ozgen C (2024) Eco innovation and (green) employment: A task based approach to measuring the composition of work in firms. J Environ Econ Manag 127:103015

  • Fang VW, Tian X, Tice S (2014) Does stock liquidity enhance or impede firm innovation? J Financ 69(5):2085–2125

    Google Scholar 

  • Geissdoerfer M, Savaget P, Bocken NMP, Hultink EJ (2017) The circular economy—A new sustainability paradigm? J Clean Prod 143:757–768

  • Ghisetti C, Marzucchi A, Montresor S (2015) The open eco innovation mode: An empirical investigation of eleven European countries. Res Policy 44(5):1080–1093

    Google Scholar 

  • Grüning P (2025) Fiscal, environmental, and bank regulation policies in a small open economy for the green transition. Resour Energy Econ 82:101493

  • Isogawa D, Nishikawa K, Ohashi H (2012) Innovation height and firm performance: Using innovation survey from Japan (RIETI Discussion Paper Series 12 E 077). Research Institute of Economy, Trade and Industry

  • Kerr WR, Kominers SD (2015) Agglomerative forces and cluster shapes. Rev Econ Stat 97(4):877–899

    Google Scholar 

  • Lachenmaier S, Rottmann H (2011) Effects of innovation on employment: A dynamic panel analysis. Int J Ind Organ 29(2):210–220

    Google Scholar 

  • Lim Z, Sun Y, Xing C, Liu J, He Y, Zhou Y, Zhang G (2022) Artificial intelligence powered large scale renewable integrations in multi energy systems for carbon neutrality transition: Challenges and future perspectives. Energy AI 10:100196

    Google Scholar 

  • Lundgren T, Marklund PO, Zhang S (2016) Industrial energy demand and energy efficiency, Evidence from Sweden. Resour Energy Econ 43:130–152

    Google Scholar 

  • Marin G, Vona F (2019) Climate policies and skill biased employment dynamics: Evidence from EU countries. J Environ Econ Manag 98:102253

    Google Scholar 

  • Martin G (2019). Sustainability prospects for autonomous vehicles: Environmental, social, and urban. Routledge

  • Mehmood MU, Chun D, Zeeshan, Han H, Jeon G, Chen K (2019) A review of the applications of artificial intelligence and big data to buildings for energy efficiency and a comfortable indoor living environment. Energy Build 202:109383

    Google Scholar 

  • Morgenstern RD, Pizer WA, Shih JS (2002) Jobs versus the environment: An industry level perspective. J Environ Econ Manag 43(3):412–436

    Google Scholar 

  • Ounifi HA, Gherbi A, Kara N (2022) Deep machine learning based power usage effectiveness prediction for sustainable cloud infrastructures. Sustain Energy Technol Assess 52:101967

    Google Scholar 

  • Popp D, Vona F, Marin G, Chen Z (2020) The employment impact of green fiscal push: Evidence from the American Recovery and Reinvestment Act. National Bureau of Economic Research Working Paper, No. w27321

  • Pu B, Lu L, Tan J, Ping J, Wu C (2023) Impact of digital economy on energy supply chain efficiency: Evidence from Chinese energy enterprises. Energies 16(1):157–174

    Google Scholar 

  • Stock JH, Yogo M (2002) Testing for weak instruments in linear IV regression (NBER Technical Working Paper No. 0284). National Bureau of Economic Research

  • Sunder J, Sunder SV, Zhang J (2017) Pilot CEOs and corporate innovation. J Financial Econ 123(1):209–224

    Google Scholar 

  • Tianren L, Sufeng H (2024) Does Digital Industrial Technology Integration reduce corporate carbon emissions? Environ Res 257:119313

  • Topalova P (2004) Trade liberalization, poverty and inequality: Evidence from Indian districts (NBER Working Paper No. 11614). National Bureau of Economic Research

  • Vinuesa R, Azizpour H, Leite I, Balaam M, Dignum V, Domisch S, Felländer A, Langhans SD, Tegmark M, Fuso Nerini F (2020) The role of artificial intelligence in achieving the Sustainable Development Goals. Nat Commun 11(1):233

    Google Scholar 

  • Vona F, Marin G, Consoli D (2019) Measures, drivers and effects of green employment: evidence from US local labor markets, 2006–2014. J Econ Geogr 19(5):1021–1048

  • Vona F, Marin G, Consoli D, Popp D (2018) Environmental regulation and green skills: An empirical exploration. J Assoc Environ Resour Economists 5(4):713–753

    Google Scholar 

  • Walker WR (2013) The transitional costs of sectoral reallocation: Evidence from the Clean Air Act and the workforce. Q J Econ128(4):1787–1835

  • Wu H, Wu Z, Wu H, Tang C, Zhang Z (2025) Policy intervention, digital village and micro modern energy use: evidence from the China Family Panel Studies (CFPS). Econ Change Restruct 58(4):72

    Google Scholar 

  • Xu H, Li Y, Lin W, Wang H (2024) ESG and customer stability: a perspective based on external and internal supervision and reputation mechanisms. Humanit Soc Sci Commun 11:981

  • Yu Y, Zhang N (2021) Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China. Energy Econ 96:105125

  • Zhang Z, Wu H, Zhang Y, Hu S, Pan Y, Feng Y (2024) Does digital global value chain participation reduce energy resilience? Evidence from 49 countries worldwide. Technol Forecast Soc Change 208:123712

    Google Scholar 

  • Zhang Z, Zhang Y, Wu H, Song S, Pan Y, Feng Y (2024) Dual effects of automation on economy and environment: evidence from A-share listed enterprises in China. China Econ Rev 88:102308

    Google Scholar 

  • Zhang Z, Zhang Y, Zhao M, Muttarak R, Feng Y (2023) What is the global causality among renewable energy consumption, financial development, and public health? New perspective of mineral energy substitution. Resour Policy 85:104036

    Google Scholar 

  • Zhang Z, Zhao M, Chen Y, Song MC, Gao Y, Feng Y (2025) The nexus between energy legislation, energy transition, and energy resilience: Evidence from 55 countries worldwide. Energy 324:135906

    Google Scholar 

  • Continue Reading

  • PSX down 3,715 points amid Gulf war

    PSX down 3,715 points amid Gulf war

    Shares of 366 companies were traded. At the end of the day, 182 stocks closed higher. PHOTO: AFP/FILE


    KARACHI:

    Pakistan’s stock market registered a sharp decline on Friday as investors resorted to profit-taking ahead of the weekend amid growing geopolitical uncertainty, which dragged the benchmark KSE-100 index down by 3,715 points to close near 157,500.

    The negative session came a day after the market staged a strong rebound of around 3.5%, prompting cautious investors to lock in gains as fears intensified that the US-Iran conflict could escalate into a prolonged war, dampening risk appetite in regional markets. Market participants largely preferred to reduce exposure before the weekend, reflecting heightened uncertainty about global developments.

    The KSE-100 slipped 2.3% during the session, with heavyweights including UBL, Engro Holdings, Fauji Fertiliser Company, Lucky Cement, Hub Power, Meezan Bank, Systems Limited, OGDC and Bank Alfalah collectively shaving 2,124 points off the benchmark index.

    At the close of trading, the KSE-100 posted a significant loss of 3,714.58 points, or 2.30%, and settled at 157,496.10.

    Pakistan’s stock market recorded a sharp weekly decline, with the benchmark index falling 6.21% week-on-week, while the market also experienced a maximum drawdown of 20.7% from its recent peak, according to a note from Arif Habib Limited (AHL). On Friday, the trading session ended on a broadly negative note as 82 shares declined while only 16 advanced, reflecting weak investor sentiment amid prevailing uncertainty.

    Among the stocks contributing positively to the index, Attock Refinery gained 2.87% and Service Industries rose 2.16%. On the downside, UBL, Engro Holdings and Fauji Fertiliser emerged as the biggest drags on the index, declining 3.5%, 4.21% and 1.8%, respectively. Their losses weighed heavily on the overall market performance, contributing to the negative close for the session and extending the broader weekly decline, AHL said.

    Market participants are now closely watching the upcoming monetary policy decision of the State Bank of Pakistan (SBP). According to the brokerage house, the central bank is expected to keep the policy rate unchanged at 10.5%, reflecting a cautious stance in light of the rapidly evolving global economic environment and heightened geopolitical uncertainties.

    Analysts believe the SBP may prefer to maintain the current rate to monitor inflationary trends and external sector dynamics before considering any policy adjustments.

    Investor sentiment may also hinge on geopolitical developments over the weekend. Market observers noted that any signs of de-escalation in global tensions could help improve confidence and potentially support a rebound in equities in the coming week. Conversely, persistent uncertainty in the international environment could keep investors risk-averse, limiting near-term upside in the market.

    Topline Securities, in its review, said that the KSE-100 index witnessed a negative session as sceptical investors, after Thursday’s positive session in which the market rose 3.5%, came in to sell before the weekend due to fears that the US-Iran conflict may turn into a prolonged war.

    The top negative contribution came from UBL, Engro Holdings, Fauji Fertiliser, Lucky Cement, Hubco, Meezan Bank, Systems Ltd, OGDC and Bank Alfalah as they cumulatively wiped off 2,124 points. Traded value-wise, Pakistan Petroleum (Rs1.83 billion), OGDC (Rs1.66 billion), Attock Refinery (Rs1.62 billion), UBL (Rs1.16 billion) and NBP (Rs980 million) dominated the activity, Topline said.

    Overall trading volumes were recorded at 363.1 million shares compared with the previous tally of 723.9 million. The value of shares traded during the day was Rs23.1 billion.

    Shares of 468 companies were traded in the ready market. Of these, 105 stocks closed higher, 311 fell and 52 remained unchanged.

    K-Electric was the volume leader with trading in 36.9 million shares, losing Rs0.24 to close at Rs7.81. It was followed by Cnergyico PK with 22.4 million shares, losing Rs0.28 to close at Rs6.70 and Unity Foods with 19.05 million shares, losing Rs0.62 to close at Rs9.46. Foreign investors sold shares worth Rs571 million, the National Clearing Company reported.

    Continue Reading