Greening the green transition: Why decarbonization-enabling sectors drive emission growth in China's light manufacturing

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper provides a nuanced analysis of the emissions dynamics in light manufacturing, revealing the green transition paradox and the need for differentiated policies.

🏢実務担当者:Corporate sustainability teams can use the insights to assess supply chain emissions, particularly in battery-intensive value chains, and to anticipate regulatory trends.

🏛政策担当者:The findings underscore the importance of designing carbon governance that distinguishes between emissions from enabling vs. non-enabling sectors and the need for more ambitious policy portfolios.

<title>Abstract</title> <p>Light manufacturing plays a vital role in employment and consumer goods production across China’s coastal provinces, yet it accounts for an estimated 15–20% of industrial emissions and faces a fundamental tension between industrial expansion (SDG 9) and climate action (SDG 13). This study examines the drivers of carbon emissions, policy effectiveness, and abatement feasibility in this sector using a unique province-sector-month panel covering eight coastal provinces and eight light manufacturing subsectors from 2018 to 2022. Applying five integrated analytical methods, LMDI decomposition, Tapio decoupling, interrupted time series, threshold regression, and Monte Carlo simulation, we uncover a green transition paradox: battery manufacturing, the most essential subsector for downstream decarbonization through electric vehicles and renewable energy storage, alone accounts for 88.9% of cumulative emission growth. Activity growth emerges as the dominant driver (+ 4,946 cumulative), while energy intensity improvements provide the largest offset, absorbing 72% of the activity effect. Tapio analysis reveals episodic weak decoupling that repeatedly collapses into expansive coupling. Province-level interrupted time series models show significant heterogeneity, with Beijing achieving strong reductions while Tianjin exhibits carbon leakage. Threshold regression identifies a carbon intensity tipping point separating two distinct policy regimes, and Monte Carlo projections indicate that even an integrated policy portfolio achieves only − 26.0% reduction by 2030, falling short of targets aligned with China’s nationally determined contributions. These findings expose a substantial policy implementation gap and call for differentiated carbon governance that distinguishes emission-intensive production serving decarbonization objectives from that which does not. The results offer transferable lessons for other industrializing economies scaling decarbonization-enabling manufacturing.</p>

• Research Square https://doi.org/10.21203/rs.3.rs-9922928/v1first seen 2026-06-06 04:24:48
• crossref https://doi.org/10.21203/rs.3.rs-9922928/v1first seen 2026-06-06 05:32:08