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三类城市作业车辆同步迭代新能源渣土车保有量占全省六成多杭州已构建渣土、环卫、清运完整低碳体系_我的网站

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Illustration: Liu Xiangya/GT
Recent media reports have questioned whether a natural gas plant built to power an Amazon data center project in Texas could become the largest climate polluter in the US. The controversy, whatever the eventual outcome, offers a reality check for America's artificial intelligence (AI) drive.
It exposes a growing contradiction: The US is racing to expand its AI capabilities, yet its protectionist trade policies are making it harder and more costly to access some of the clean-energy technologies needed to sustain that expansion. This raises a broader question: Can an energy-intensive AI race afford the costs of renewable energy protectionism?
The US is entering a new era of rising electricity demand. Data centers, the backbone of the AI economy, are emerging as one of the fastest-growing sources of power consumption. Much of that demand is still being met by fossil fuels: The International Energy Agency reports that natural gas supplies more than 40 percent of the electricity used by data centers in the US, making it their largest source of power.
So, it's not surprising that the expansion of data centers has raised concerns over their environmental impact and the pressure they could place on local power systems and electricity bills. A Gallup survey conducted in March found that seven in 10 Americans opposed the construction of AI data centers in their local area, including 48 percent who strongly opposed such projects.
The findings point to a broader challenge for the US: The race to develop AI is increasingly becoming a race to meet growing energy needs. Addressing this challenge will require more than advances in computing technology; it will also depend on an energy system capable of delivering large amounts of reliable, affordable and cleaner power. That, in turn, will require faster development and broader deployment of clean-energy technologies, from solar power to energy storage.
Yet in the clean-energy sector, the US has increasingly relied on protectionist trade measures that limit access to cost-competitive products from global markets. The country has placed greater emphasis on expanding domestic manufacturing capacity, but rebuilding entire clean-energy supply chains at home is a costly and time-consuming process. Even if expanded domestic production is achieved, it is likely to come at a higher cost, making the deployment of renewable technologies more expensive and potentially slower.
The solar industry offers a clear illustration of this policy direction. The US has continued to expand trade barriers in the sector. Reuters reported that the US government announced on Thursday a series of price floors and a 15 percent tariff on products made from polysilicon, a raw material used in solar panels.
The challenge lies in the limited scale of the US polysilicon industry. Reuters reported that the country has two polysilicon factories. Against this backdrop, relying on domestic polysilicon production while restricting access to imports runs counter to the goal of expanding solar power in the US. The country risks creating barriers that ultimately constrain its own access to the global supply chains needed for growth.
The pressing issue for the US is the speed at which new power demand is emerging. The expansion of data centers is creating electricity needs that cannot wait for domestic clean-energy capacity to develop gradually. Global supply chains can provide the scale and speed required in the near term. By narrowing access to these sources, the US risks turning clean-energy policy into a drag on the infrastructure needed for its AI race.
The US has placed AI high on its economic and technological agenda. The outcome of this race will matter greatly, as financial markets are also watching whether America can turn its AI efforts into commercial success.
This leaves the US with a difficult choice: Can it afford the cost of clean-energy protectionism while racing to build AI infrastructure? The answer may be no. Trade barriers that limit access to competitive renewable technologies could ultimately become a constraint on the AI expansion that Washington is seeking to accelerate.
The author is a reporter with the Global Times. [email protected]

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B | 截至今年7月底，杭州新能源工程渣土车保有量已达1046台，占全省总量的65%；今年以来新增617台，已超额完成年度更新目标。政策赋能是这场变革的核心推力。早在2024年7月，杭州城发物流投用全市首批新能源渣土车，其中一台甲醇清洁能源车型挂上“0000”号绿牌。截至目前，该企业已累计投放200台新能源渣土车，投放规模领跑全市。

C | 企业的底气来自实打实的政策红利——2025年1月起施行的相关支持政策明确，通过购置补贴和运营补贴，单车最高可申领21.2万元；此外，新能源渣土车享有早晚高峰通行权限，政府投资项目招投标将新能源车辆保有量纳入评审指标，多维度降低运营成本。“以前柴油车采购价在35万至40万元，新能源车型虽贵了约20万元，但补贴直接拉低了购置门槛。”杭州城发物流董事长曹献稳算了一笔账：油价高位时柴油车每公里成本超3元，甲醇车每公里仅需2.16元；路权开放更直接带动司机增收——传统柴油车日均最多跑3至4趟，新能源车不受限行约束，日均最高可跑6至7趟。绿色转型的红利，更直观体现在城市治理效能的升级上。如今的新能源渣土车配备六路监控，数据实时接入监管平台，超载、疲劳驾驶等立即自动触发预警；全密闭货箱彻底解决了渣土遗撒、扬尘扰民等问题，市民相关投诉量明显减少；甲醇清洁能源车型加注仅需5分钟，续航可达600公里，进一步提升车辆运维效率。“以前见了渣土车就躲，现在这些绿牌车干干净净，沿途再也见不到扬尘了。

D | ”家住钱塘码头周边的市民感受十分真切。这场变革并不局限于渣土运输单一领域。目前杭州已构建起覆盖工程建设、环卫保洁、固废处置的完整低碳体系，新能源渣土车、环卫清扫车、生活垃圾清运车三类绿色作业车辆，已成为杭州街头的流动风景线——全市现有新能源环卫清扫车394台，占比15%；2026年3月投用的首批74台醇氢新能源生活垃圾清运车，较国六柴油车颗粒物（PM）排放下降98%、氮氧化物排放削减82%，且运行噪音更低。按照规划，2027年底杭州生活垃圾清洁运输比例将提升至80%。接下来，杭州城管部门将持续深化政策配套、完善补能基础设施，助力杭州打造绿色低碳发展新高地。市综合行政执法局供图。

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