Can digital finance promote inclusive growth to meet sustainable development in China? A machine learning approach
Chunhua Xin,
Shuangshuang Fan and
Zihao Guo ()
Additional contact information
Chunhua Xin: China University of Mining and Technology-Beijing
Shuangshuang Fan: China University of Mining and Technology-Beijing
Zihao Guo: Shandong University
Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 10, No 85, 26647-26677
Abstract:
Abstract The global spread of the COVID-19 epidemic has caused increasingly grievous issues such as poverty, inequality and economic recession, which has hindered the realization of inclusive growth (IG) and disrupted the sustainable development trajectory. Meanwhile, with the vigorous development of digital finance (DF) based on advanced digital technologies such as big data, the Internet of things and artificial intelligence, new vitality has been injected into China’s growth model. Thus, whether DF could affect IG and to what extent has drawn attention from scholars to policymakers. This study examines whether DF significantly contributes to IG using the XGBoost machine learning (ML) algorithm for the first time. Using a panel of 281 prefecture-level cities from 2005 to 2020, we employ the Entropy-VIKOR model to assess cities’ inclusive growth index and reveal the spatial–temporal evolution and regional differences characteristics. We find that DF plays an indispensable role in promoting urban IG and influences the three sub-dimensions of IG: economic growth, opportunity equity and achievement sharing. The heterogeneous analyses based on geographic location and population size show that digital finance plays a more significant role in promoting inclusive growth of cities in central and western China than cities in eastern China; however, cities with different population sizes have little difference. Our findings using ML algorithms are robust to using traditional econometric models. This study sheds light on how DF could help achieve the IG in developing countries similar to China.
Keywords: Digital finance; Inclusive growth; XGBoost; Machine learning; Influencing factors (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10668-023-03748-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:26:y:2024:i:10:d:10.1007_s10668-023-03748-2
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668
DOI: 10.1007/s10668-023-03748-2
Access Statistics for this article
Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens
More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().