Decoding Green Innovation Through Multitier Carbon‐Adjusted Supply Chain Complexity

ABSTRACT This study examines whether incorporating carbon‐conscious elements into supplier–firm networks influences the green innovation performance of focal firms, grounded in adaptive theory. We construct a measure of supplier‐to‐firm supply chain complexity that captures horizontal, vertical, and geographic dimensions, explicitly integrating carbon emission considerations. Empirical results indicate that firms situated within more complex supply networks tend to exhibit higher levels of green innovation, and these relationships remain consistent after addressing potential endogeneity. Additional analyses suggest that a firm's strategic orientation and operational flexibility can enhance the positive effect of supply chain complexity, although the magnitude of this moderating role differs across the distinct dimensions. The evidence implies that managerial commitment to R&D and organizational adaptability can help convert the challenges posed by carbon‐intensive, complex supply chains into avenues for environmentally focused innovation and competitive positioning. By introducing a carbon‐adjusted, multidimensional perspective on supply chain complexity, this study offers a framework for understanding how structural and spatial characteristics of supply chains contribute to eco‐innovation outcomes.

Authors

Institutions

Publication Details

Journal
Business Strategy and the Environment
Published
2026-05-08
DOI
https://doi.org/10.1002/bse.70967
Primary Topic
Sustainable Supply Chain Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decoding Green Innovation Through Multitier Carbon‐Adjusted Supply Chain Complexity

Chuansong Zhao, Shuai Che, Miaomiao Tao
Business Strategy and the Environment
Sustainable Supply Chain Management
article

Decoding Green Innovation Through Multitier Carbon‐Adjusted Supply Chain Complexity

Chuansong Zhao, Shuai Che, Miaomiao Tao
article en

Abstract

ABSTRACT This study examines whether incorporating carbon‐conscious elements into supplier–firm networks influences the green innovation performance of focal firms, grounded in adaptive theory. We construct a measure of supplier‐to‐firm supply chain complexity that captures horizontal, vertical, and geographic dimensions, explicitly integrating carbon emission considerations. Empirical results indicate that firms situated within more complex supply networks tend to exhibit higher levels of green innovation, and these relationships remain consistent after addressing potential endogeneity. Additional analyses suggest that a firm's strategic orientation and operational flexibility can enhance the positive effect of supply chain complexity, although the magnitude of this moderating role differs across the distinct dimensions. The evidence implies that managerial commitment to R&D and organizational adaptability can help convert the challenges posed by carbon‐intensive, complex supply chains into avenues for environmentally focused innovation and competitive positioning. By introducing a carbon‐adjusted, multidimensional perspective on supply chain complexity, this study offers a framework for understanding how structural and spatial characteristics of supply chains contribute to eco‐innovation outcomes.

Business Strategy and the Environment
Zhengzhou University (CN), Zhengzhou Business University (CN), Shandong University of Finance and Economics (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 4%
Sustainable Supply Chain Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.