Unlocking sustainable production pathways: Digital transformation driving green dual innovation in Chinese enterprises

In the context of accelerating digital transformation and increasing environmental pressures, this study investigates how enterprise digitalization influences green dual innovation—comprising green exploratory innovation (EI) and green developmental innovation (DI)—within Chinese A-share listed firms from 2016 to 2023. Drawing upon fixed-effects and triple difference models, we empirically examine the mechanisms through which digital transformation (DCG) enhances green innovation and assess the mediating role of R&D investment and the moderating effect of government subsidies (GS). Results reveal that digital transformation significantly promotes both EI and DI, with a stronger effect on EI. R&D investment fully mediates this relationship, indicating that digital capabilities boost green innovation primarily through enhanced research capacity. Government subsidies exhibit a dual effect: they positively moderate the impact of digital transformation on EI, while dampening its influence on DI, possibly due to over-reliance on external funding. Heterogeneity analysis through triple difference methods uncovers substantial regional and industry-level variation—particularly among high-tech and heavily polluting firms in Eastern China—where digital transformation's green effects are constrained by strategic short-termism or technological limitations. These findings offer theoretical insights and practical implications for firms and policymakers aiming to align digital strategy with green transformation objectives under sustainability mandates. • Digital transformation (DCG) significantly boosts both green exploratory (EI) and developmental (DI) innovation, with a stronger effect on EI. • R&d investment is fully mediated in DCG and DI/EI, highlighting its key role in channeling digital capabilities. • Government subsidies (GS) positively moderate DCG's impact on EI but negatively moderate its effect on DI, revealing a dual policy effect. • Triple difference analysis reveals DCG’s green effects are constrained in Eastern China’s high-tech and heavy-polluting firms due to strategic short-termism or technological limitations. • The study provides evidence for optimizing digital-green synergy via tailored R&D strategies and differentiated subsidy policies.

Authors

Institutions

Publication Details

Journal
Cleaner Environmental Systems
Published
2025-07-20
DOI
https://doi.org/10.1016/j.cesys.2025.100301
Citations
7
Primary Topic
Economic and Technological Innovation
Type
article
Field-Weighted Citation Impact
21.92

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unlocking sustainable production pathways: Digital transformation driving green dual innovation in Chinese enterprises

Xudong Chen, Tian Xia
7 citations
Cleaner Environmental Systems
Economic and Technological Innovation
21.92
article

Unlocking sustainable production pathways: Digital transformation driving green dual innovation in Chinese enterprises

Xudong Chen, Tian Xia
article en
7 citations

Abstract

In the context of accelerating digital transformation and increasing environmental pressures, this study investigates how enterprise digitalization influences green dual innovation—comprising green exploratory innovation (EI) and green developmental innovation (DI)—within Chinese A-share listed firms from 2016 to 2023. Drawing upon fixed-effects and triple difference models, we empirically examine the mechanisms through which digital transformation (DCG) enhances green innovation and assess the mediating role of R&D investment and the moderating effect of government subsidies (GS). Results reveal that digital transformation significantly promotes both EI and DI, with a stronger effect on EI. R&D investment fully mediates this relationship, indicating that digital capabilities boost green innovation primarily through enhanced research capacity. Government subsidies exhibit a dual effect: they positively moderate the impact of digital transformation on EI, while dampening its influence on DI, possibly due to over-reliance on external funding. Heterogeneity analysis through triple difference methods uncovers substantial regional and industry-level variation—particularly among high-tech and heavily polluting firms in Eastern China—where digital transformation's green effects are constrained by strategic short-termism or technological limitations. These findings offer theoretical insights and practical implications for firms and policymakers aiming to align digital strategy with green transformation objectives under sustainability mandates. • Digital transformation (DCG) significantly boosts both green exploratory (EI) and developmental (DI) innovation, with a stronger effect on EI. • R&d investment is fully mediated in DCG and DI/EI, highlighting its key role in channeling digital capabilities. • Government subsidies (GS) positively moderate DCG's impact on EI but negatively moderate its effect on DI, revealing a dual policy effect. • Triple difference analysis reveals DCG’s green effects are constrained in Eastern China’s high-tech and heavy-polluting firms due to strategic short-termism or technological limitations. • The study provides evidence for optimizing digital-green synergy via tailored R&D strategies and differentiated subsidy policies.

Cleaner Environmental SystemsVol. 19
Chengdu University of Technology (CN), Chengdu University (CN)
Chengdu University of Technology
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Economic and Technological Innovation
21.92
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.