Can digital economy improve urban ecological development? evidence based on double machine learning analysis

The role of digital economy (DE) in improving urban ecological development (UED) has attracted scholarly attention. Additionally, traditional causal inference models encounter several challenges, such as model misspecification and the “curse of dimensionality.” In response to these problems, the double machine learning method is applied to assess the effect of DE on UED. Leveraging data from 282 Chinese cities in 2006–2021, several valuable conclusions emerge. First, DE improves UED and positively contributes to ecological resilience and recovery. Second, promoting green innovation, enhancing environmental efficiency, and optimizing industrial structures are the pathways through which DE contributes to UED. Third, the influence of DE on UED displays heterogeneity. Based on the results, this work proposes relevant recommendations grounded in empirical research.

Authors

Publication Details

Journal
Frontiers in Environmental Science
Published
2025-04-08
DOI
https://doi.org/10.3389/fenvs.2025.1542363
Citations
3
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
9.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Can digital economy improve urban ecological development? evidence based on double machine learning analysis

Xu Yue, Lei Li, Yuchen Jiang
3 citations
Frontiers in Environmental Science
Energy, Environment, Economic Growth
9.39
article

Can digital economy improve urban ecological development? evidence based on double machine learning analysis

Xu Yue, Lei Li, Yuchen Jiang
article en
3 citations

Abstract

The role of digital economy (DE) in improving urban ecological development (UED) has attracted scholarly attention. Additionally, traditional causal inference models encounter several challenges, such as model misspecification and the “curse of dimensionality.” In response to these problems, the double machine learning method is applied to assess the effect of DE on UED. Leveraging data from 282 Chinese cities in 2006–2021, several valuable conclusions emerge. First, DE improves UED and positively contributes to ecological resilience and recovery. Second, promoting green innovation, enhancing environmental efficiency, and optimizing industrial structures are the pathways through which DE contributes to UED. Third, the influence of DE on UED displays heterogeneity. Based on the results, this work proposes relevant recommendations grounded in empirical research.

Frontiers in Environmental ScienceVol. 13
Openalex Percentile: Top 3%
Energy, Environment, Economic Growth
9.39
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.