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TRUVACE RECORD VERSION
record: TRV-2026-2589
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:58:39.530637Z
status: published
lens: p_space
sector: climate
headline: China’sArtificial Intelligence Industry as a Carbon-Linked Production System: Embodied Emissions, Structural Paths and Demand-Side Drivers
dek: Artificial intelligence (AI) is commonly assessed through the electricity used by models and data centres, leaving the carbon transferred through the wider production system insufficiently resolved. We disaggregate China’s AI industry from the broader information and communication technology sector and construct comparable 31-sector environmentally extended input–output accounts for 2015, 2017, 2020 and 2023. Embodied-emission accounting is integrated with linkage analysis, structural path analysis and structura…
gain_title: (none)
problem_title: An input–output study found China's AI-related embodied CO2 emissions rose from 140.03 Mt in 2015 to 324.37 Mt in 2023, with indirect supply-chain emissions exceeding direct ones.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: An input–output study found China's AI-related embodied CO2 emissions rose from 140.03 Mt in 2015 to 324.37 Mt in 2023, with indirect supply-chain emissions exceeding direct ones.
problem_evidence: AI-related embodied CO2 emissions increased from 140.03 Mt in 2015 to 324.37 Mt in 2023. | Indirect emissions reached 187.01 Mt in 2023 and remained higher than direct emissions
quick_read: A study of China's AI industry estimates its embodied CO2 emissions grew from 140.03 million tonnes in 2015 to 324.37 million tonnes in 2023. Researchers separated AI from the wider information and communication technology sector and built 31-sector input–output accounts for 2015, 2017, 2020 and 2023.

Indirect emissions, those generated across supply chains, reached 187.01 Mt in 2023 and stayed above direct emissions. That shifts attention from model and data-centre electricity use to the industries that supply AI, including electricity and heat, transport, metals and non-metallic minerals.

Between 2020 and 2023, domestic final demand was associated with a 138.82 Mt increase in emissions, while changes in production structure cut 20.24 Mt. The authors argue that efficiency gains alone are not enough and call for demand management, low-carbon electricity, hardware circularity and targeted upstream procurement.
limitation: 
tag: Evidence-backed problem
key_points: Embodied CO2 tied to China's AI industry rose from 140.03 Mt in 2015 to 324.37 Mt in 2023. | Indirect emissions reached 187.01 Mt in 2023, higher than direct emissions. | Backward linkages consistently exceeded forward linkages, meaning emissions arise mainly from what the AI industry buys from other sectors. | About half of the footprint sat within the first three production tiers. | Other ICT, electricity and heat, transport, metals and non-metallic minerals formed the largest short supply-chain paths. | From 2020 to 2023, domestic final demand added 138.82 Mt, while production-structure change reduced emissions by 20.24 Mt.
rundown: The researchers disaggregated China's AI industry from the broader ICT sector and built comparable 31-sector environmentally extended input–output accounts for 2015, 2017, 2020 and 2023. They combined embodied-emission accounting with linkage analysis, structural path analysis and structural decomposition analysis.

The results show most of the footprint sits in supply chains rather than in direct operations. Roughly half of it was concentrated within the first three production tiers, with other ICT, electricity and heat, transport, metals and non-metallic minerals forming the largest short paths.

In the current-price decomposition for 2020 to 2023, domestic final demand was associated with a 138.82 Mt rise, outweighing a 20.24 Mt reduction from production-structure change. The authors frame AI decarbonisation as a systems-governance problem requiring demand management, low-carbon electricity, hardware circularity and targeted upstream procurement.
sources:
- peer_reviewed | Systems | https://doi.org/10.3390/systems14081000 | 2026-08-16
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