Global Pulse

分类： technology

  • US-China collaboration seen as path to meet AI’s power demands

    US-China collaboration seen as path to meet AI’s power demands

    The global artificial intelligence boom is reshaping energy needs across every continent, and it has carved out an unexpected new space for cooperation between the world’s two largest economies: the United States and China. Industry leaders and clean energy experts argue that a partnership between the two nations on clean energy development and grid management could not only ease the growing energy crunch holding back AI expansion but also deliver widespread benefits for both countries and the entire global economy.

    As AI adoption accelerates, data center footprints are expanding rapidly and computing power requirements are skyrocketing, pushing energy infrastructure to its breaking point. This crisis has emerged as one of the most urgent bottlenecks for sustained AI development worldwide. Against this backdrop, industry observers note that the complementary strengths of the US and China make cross-border collaboration not just a feasible path forward, but a logically necessary one to address the shared challenge.

    Ramkumar Krishnan, a cleantech entrepreneur and technologist with over two decades of experience in clean energy and advanced technology, shared his perspective with China Daily during an interview in San Francisco. “Technologies and products that are used, whether it’s in the US or in China or in other parts of Asia, they’re all very similar,” Krishnan explained. “There are lots of ways that I think technology is a connecting piece that brings all the nations together, because we all need technologies to solve the problem.”

    China has secured its position as a global leader in large-scale clean energy production. Official data from China’s National Energy Administration shows the country’s annual clean power output has already surpassed 10 trillion kilowatt-hours — more than double the comparable figure for the United States, and exceeding the combined total power consumption of the European Union, Russia, India and Japan. This massive scale has allowed China to build unmatched operational experience and deploy extensive infrastructure that other nations can draw from, especially when it comes to developing intelligent grid management systems capable of handling the high, consistent energy demands of AI data centers, Krishnan said.

    Krishnan pointed out that renewable energy technologies hold a major advantage over traditional large-scale power projects in their speed of deployment. A utility-scale solar farm, for example, can be completed and connected to the grid in 18 months or less, while conventional nuclear or hydropower projects often require 10 to 15 years to reach operation. “Bringing the right portfolio of solutions that can help to accelerate the adoption of energy, and bringing new energy, that could be another area that we can solve the demand that we have from AI,” he added.

    For the United States, the key challenge lies in unlocking greater efficiency from existing energy infrastructure, Krishnan noted. The country holds a large amount of untapped excess grid capacity, but much of this capacity remains underutilized due to inconsistent availability and geographically fragmented distribution. “How do we actually intelligently manage that? That’s an area of pretty strong interest — how do we model how energy is used, so that we can utilize that excess capacity in lots of different places, whether it’s industries, EV charging stations,” he said. Notably, Krishnan suggested that AI itself could be part of the solution to the energy crisis it has created: advanced AI systems can enable far more dynamic, intelligent energy management that matches variable renewable energy supply with constantly shifting demand across grids.

    One major Chinese clean energy enterprise is already moving to turn this collaborative vision into concrete action. GCL Group, a leading Chinese clean energy service provider that has already developed utility-scale power projects across California, Colorado and New York, is actively pursuing partnerships with United States AI companies to deliver integrated energy solutions for global AI infrastructure expansion.

    During a recent visit to Silicon Valley, GCL Group chairman Zhu Gongshan outlined the scope of the growing energy challenge. “Computing demands of generative AI are driving explosive growth in the global AI data center market, while putting mounting pressure on power supplies worldwide,” Zhu said. “As demand for computing capacity surges, energy is emerging as one of the biggest bottlenecks to AI development.”

    Zhu highlighted Southeast Asia as a particularly high-potential emerging market for this cross-border collaboration model. The region is seeing a rapid surge in demand for AI computing capacity, and it could be best served by integrated platforms that combine US AI technological expertise with China’s scalable clean energy solutions, he added.

  • Unique AI model tracks global carbon emissions

    Unique AI model tracks global carbon emissions

    A groundbreaking, one-of-a-kind artificial intelligence model developed to map and track carbon emissions across global production chains, consumption patterns, and natural carbon sinks has been unveiled by a team of Chinese researchers, a development that experts say could reshape dynamics in international climate negotiations and rewrite how global emissions accountability is calculated.

    The innovative large language model was publicly launched on Wednesday by the Shanghai Advanced Research Institute (SARI) under the Chinese Academy of Sciences, rolling out at a pivotal moment when China works to support domestic enterprises in hitting ambitious carbon reduction targets while cementing its position as a technical leader in global climate governance frameworks.

    Built as a large language model trained on petabytes of structured and unstructured environmental data, the system boasts 32 billion parameters — the core AI building blocks that act like neural synapses, enabling the tool to detect complex emission patterns and generate accurate, data-backed predictions. To handle the multifaceted nature of global carbon tracking, the model integrates five specialized artificial intelligence sub-programs, called intelligent agents, each tailored to a distinct critical task.

    These specialized agents cover a wide range of use cases: digital simulation to identify the most energy-efficient operational configurations for industrial factories, cross-border carbon transfer tracking that maps how embodied carbon moves between countries through global trade, full life cycle assessment that calculates a product’s total environmental footprint from raw material extraction through end-of-life disposal. The system also includes a natural carbon source accounting agent to quantify carbon sequestration from ecosystems like forests, and an uncertainty analysis agent to validate the reliability and consistency of all output data.

    Gao Yunhu, a lead researcher at SARI, described the AI as a specialized “carbon accounting butler” that outperforms legacy carbon tracking methods by a wide margin. Traditional carbon accounting workflows are notoriously slow, labor-intensive, and costly for businesses, but the new model enables real-time simulation of production processes to help companies identify the most cost-effective pathways to cutting emissions.

    Zhang Xian, director of the Division of Global Environment at the Administrative Center for China’s Agenda 21, echoed this assessment, noting that conventional accounting methods not only drain time and resources but also make it nearly impossible for enterprises to measure emissions accurately across every stage of a product’s supply chain. Unlike these outdated approaches, the new AI tool can conduct a full life cycle assessment starting from the extraction of raw materials, turning what was once a costly regulatory burden into a competitive advantage for businesses by enabling targeted deployment of emission-cutting technologies.

    Lai Xiaoming, chairman of the Shanghai Environment and Energy Exchange, explained that by standardizing emissions quantification across entire industrial and supply chains, the model improves market monitoring, emissions quota verification, and climate policy impact assessment. It also provides robust, reliable technical infrastructure to support global green trade and transparent carbon pricing systems, he added.

    The launch comes at a particularly critical juncture for Chinese exporters, who now face new carbon-based import taxes under the European Union’s Carbon Border Adjustment Mechanism (CBAM), which imposes a price on carbon embedded in carbon-intensive goods including steel and cement entering the EU bloc.

    Mi Zhifu, a professor of climate change economics at University College London, pointed out that the EU currently relies on standardized “default values” to estimate emissions when an importing company cannot provide independently verified emissions data. For many Chinese products, most notably steel, these default values are often significantly higher than the actual emissions generated during production, incorrectly painting Chinese goods as more carbon-intensive than they truly are and exposing exporters to unnecessary extra taxes. By generating independently verifiable, granular emissions data, the new AI model helps Chinese firms avoid these inflated tax assessments, especially in key CBAM-regulated sectors including steel, cement, hydrogen, electricity, and fertilizers.

    One of the model’s most distinctive contributions to global climate accounting is its core focus on consumption-based emissions accounting, a departure from the territorial-based standards that dominate current international frameworks. Under current common standards, emissions are attributed entirely to the country where production takes place. The SARI model, by contrast, tracks “embedded carbon” — the total carbon footprint hidden within finished products traded across borders — to recognize that consuming countries share equal responsibility for the emissions generated during production.

    As an example, Wei Wei, vice-president at SARI, cited China’s exports of renewable energy technology. In 2024, the manufacturing of Chinese-made wind turbines and solar panels generated approximately 2 million metric tons of carbon emissions. Over the operational lifespan of these products, however, they will help countries around the world cut more than 350 million metric tons of carbon emissions, a net climate benefit that is not reflected in traditional territorial accounting.

    Traditional accounting frameworks attribute all emissions from manufacturing for export to China, which obscures the emissions responsibility of developed countries that consume these traded goods, Zhang noted. By quantifying these unrecognized “carbon leaks” embedded in global trade, Chinese climate officials and researchers believe the model provides a more scientifically sound foundation for future international climate negotiations, enabling more equitable allocation of global emissions reduction responsibilities.

  • China unveils large model for carbon emission accounting

    China unveils large model for carbon emission accounting

    In a landmark technological advance that reshapes global climate action infrastructure, China publicly launched a groundbreaking generative artificial intelligence large model dedicated to carbon emission accounting in Shanghai on Wednesday. This launch marks the first full-spectrum carbon accounting system in the world that integrates measurement across production-side emissions, consumption-side emissions, and natural carbon sources, according to its developer, the Shanghai Advanced Research Institute under the Chinese Academy of Sciences (CAS).

    Carbon emission accounting serves as a non-negotiable foundation for global climate policy compliance, a core underpinning for international carbon pricing mechanisms, and a mandatory prerequisite for nations working to meet their peak carbon and carbon neutrality commitments. For decades, the field has been held back by persistent bottlenecks: steep knowledge barriers for practitioners, convoluted and time-consuming data processing workflows, long analysis timelines, and low spatial and temporal resolution in results. The new large model is specifically engineered to overcome these limitations, leveraging generative AI to rewrite the standard operating paradigm of carbon accounting.

    Built upon CAS’s existing foundational scientific model ScienceOne, the new carbon accounting model rests on three core technical pillars. First, it incorporates eight independently owned proprietary datasets that support high-frequency data updates and seamless cross-source data fusion. Second, it relies on a home-grown methodological framework powered by a large language model-based multi-agent collaboration system, which drastically improves the accuracy of accounting results. Third, it operates on a hybrid computing cluster that optimizes resource allocation across internal institutional servers and external high-performance computing centers.

    Currently, the model’s open service interface hosts a 32-billion-parameter vertical-domain large language model paired with an intelligent emissions database, offering access through both conversational user interfaces and open programming interfaces for developers and researchers. Five functionally distinct specialized intelligent agents have been integrated into the system, each tailored to handle specific core tasks: digital simulation and process optimization for industrial systems, accounting for carbon transfer embedded in cross-border trade, full product life cycle assessment, natural carbon source accounting, and systematic uncertainty analysis of results.

    Of particular note is the life cycle assessment agent, which can autonomously complete the entire end-to-end workflow of product carbon footprint accounting — from defining assessment goals and scope, compiling emissions inventories, conducting formal accounting, to interpreting final results — fully automating a process that previously required extensive manual input from specialized experts.

    Building on the model’s high-resolution calculation capabilities, research teams have already completed an initial high-precision national-level carbon holographic map. Using 2022 emissions data as a test case, the model’s new, scientifically more equitable accounting framework produced adjusted emission figures for major economies that differed significantly from traditional production-side calculations published by the Intergovernmental Panel on Climate Change (IPCC): China’s total emissions were adjusted downward by 17.7%, while the United States’ emissions were adjusted upward by 15.2% and Japan’s by 7.2%.

    The model’s analysis also uncovered a key systemic bias in current international carbon policy: the default emission factors used in the European Union’s Carbon Border Adjustment Mechanism (CBAM) systematically overestimate the carbon intensity of Chinese manufactured goods, a finding that underscores the urgent need for more accurate accounting and the adoption of localized, region-specific emission factors for trade policy.

    Beyond identifying structural gaps in global carbon governance, the model also quantifies the global climate benefits of China’s green technology exports. For example, it calculated that Chinese-produced wind turbines and photovoltaic products exported in 2024 generated roughly 2 million tonnes of carbon emissions during their domestic manufacturing phase, but will deliver an estimated 350 million tonnes of cumulative carbon emission reductions during their operational lifetime around the world.

    For China, the new model provides critical technical support for compiling national greenhouse gas inventories, developing the national carbon trading market, driving the green transition of high-emission key industries, and formulating evidence-based responses to international carbon-related trade policies. On the global stage, the breakthrough offers Chinese technical expertise to international efforts to build a fairer, more scientifically rigorous global system for carbon accounting and climate responsibility allocation, strengthening China’s technological influence in global climate governance.

  • China launches new internet satellite group

    China launches new internet satellite group

    On the morning of April 9, 2026 Beijing time, China successfully carried out a new orbital launch mission at the Taiyuan Satellite Launch Center located in northern China’s Shanxi Province, sending a freshly developed batch of low-orbit internet satellites into their pre-planned operational orbit. The mission utilized an upgraded variant of the Long March 6 carrier rocket, a workhorse of China’s domestic commercial and scientific launch fleet that has been repeatedly modified and optimized over years of operational use to improve payload capacity and launch reliability for low-orbit satellite constellation deployment. This specific satellite cluster marks the 21st batch of satellites launched for China’s expanding low-orbit internet constellation, a infrastructure project designed to deliver global high-speed internet coverage, particularly to remote and underserved regions that lack access to consistent terrestrial connectivity. The launch also marks a key milestone for China’s entire Long March carrier rocket program, standing as the 637th flight mission completed by the Long March series, the country’s most long-serving and versatile family of launch vehicles. Since the first Long March launch in 1970, the rocket series has supported nearly all of China’s space initiatives, from crewed space missions and lunar exploration to commercial satellite deployment, cementing its reputation as a reliable foundation for the country’s growing space sector. This latest launch continues China’s steady cadence of low-orbit satellite deployment, as countries around the world expand their space-based internet infrastructure to meet growing global demand for connectivity from aviation, maritime, remote industrial, and rural user bases.

  • Houston, we have a problem … with the toilet

    Houston, we have a problem … with the toilet

    The Artemis II crewed lunar flyby mission, which has marked a historic step for NASA’s deep space exploration ambitions, has hit an unexpected and unusually awkward snag: the $23 million high-tech toilet aboard the Orion capsule has developed a persistent clog that is preventing the system from flushing wastewater into outer space.

    Following a successful loop around the Moon, the four-person crew has been on a steady return trajectory toward a planned Pacific Ocean splashdown this Friday. All critical spacecraft systems have functioned as expected through the mission’s most demanding phases — save for the Universal Waste Management System, the custom-designed toilet built specifically for the Orion deep-space capsule. According to NASA mission officials, the malfunction emerged just hours after the mission launched from Cape Canaveral, Florida, and has reemerged despite initial troubleshooting attempts.

    Flight director Rick Henfling clarified the situation in a Tuesday press briefing from NASA’s Johnson Space Center in Houston, Texas, noting that the toilet itself remains usable, and the separate waste disposal line for solid waste is operating without issue. The core problem is the inability to empty the mission’s wastewater tank. “The challenge that we’re working through is evacuating the wastewater tank, so we’re having to fall back to some other alternate means,” Henfling explained. That workaround has the crew using personal, reusable collapsible containers designed for contingency urine disposal while engineering teams on the ground work through potential fixes.

    Astronaut Christina Koch, one of the four Artemis II crew members, first reported the issue soon after liftoff, noting the system was emitting an unusual “burning heater smell.” Koch initially adjusted the system’s controls and restarted it in coordination with mission control, a step that appeared to resolve the problem at the time. “I’m proud to call myself a space plumber,” Koch joked in her first in-space briefing, adding that the toilet is “probably the most important piece of equipment on board.” Unfortunately, the clog returned shortly after, leaving the crew unable to offload wastewater to space as designed.

    NASA engineers first hypothesized that the blockage was caused by frozen water trapped in the system’s filters, a common issue in the extreme temperature environment of space. To test this theory, mission control ordered the spacecraft to rotate to face the sun, allowing solar heat to “bake off” any accumulated ice, and activated all of the system’s built-in heaters. When the blockage persisted, engineers revised their assessment. Henfling confirmed that the latest working theory blames an unexpected chemical reaction in the urine treatment system. The system relies on chemical additives to prevent the growth of biofilms and harmful microorganisms in the wastewater tank; the reaction may have generated solid debris that has become lodged in the system’s filter, causing the blockage.

    This mission marks the first crewed deep-space test of the Orion toilet, which is an upgraded iteration of the system currently used on the International Space Station. Unlike the Apollo missions of the 1960s and 1970s, which did not carry an onboard toilet at all and forced astronauts to use sealed waste bags, the Orion system was designed to provide greater comfort and privacy for multi-day deep-space missions. Tucked beneath the capsule’s floor, the toilet is the only private space on board the 5-meter-diameter capsule for the four-person crew. It uses suction systems to operate in microgravity, and solid waste is stored in compacted disposable bags to be brought back to Earth for disposal. The small, cramped space is notoriously noisy, requiring astronauts to wear hearing protection during use.

    The current malfunction has been a frequent topic of discussion at Houston mission control press briefings, a coincidence that has not been lost on space observers. Johnson Space Center was the same facility that received the famous 1970 message from Apollo 13 astronaut Jack Swigert: “Houston, we’ve had a problem,” after an oxygen tank explosion derailed that mission’s lunar landing and forced a dangerous emergency return that ultimately brought the crew home safely.

    NASA officials say they will not be able to fully diagnose and resolve the issue until the Orion capsule splashes down and is recovered. “As soon as we get this down on the ground, we’ll be able to get inside and we will get to the root” of the problem, said Lori Glaze, associate administrator of NASA’s Exploration Systems Development Mission Directorate. The mission, which is testing the Orion capsule and Space Launch System rocket for future crewed lunar landings as part of NASA’s Artemis program, remains on track for its scheduled Friday landing.

  • Shanghai hosts forum on AI and human intelligence in education

    Shanghai hosts forum on AI and human intelligence in education

    On Tuesday, Shanghai became the focal point for global education and technology innovation as it hosted the 11th Science Education Forum organized by the Academic Divisions of the Chinese Academy of Sciences, bringing together hundreds of stakeholders to examine how artificial intelligence and human intelligence can work in tandem to reshape modern K-12 and higher education.

    Held at Shanghai Jiao Tong University, this year’s forum centered its core discussions on two pressing priorities for modern education: the ongoing transformation of science-focused classrooms and the advancement of adaptive, personalized learning for primary and secondary school students. More than 400 attendees, including school principals, K-12 frontline educators, educational research scholars, and university-based experts from across China, participated in targeted workshops and dialogues exploring the shifting landscape of 21st century education.

    Speaking at the opening ceremony, representatives from key international bodies including the United Nations Development Programme and the United Nations Educational, Scientific and Cultural Organization joined leadership from the Chinese Academy of Sciences to set the agenda for the event. Forum participants uniformly emphasized that robust, forward-thinking science education forms the foundational backbone of nurturing the next generation of innovative talent. They highlighted that the thoughtful deep integration of artificial intelligence tools with human-led instruction is injecting critical new momentum into science education systems to meet the demands of the modern era.

    On the same day the main forum concluded, Shanghai Jiao Tong University convened a parallel high-level summit at Shanghai Grand Zero Bay focused on AI-driven empowerment for the integrated development of education, scientific research, and talent cultivation. This complementary event drew university presidents and senior representatives from more than 40 top higher education institutions across the globe. Through a series of keynote addresses and closed-door in-depth dialogues, attendees exchanged insights on three core challenges: reshaping educational frameworks to keep pace with AI advancement, leveraging new technologies to accelerate scientific progress, and adapting talent development pipelines to meet evolving workforce needs in the AI age.

    To cap off the day’s announcements, organizers officially launched the annual “SJTU AI Week” initiative, a recurring event scheduled to take place every April. The program is designed to build actionable connections between the academic education sector, cutting-edge technology research, and industrial practice, creating a sustained cross-disciplinary platform that fosters collaborative exchange and innovative problem-solving at the intersection of AI and education.

  • Pony.ai launches robotaxi service in Singapore

    Pony.ai launches robotaxi service in Singapore

    Chinese autonomous driving technology firm Pony.ai has entered a new phase of its global growth strategy, launching its first commercial robotaxi passenger service in Singapore in partnership with local transportation giant ComfortDelGro. The service officially went live on Tuesday, April 8 2026, bringing autonomous mobility options to residents and visitors in the Punggol district of northern Singapore.

    According to Pony.ai, the launch represents a major milestone in the company’s ongoing push to expand its footprint in international autonomous mobility markets. The current service covers a 12-kilometer route that connects two major residential communities in Punggol: Punggol Northshore and Waterway Sunrise, providing convenient last-mile and inter-neighborhood travel options for local users.

    This collaboration is the fruit of a strategic partnership signed between the two companies back in September 2025. ComfortDelGro, Singapore’s largest land transport operator, brings deep local market knowledge, regulatory connections and operational experience to the joint project, while Pony.ai contributes its industry-leading autonomous driving algorithm technology and fleet management capabilities.

    “Partnering with ComfortDelGro to launch passenger services marks a solid step forward for our Robotaxi fleet in Singapore,” stated James Peng, founder and chief executive officer of Pony.ai, in a statement following the launch.

    The Singapore launch aligns with Pony.ai’s aggressive 2026 global deployment plan, which the company outlined during its 2025 full-year financial performance review. The Guangzhou-headquartered firm announced it aims to roll out a fleet of more than 3,000 robotaxis across over 20 cities worldwide by the end of 2026, with nearly half of those cities located outside of China. Currently, the company is working closely with local industry partners and government regulatory agencies across its target international markets to facilitate a smooth transition from small-scale pilot testing to full commercial public operation.

    Industry observers note that the successful commercial launch in Singapore positions Pony.ai as one of the first Chinese autonomous driving firms to gain a foothold in the Southeast Asian mobility market, setting a benchmark for future cross-market expansion of Chinese smart mobility technology.

  • Brit says he is not elusive Bitcoin creator named by New York Times

    Brit says he is not elusive Bitcoin creator named by New York Times

    One of the crypto world’s most enduring unsolved mysteries has reignited after a high-profile New York Times investigation recently named British Bitcoin entrepreneur and developer Adam Back as the anonymous inventor of Bitcoin, known only by the pseudonym Satoshi Nakamoto. Back has publicly and firmly rejected the claim, dismissing the publication’s findings as a classic case of confirmation bias.

    In a post shared on X with the BBC, Back clarified his position in the Bitcoin ecosystem: “I’m not satoshi, but I was early in laser focus on the positive societal implications of cryptography, online privacy and electronic cash.”

    The 11,000-word feature from reporter John Carreyrou laid out several pieces of circumstantial evidence linking Back to Satoshi Nakamoto, including linguistic similarities between Back’s old emails and online posts and the writing style of the person behind the Satoshi pseudonym. The article also noted a correlation between Back’s online activity gaps and the timeline of Satoshi’s sudden disappearance from public crypto forums shortly after Bitcoin’s foundational white paper was published in 2008. The report claimed Back was absent from Bitcoin discussion boards during Satoshi’s most active period, only to reemerge after Satoshi vanished.

    Back pushed back against each of these claims directly. He countered that he was an active contributor to early Bitcoin forums, saying he actually “did a lot of yakking” on the platforms during the period in question. The remaining evidence cited by the New York Times, he argued, is nothing more than “a combination of coincidence and similar phrases from people with similar experience and interests.” Back also joked about his own relatively small Bitcoin holdings, writing online: “Kicking myself for not mining in anger in 2009.”

    The global fascination with Satoshi Nakamoto’s identity stems not just from the mystery itself, but from the enormous fortune the inventor is believed to hold. Satoshi mined more than 1 million Bitcoins in the early days of the cryptocurrency, when mining was far less competitive. That holding accounts for roughly 5% of the total 21 million Bitcoins that will ever exist, and at current market prices, the stash is valued at approximately $70 billion — enough to place Satoshi among the 20 wealthiest people on the planet.

    This recent accusation against Back is far from the first time someone has been publicly “unmasked” as Satoshi Nakamoto, and nearly all prior claims have been denied or disproven. In 2014, Newsweek magazine identified Japanese-American engineer Dorian Nakamoto as Bitcoin’s creator, a claim he immediately denied that has since been fully debunked. In 2015, two tech outlets Wired and Gizmodo pointed to Australian computer scientist Craig Wright, who later went so far as to claim publicly that he was Satoshi. After years of legal wrangling and unsubstantiated assertions, a UK High Court judge ruled Wright was not Satoshi Nakamoto — and Back himself testified as a witness against Wright’s claims during the proceedings.

    More recent attempts to name Satoshi have also fallen flat. In 2024, an HBO documentary named Canadian crypto expert Peter Todd as the inventor, a claim Todd called “ludicrous” and backed up with evidence discrediting the accusation. Just months later, a British man named Stephen Mollah held a London press conference to claim he was Satoshi, but his assertion was widely dismissed by the crypto community.

    For many core members of the Bitcoin community, the anonymity of Satoshi Nakamoto is not just a random curiosity — it is a core part of Bitcoin’s ideological identity as a decentralized, leaderless digital currency unconnected to any single person or institution. Back echoed this widely held view in his recent comments, writing that he does not know who Satoshi Nakamoto is, and “I think it is good for bitcoin.”

  • AI weather model to aid BRI nations

    AI weather model to aid BRI nations

    Against a backdrop of globally rising extreme weather frequency and intensity, nations across the Belt and Road Initiative (BRI) have voiced strong enthusiasm for a new China-initiated meteorological project that leverages artificial intelligence to upgrade weather forecasting capabilities and boost disaster preparedness. The initiative, officially launched in March 2026, is funded by China’s Ministry of Science and Technology and headed by the Center for Earth System Modeling and Prediction under the China Meteorological Administration (CMA).

    The project builds on the foundation of MAZU, China’s existing open-source early warning meteorological platform that has already been rolled out in BRI partner states including Pakistan and Ethiopia, where it currently supports real-time atmospheric monitoring and rapid disaster alert dissemination. For participating nations, the initiative addresses long-standing gaps in climate resilience that have held back sustainable development.

    Kouam Magloire, head of data processing at Cameroon’s national meteorological services, noted that the collaboration represents a transformative opportunity for his country to reinforce early warning infrastructure and improve response outcomes when extreme weather strikes. Mongolia, which regularly faces severe droughts, winter blizzards and other extreme events, also highlighted the urgent need for AI-powered nowcasting — short-term forecasts ranging from minutes to hours ahead that rely on high-resolution satellite and radar data. “Through this partnership, we aim to build a far more advanced long-term forecasting system that can better protect our communities,” said Altansuvd Bold, an engineer with Mongolia’s National Agency for Meteorology and Environmental Monitoring.

    Ethiopia, another BRI partner already hosting the MAZU platform, emphasized China’s leading position in both meteorological innovation and artificial intelligence development. “Ethiopia looks forward to accessing cutting-edge technology through this project, training local technical experts, and closing critical gaps in our national nowcasting and early warning services,” explained Leta Bekele Gudina, a senior expert at the Ethiopian Meteorological Institute.

    Data from the CMA underscores the urgent need for this intervention: between 1980 and 2022, BRI participating nations suffered an average of $214.7 billion in direct annual economic losses from meteorological disasters, accounting for 28.4% of total global losses from such events. Most of these countries face systemic constraints including sparse weather observation networks, limited computing infrastructure, and outdated forecasting technology, all of which hinder effective disaster preparedness and long-term sustainable growth.

    To tackle these overlapping challenges, the project’s core goal is to develop a fully integrated AI-powered forecasting system that delivers accurate predictions across all time scales, from immediate nowcasting out to sub-seasonal outlooks. The new framework combines traditional physical atmospheric models with cutting-edge machine learning approaches, and will be customized to fit the unique geographic and climatic conditions of each partner nation. A flexible, modular intelligent forecasting device will also be designed to adapt to nations with varying levels of existing technical infrastructure, removing barriers to deployment.

    Project leader Han Wei outlined the initiative’s implementation timeline: the platform will operate for a minimum of six months across more than six BRI partner nations, with early warning services expected to reach approximately 10 million vulnerable people once fully deployed. All AI forecasting models developed through the project will eventually be integrated into the existing MAZU platform to create a sustained, stable technological foundation for long-term international meteorological cooperation.

    Leading Chinese climate scientists have praised the initiative for aligning cutting-edge technology with pressing global development needs. Chen Deliang, an academician of the Chinese Academy of Sciences, noted that the project directly answers unmet urgent demands from BRI nations while advancing the practical application of artificial intelligence in the atmospheric sciences. Zhang Xiaoye, an academician of the Chinese Academy of Engineering, added that future work should focus on strengthening regional downscaling techniques to better tailor forecasting outputs to the specific needs of individual partner countries.

  • Driverless delivery cars transform courier sector

    Driverless delivery cars transform courier sector

    Across Chinese cities and rural regions, a quiet technological revolution is transforming last-mile delivery. On a routine day in Qingdao, a coastal metropolis in eastern China’s Shandong Province, local resident Zhou Li stepped up to a boxy, driverless vehicle parked curbside, scanned a QR code on its exterior, and retrieved her grocery order of cold beer, savory sausage and roasted nuts. Minutes later, the compact autonomous vehicle glided away silently to its next drop-off, its navigation entirely self-directed.

    Zhou’s first encounter with unmanned delivery left her stunned, she recalled, adding that the service — priced at just 9.9 yuan ($1.44) for trips up to 30 kilometers — struck her as surprisingly affordable. What once seemed like science fiction has now become a common sight on Qingdao’s public streets, where roughly 1,150 autonomous delivery units currently operate, making the city one of China’s largest hubs for real-world testing and deployment of this technology.

    Unlike conventional passenger vehicles, these driverless delivery units have no steering wheel or dedicated driver cabin. Clocking in at roughly half the size of a standard sedan, they rely on sophisticated artificial intelligence systems to map optimal routes, interpret real-time road conditions, detect traffic signals and nearby obstacles, and execute automatic braking or evasive maneuvers to prevent collisions. Today, these vehicles handle a wide range of core logistics tasks across China: moving bulk parcels from central distribution hubs to local sorting stations, delivering temperature-sensitive perishable goods and cold-chain pharmaceuticals on tight schedules, and supporting internal logistics operations within large industrial parks.

    Qingdao’s largest fleet operator is Neolix, one of China’s leading domestic developers of autonomous delivery technology. The company launched its pilot service in the port city in June 2025, rolling out vehicles with a 1-ton load capacity, 6-cubic-meter cargo hold, a maximum cruising speed of 45 kilometers per hour and a 200-kilometer battery range on a single charge. According to Neolix Chief Technology Officer Miao Qiankun, the firm has been refining AI-powered visual navigation algorithms since 2021, equipping vehicles with the decision-making capabilities of a veteran human driver while drastically cutting the costs associated with map data collection and real-time updates. As of September 2025, Neolix has deployed more than 10,000 autonomous vehicles globally, partnering with major Chinese courier companies while expanding its footprint to more than 15 countries and regions across Asia, Europe and the Middle East, including Japan, South Korea, Germany and the United Arab Emirates.

    Qingdao is far from the only region embracing this innovation. Driverless delivery has moved from experimental testing to routine daily operation across more than 100 Chinese cities, supported by policy backing and growing market demand. In Shenzhen, the southern technology hub in Guangdong Province, 432 autonomous delivery vehicles completed 1.02 million drop-offs in September 2025 alone, generating 8.7 million yuan in operational revenue. In Beijing, leading on-demand delivery platform Meituan has adopted a hybrid human-machine model: autonomous vehicles transport bulk orders from warehouses to neighborhood relay points, where human couriers complete the final short-distance drop-off to customers. Even in remote rural areas of the Xinjiang Uygur Autonomous Region, unmanned vehicles now serve isolated villages located up to 60 kilometers from distribution hubs, where traditional delivery is unprofitable due to the small volume of parcels per trip.

    Industry operators highlight the drastic cost savings unlocked by this technology. Compared to traditional human-led delivery models, autonomous fleets can cut overall operating costs by nearly half, according to Yao Lei, a manager at major Chinese express provider Yunda Express. By June 2025, more than 100 Chinese cities had launched official pilot programs permitting autonomous delivery vehicles to operate on public roads. In September, China’s Ministry of Commerce and other national regulatory bodies released new policy guidelines encouraging regions with appropriate infrastructure to accelerate the development of unified operational and safety standards for the emerging sector.

    Economics professor Li Tiegang of Shandong University’s School of Economics explained that the key value of low-cost, easily dispatched, high-efficiency autonomous delivery is its ability to solve long-standing pain points in last-mile logistics, most notably rising operational costs and widespread industry labor shortages. Li noted that China’s rapid large-scale adoption of this technology stems from three overlapping factors: mature domestic autonomous driving technology, robust market demand from a fast-growing e-commerce sector, and clear policy support from national and local governments.

    The development of intelligent connected vehicles, including autonomous delivery units, is explicitly highlighted as a strategic emerging industry in China’s 14th Five-Year Plan (2026-2030), which also encourages the integration of artificial intelligence across all sectors of the economy to better meet evolving public daily needs. Li emphasized that the rollout of autonomous delivery is not aimed at replacing human workers entirely. Instead, the model centers on collaborative human-machine work: while autonomous systems optimize routine long-distance and bulk transport to improve service efficiency, the transition will also create new job opportunities in vehicle operation, maintenance and fleet dispatch.