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産業用ロボット露出と都市の低炭素移行効率：中国都市からの証拠

Industrial Robot Exposure and Urban Low-Carbon Transition Efficiency: Evidence from Chinese Cities (原題)

Xingchen Lai, Fan Li, Hiroatsu Fukuda

Land📚 査読済 / ジャーナル2026-10-30#省エネOrigin: CN経営インパクト: コスト削減対象セクター: manufacturing
DOI: 10.3390/land15112074
原典: https://doi.org/10.3390/land15112074

🤖 gxceed AI 要約

日本語

中国278都市の2010〜2023年パネルデータを用い、Super-SBMモデルで都市の低炭素移行効率（LCTE）を測定し、産業用ロボット露出との関連を二方向固定効果モデルで推計した。ロボット露出はLCTEと正に関連し、緑の技術革新や科学技術投資が経路として整合する。効果は中小都市でより顕著で、空間依存性も確認されたが、隣接都市への間接効果は統計的に有意でない。

English

Using panel data for 278 Chinese cities (2010–2023), the study measures urban low-carbon transition efficiency (LCTE) via a Super-SBM model and estimates its association with industrial robot exposure using two-way fixed effects. Robot exposure is positively associated with LCTE, with green innovation and S&T investment as consistent pathways; effects are stronger in small and medium cities. Spatial dependence is confirmed, but indirect spillover effects are not statistically significant.

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

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本では製造業の省人化・自動化投資とGX（省エネ・脱炭素）を結びつける議論が乏しく、自動化投資の環境効率への波及を測る分析枠組みとして参考になる。ただし中国都市データであり、日本のSSBJ・有報開示への直接の示唆は限定的。

In the global GX context

Adds empirical evidence to the automation–decarbonization nexus, a theme largely absent from TCFD/ISSB disclosure debates, which tend to treat operational efficiency gains as opaque. Useful for scholars asking whether industrial automation should be counted as a transition lever in climate-risk and transition-plan analysis.

👥 読者別の含意

🔬研究者:自動化と都市低炭素効率の関連を測るSuper-SBM＋空間計量の手法設計が参考になる。

🏢実務担当者:自動化・省人化投資を省エネ効果として説明する際の定量的な論拠になり得る。

🏛政策担当者:産業自動化支援策を都市の脱炭素効率向上と結びつける政策設計の示唆がある。

📄 Abstract（原文）

Industrial robots are increasingly embedded in urban production systems, yet their association with city-level low-carbon transition efficiency remains insufficiently understood. Using panel data for 278 Chinese cities from 2010 to 2023, this study measures urban low-carbon transition efficiency (LCTE), the relative efficiency of urban resource conversion under environmental constraints, with a super-efficiency slack-based measure (Super-SBM) model. We estimate its association with industrial robot exposure using a two-way fixed-effects model, followed by robustness checks, a lagged-exposure instrumental-variable analysis, innovation-pathway analysis, city-size heterogeneity tests and spatial econometric modeling. Industrial robot exposure is positively associated with LCTE; the direction of this association is preserved across sensitivity analyses, although its magnitude falls to about one quarter of the baseline estimate under constant returns to scale. The coefficient patterns for green technological innovation and science and technology investment intensity are consistent with potential innovation-related pathways. The positive association is more pronounced in small and medium-sized cities than in major cities. Spatial Durbin model estimates confirm spatial dependence in LCTE and a robust positive direct association with local LCTE; the indirect association with spatially connected cities is negative in point estimates but not statistically significant once standard errors are clustered by city. These conditional associations suggest that the relationship between production automation and LCTE varies across innovation conditions, city sizes and spatial contexts.

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