Unleashing the Low-Carbon Potential of the Digital Economy: Research on the Configuration Path of High Carbon Productivity
デジタルエコノミーの低炭素ポテンシャルを引き出す：高炭素生産性の構成経路に関する研究 (AI 翻訳)
Chunyu Bai, Wenwen Wang, Ming Zhang
🤖 gxceed AI 要約
日本語
本研究は、デジタル経済が炭素生産性に与える影響を中国的文脈で分析。GMDHアルゴリズムで重要な変数を特定し、fsQCAで高炭素生産性に至る3つの構成経路を発見。地域ごとに異なる発展モデルを提示し、政策提言を行う。
English
This study analyzes how the digital economy affects carbon productivity in a Chinese context. It uses the GMDH algorithm to identify key variables and fsQCA to reveal three configurational pathways leading to high carbon productivity. It proposes different development models for various regions and offers policy recommendations.
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
日本のGX政策においても、デジタル技術を活用した炭素生産性向上は重要なテーマ。本論文の地域別モデルは、日本の都道府県レベルの政策立案にも示唆を与える可能性がある。
In the global GX context
This paper contributes to the global literature on the digital economy's role in decarbonization. The configurational approach and regional typology offer a nuanced understanding that can inform climate policy in other countries.
👥 読者別の含意
🔬研究者:Provides a novel methodological combination (GMDH+fsQCA) to study carbon productivity determinants in the digital economy era.
🏢実務担当者:Highlights which digital economy factors (e.g., robot penetration, digital finance usage) are linked to higher carbon productivity, guiding corporate strategy.
🏛政策担当者:Offers region-specific policy pathways for leveraging digitalization to achieve carbon productivity gains, directly applicable for regional climate planning.
📄 Abstract（原文）
The digital economy (DE) is increasingly associated with higher carbon productivity (CP) and is widely regarded as an important factor in efforts to achieve the dual-carbon goals. However, the formulation of differentiated policies is constrained by a limited understanding of the multi-factor collaborative mechanisms and their asymmetric configurational pathways. This study combines the GMDH algorithm with the fsQCA approach to explore the multiple sufficient paths for high carbon productivity. Through feature selection and nonlinear modeling, the GMDH algorithm identifies five key variables associated with CP: the industrial robot permeability, software business development, digital innovation input, the usage depth of digital finance, and mobile communication facilities. The fsQCA method reveals that three configurational pathways consistent with higher levels of CP: the “innovation and finance-driven model” represented by Sichuan and Hunan, the “innovation-assisted digital industrialization model” represented by Henan and Hebei, and the “industry digitalization first developing model” represented by Jiangxi, Guangdong, Zhejiang, and Shanghai. Considering the uneven regional development across China, this study further categorizes provinces into four regional development types: innovation and finance-driven, digital industry empowerment, industrial digitalization leadership, and potential cultivation. Correspondingly, tailored policy recommendations are proposed for each region, providing practical insights consistent with the observed configurational patterns for improving CP in the context of DE development.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18104988first seen 2026-05-21 04:45:07
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