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中国地方政府为什么更喜欢“造东西”,而不是“让人消费”?// Why Do Chinese Local Governments Prefer Building Things Over Boosting Consumption?

一个很重要、但经常被忽略的原因,是税制激励。

中国现行增值税是中央和地方共享税。2016年营改增后,地方分享比例从25%提高到50%,而地方拿到的这部分收入,仍然很大程度上跟企业纳税地绑定。换句话说,一家汽车厂、电池厂、化工厂落在哪里,哪里不仅拿GDP、就业和投资,也能拿到持续的增值税税源。

这会自然强化地方政府的制造业偏好。

假设一个新工厂每年创造10亿元应缴增值税。过去地方可能只分到2.5亿元,2016年以后可以分到5亿元。再叠加企业所得税、城建税及各种附加,一个大型制造项目对地方财政的价值非常高。

于是地方政府最合理的行为,就是抢工厂、抢产能、抢总部、给土地、给融资、建产业园。单个地方这样做完全理性,但全国所有地方一起这样做,就容易形成重复投资和过剩产能。

反过来,一个居民多消费1000元,对当地政府的财政收益却没有那么直接。

比如上海居民买一辆安徽生产的车,制造企业缴纳的增值税税源主要仍跟企业纳税地相关。上海创造了最终需求,却未必能像生产地一样直接获得对应税收。

这就是中国税制一个很重要的结构性特征:

地方财政更奖励“生产发生在哪里”,而不是“消费发生在哪里”。

其实“消费地”并不一定难定义。实体商品可以按最终收货地,线下消费可以按门店所在地,汽车可以按上牌地;数字订阅没有收货地址,也可以按信用卡或支付账户的账单/KYC地址归属。规则不需要做到哲学意义上的100%准确,只要统一、稳定、可验证即可。

因此,中国并不一定需要简单加税。可以降低一部分现有VAT,同时把对应税率改成按最终消费地归属的sales tax;或者更简单,把现有地方VAT的一部分从“纳税地分配”改成“消费地分配”。

核心不是税叫什么名字,而是地方政府面对什么激励。

如果新增一座工厂能明显增加地方财政收入,而新增100亿元居民消费却不能,那么地方政府自然会继续优先招商、投资和扩产。

所以中国“重投资、重制造、轻消费”不仅是产业政策问题,也有很强的财政制度基础。

想真正刺激消费,除了给居民发钱,更重要的是让地方政府也能从消费增长中赚钱。


One important but often overlooked reason is the incentive created by China’s tax system.

China’s VAT is shared between the central and local governments. After the 2016 VAT reform, the local share rose from 25% to 50%. Crucially, much of that local revenue is still tied to where the company pays tax. So when an auto plant, battery factory, or chemical plant locates in a city, the local government gets not only GDP, jobs, and investment, but also a recurring VAT tax base.

That naturally strengthens the incentive to attract manufacturing.

If a new factory generates RMB1 billion of VAT a year, the local government might have received roughly RMB250 million before 2016 and RMB500 million afterward. Add corporate income tax, urban maintenance tax, and other surcharges, and a large manufacturing project becomes extremely valuable to local finances.

The rational response is obvious: compete for factories, capacity, headquarters, industrial parks, land investment, and financing support. That may be rational for each individual locality, but when every locality behaves the same way, the national result can be duplicated investment and excess capacity.

Consumption works differently.

If a Shanghai resident buys a car produced in Anhui, much of the VAT-related local revenue still follows the producer’s tax location. Shanghai creates the final demand, but it does not necessarily capture the corresponding marginal tax revenue in the same way Anhui does.

This creates a structural bias:

Local fiscal systems reward where production happens more than where consumption happens.

A consumption-based system does not necessarily have to be technically complicated. Physical goods could be sourced based on the final delivery address, offline services based on the store location, and cars based on the registration location. For digital subscriptions with no shipping address, the tax could be assigned using the billing or KYC address of the credit card or payment account.

The rule does not need to identify the philosophically perfect “true” place of consumption. It only needs to be uniform, stable, and verifiable.

China therefore would not necessarily need to raise taxes. One option would be to reduce part of the existing VAT and replace it with a sales tax allocated according to the final place of consumption. An even simpler option would be to keep the VAT system intact but redistribute part of the local VAT share based on consumption rather than the producer’s tax location.

The key question is not what the tax is called. It is what behavior the fiscal system rewards.

If building another factory clearly increases local government revenue, while another RMB10 billion of household consumption does not, local governments will naturally continue to prioritize investment, manufacturing, and capacity expansion.

China’s bias toward investment and manufacturing is therefore not only an industrial-policy issue. It is also embedded in the fiscal system.

If China wants to stimulate consumption structurally, it is not enough to give consumers more money. Local governments also need to make more money when consumption rises.

ROIC of Nvidia data center built in 2024 for 2026 inference

Assume you invested in 2024 for Nvidia chips based data centers.

Back then, you used H200.

An 8-GPU HGX H200 server is closer to roughly 10–12kW at the server level.

1 MW IT load → ~80–90 eight-GPU servers
× 8 GPUs/server → ~640–720 H200s/MW
1 GW → ~640k–720k H200s

So 680 H200 per MW is probably more reasonable and thus cost ~$35mn per 1MW, or $35bn per GW.

 

 

1 GW IT load 2024 H200-era capex
GPU/HGX servers ~$18–24bn
Networking/storage ~$3–5bn
Facility/power/cooling ~$8–12bn
All-in ~$30–38bn/GW

Source: ChatGPT

How much revenue it can generate today?

Using DeepSeek V4-Pro pricing and token per day estimates like this give you ~$33.3k revenue/day/MW, or ~$12.2bn revenue/year/GW.

 

Per MW/day Tokens
Input ~51.0bn
Output ~12.8bn
Total ~63.8bn

Source: ChatGPT

Assume cash ebitda margin is 80%.

So the 2024 H200 gives you $10bn per year.

After tax (depreciation deducts income), that is ~30% roic over the $35bn capex in 2024.

HK record financing

Alibaba now tops the list with 80bn HKD financing.

排名 公司 / 时间 规模 关键 Terms HKEX 官方公告
1 阿里巴巴 9988 8/23交易,8/24公告 HK$80.0bn 710m新股;HK$112.70;较参考股价折价约3.6%;扩大后占股本3.57%;net约HK$79.7bn;预计8/26交割。 HKEX 配售条款公告
2 中际旭创 3308 7/30上市 HK$53.410bn IPO 54.5m股 × HK$980;net HK$52.891bn;另有8.175m股绿鞋,但该HK$53.41bn数字未计绿鞋。 HKEX 最终发售价及配发结果
3 宁德时代 3750 4/28 HK$39.190bn 配售 62.385m H股 × HK$628.20;较前收折价7.0%,较5日均价折价10.46%;扩大后稀释约1.36%。 HKEX 配售条款公告
4 智谱 2513 7/9 HK$31.411bn 最多 19.78m H股 × HK$1,588;较前收折价12.99%,较5日均价折价6.72%;扩大后约4.25%。 HKEX 配售条款公告
5 立讯精密 2475 7/9上市;8/5绿鞋 ≈HK$25.060bn 基础 383.473m股 × HK$63.28 = HK$24.266bn;8/5部分绿鞋 12.542m股 × HK$63.28 ≈ HK$0.794bn。 IPO最终结果 · 绿鞋公告
6 胜宏科技 2476 4/21上市;4/22绿鞋 ≈HK$23.135bn IPO最终95.85m股、HK$209.88,基础gross HK$20.117bn;随后全额绿鞋14.3775m股,同价发行。(HKEX News) IPO最终结果 · 全额绿鞋公告
7 美的集团 0300 5/6定价;5/7公告 HK$17.248bn 两档各HK$8.624bn、均zero coupon、100%发行。2027档:到期11 May 2027、转股价HK$96.82;2033档:到期13 May 2033、转股价HK$115.76,到期赎回114.95%。 HKEX 双档CB完整条款
8 MiniMax 0100 7/10 HK$16.041bn 配售:35.6m股 × HK$268 = HK$9.541bn;另发 HK$6.5bn zero-coupon CB,100%发行,约7/14/27到期,初始转股价HK$335,较前收premium 12.64%,到期赎回102.75%。 HKEX 配售+CB完整条款
9 联想集团 0992 6/17交易;6/18公告 US$2.0bn zero coupon、100%发行、7年期,到期25 Jun 2033;初始转股价HK$36.70,较签约日收盘价premium约47.5%;net约US$1.979bn。 HKEX CB完整条款
10 MMG 1208 6/16 HK$6.268bn + US$0.8bn CB 配股 705.892m股 × HK$8.88;另发US$800m zero-coupon CB,发行价102%,所以CB gross cash为US$816m;21 Jun 2027到期;初始转股价HK$10.21。 HKEX 配售+CB完整条款
11 牧原股份 2714 2/6上市;3/5绿鞋 ≈HK$12.099bn 基础 273.9514m股 × HK$39 = HK$10.684bn;3/5部分绿鞋 36.2717m股 × HK$39 ≈ HK$1.415bn。 IPO最终结果 · 绿鞋公告
12 紫金矿业 2899 1/29交易;1/30公告 US$1.5bn face / US$1.5375bn gross zero coupon;发行价102.5%;5 Feb 2031到期;初始转股价HK$63.30,较前收premium 37.19%、5日均价premium 45.92%。(HKEX News) HKEX CB完整条款
13 中国宏桥 1378 4/28 RMB10.2bn USD-settled zero coupon CB;100%发行;3 May 2027到期;初始转股价HK$43.90。 HKEX CB完整条款
14 东鹏饮料 9980 2/3上市;2/28绿鞋 ≈HK$11.099bn 基础 40.8899m股 × HK$248 = HK$10.141bn;部分绿鞋 3.8658m股 × HK$248 ≈ HK$0.959bn。 IPO最终结果 · 绿鞋公告
15 华泰证券 6886 2/3 HK$10.0bn zero coupon、100%发行;8 Feb 2027到期;初始转股价HK$19.70,较前收premium 6.78%,较5日均价premium 5.09%;net HK$9.925bn。 HKEX CB完整条款
16 洛阳钼业 / CMOC 3993 1/19交易;1/20公告 US$1.2bn zero coupon、100%发行;24 Jan 2027到期;初始转股价HK$28.03,较前收premium 28.70%,较5日均价premium 26.17%。 HKEX CB完整条款

Source: ChatGPT

Evidence of deflation in China – %Arabica coffee

I met %Arabica Coffee in Hong Kong.

I liked it, especially the design of the coffee shop; I still kept its physical customer loyalty program card.

The coffee chain expanded in mainland China in 2018.

I have been to several of its stores in mainland China and it’s one of my favorites.

In mainland China, the %Arabica coffee loyalty program is more digital (I am using its mini-program on WeChat) and ran separately from the HK one.

The mini-program kept my previous purchases and the Grande Ice Americano is 35 rmb.

You know what, I am buying the %Arabica Grande Ice Americano with delivery to my door for 24 rmb (on weekend) !

As Meituan should also take a cut, the net price for %Arabica is much lower than 23 rmb.

That’s 1/3 cut for consumers and 40-50% cut for %Arabica!

 

That even makes the housing price decline in China “normal” and “not sufficient”.

Best AI

The best AI is the AI that can learn the best after being created.

Education is college is pre-training.

Humans with the best grades in college is probably not the best humans afterwards.

Even you add post-training, which is like internship or few years of work experience, that is still not the best part.

The best AI/human can keep compound on itself.

That is especially true for AI as it can live much longer.

Alibaba’s 3-year payback period

In Alibaba’s earnings call, it touted 3 years of payback and could improve that to 2.5 years or even 2 years.

That sounds strong.. but if you compare, that is nothing.

Even Luckin claims its franchisees have 1.5 – 2 years of payback periods.

And SpaceX said during recent 2q26 earnings that it has <1 year of payback period.

Nebius’s payback period is now 1 year and 10 months, down from 2-3 years, disclosed in 2q26 earnings.

 

Bond yield becomes a topic? looks fine for Europe

Benchmark 10-year yield ranges:

Period Germany Italy Spain Background
Late 1970s–1980s 6–11% 10–14% 11–14% High inflation and nominal growth
Early 1990s peak ~9% 14.2% 14.0% Currency/ERM crisis
2000–07 3–5% 3.5–5.5% 3.5–5.5% Euro convergence
2011–12 crisis peak ~2% 7.3% ~7.6% Euro break-up/default risk
2020–21 low −0.8% 0.45% Around 0% ECB QE and pandemic policy
August 2026 3.26% 4.08% 3.72% Fiscal expansion, inflation and supply

How did European stocks perform?

 

Year STOXX Europe 600 price return Total return Broad environment
1988 +22.2% +25.4% Growth and yields rising
1989 +24.5% +28.0% Reunification optimism
1990 −17.8% −15.3% Bund yields/rates peak; recession fears
1991 +12.5% +15.8% Partial recovery
1992 +1.6% +5.8% ERM currency crisis
1993 +35.9% +40.7% Rate cuts and collapsing yields

 

Index 2011 2012
STOXX Europe 600 price −11.3% +14.4%
STOXX Europe 600 total return −8.6% +18.2%
Germany DAX −14.7% +29.1%
Italy −25.2% +7.8%
Spain IBEX −13.1% −4.7%

Note that 2011-12 is different – one key evidence is German yield yield was falling, while now it’s rising in 2026. Note that 2012 stock increased due to euro-breakup crisis being contained.

Germany’s 10-year yield has increased from about 2.86% at end-2025 to 3.26%, or roughly +40 bp YTD.

Year-end Germany Italy Spain STOXX 600 price return
2008 3.05% 4.47% 3.86% −45.6%
2009 3.14% 4.01% 3.80% +28.0%
2010 2.91% 4.60% 5.37% +8.6%
2011 1.93% 6.81% 5.50% −11.3%
Jul-12 1.24% 6.00% 6.80% Crisis peak
2012 year-end 1.30% 4.54% 5.34% +14.4%

 

Diverging path: Baidu vs. Google

In Q2 2026, Baidu online marketing revenue fell 19% YoY to RMB13.1bn, after falling 22% in Q1.

And AI-native ads doesn’t seem impressive.

Meanwhile Google Search & Other revenue grew 17% YoY to $63.3bn in 2q26.

Google search revenue didn’t feel much impact from growing usage of chat AI.

One difference, probably unrelated to AI-era, is that content sits inside giant closed ecosystems, much of which Baidu either can’t index properly or isn’t the natural starting point for.

Consumers can go directly to vertical apps. This is especially true when mobile is more important than web in China.

Baidu is simply not the end of a discovery journal, while Google still is.

Even if you chat with AI, you might still go to Google to do final checks or to look for places to do the transaction.

Another issue is that Google AI Overview seems more successful in monetization.

Baidu management says it is “deliberately holding back” AI-search monetization.

Google previously disclosed that queries showing AI Overviews monetized at approximately the same rate as traditional Search, and in Q2’26 said it continued to be encouraged by AI Overview monetization even as it expanded into more commercial queries.