Construction and Comprehensive Evaluation of Green and Low-Carbon Consumption Indicator System in the Context of “Double Carbon” Goal
「ダブルカーボン」目標下におけるグリーン・低炭素消費指標体系の構築と包括的評価 (AI 翻訳)
Huihui Tian
🤖 gxceed AI 要約
日本語
本研究は、中国の「ダブルカーボン」目標の下で、グリーン・低炭素消費の指標体系を構築し、ファジィAHPと空間的自己相関分析を用いて評価した。技術革新と人材導入は正の空間スピルオーバー効果を示し、産業構造と都市化レベルは抑制要因となった。
English
This study constructs a green and low-carbon consumption indicator system under China's dual carbon goals, using fuzzy AHP and spatial autocorrelation analysis. It finds that technological innovation and talent introduction have positive spatial spillover effects, while industrial structure and urbanization restrain consumption upgrading.
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
中国の「ダブルカーボン」目標下での消費指標体系は、日本のGX政策における消費側評価の参考になる可能性がある。
In the global GX context
This study provides a methodology for constructing and evaluating green consumption indicators under national carbon goals, relevant for global policy design on demand-side decarbonization.
👥 読者別の含意
🔬研究者:Researchers can adopt the indicator system and spatial analysis methods for evaluating consumption-side decarbonization in other contexts.
🏛政策担当者:Policymakers can use the findings on technology and talent spillover to design regional policies for promoting green consumption.
📄 Abstract(原文)
Against the backdrop of the “dual carbon” goals, practicing green and low-carbon consumption has become a crucial component of national strategic objectives. However, the exploration of its implementation still faces a series of new challenges and issues.For the purpose of executing an unbiased and all-round quantitative evaluation of green low-carbon consumption, this research has constructed an index system that is prepared for environment-friendly low-carbon consumption. After that, this research uses the fuzzy analytic hierarchy process, which is called AHP, to build the weights of these indices.Furthermore, the spatial autocorrelation analysis is utilized by us to measure the spatial mutual connection of eco-friendly and low-carbon consumption, revealing its spatial distribution patterns and regional disparities. Spatial econometric models are then utilized to explore the factors influencing green and low-carbon consumption. The regression coefficients for technological innovation and talent introduction reached 0.015 and 0.162 respectively, demonstrating significant positive spatial spillover effects at the 1% confidence level. Conversely, the regression coefficients for industrial structure and urbanization level were both negative, tutting forward a restraining function for the promotion of environment-protective and low-carbon consumption degrees.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.65102/is2026582first seen 2026-05-17 05:29:22
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