Exploring spatiotemporal distribution characteristics of air quality and driving factors: empirical evidence of 288 cities in China DOI
Qing Guo,

Hongrui Sun

Environmental Geochemistry and Health, Journal Year: 2024, Volume and Issue: 46(6)

Published: June 1, 2024

Language: Английский

Effect of digital economy on air pollution in China? New evidence from the “National Big Data Comprehensive Pilot Area” policy DOI

Yiren Zhang,

Congjing Ran

Economic Analysis and Policy, Journal Year: 2023, Volume and Issue: 79, P. 986 - 1004

Published: July 14, 2023

Language: Английский

Citations

67

How digital transformation facilitate synergy for pollution and carbon reduction: Evidence from China DOI
Bei Liu, Zhaoxuan Qiu, Letian Hu

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 251, P. 118639 - 118639

Published: March 18, 2024

Language: Английский

Citations

28

Artificial intelligence-driven transformations in low-carbon energy structure: Evidence from China DOI
Weiliang Tao, Shimei Weng, Xueli Chen

et al.

Energy Economics, Journal Year: 2024, Volume and Issue: 136, P. 107719 - 107719

Published: June 19, 2024

Language: Английский

Citations

19

Blue Sky Protection Campaign: Assessing the Role of Digital Technology in Reducing Air Pollution DOI Creative Commons
Yang Shen, Xiuwu Zhang

Systems, Journal Year: 2024, Volume and Issue: 12(2), P. 55 - 55

Published: Feb. 5, 2024

Air pollution severely threatens people’s health and sustainable economic development. In the era of digital economy, modern information technology is profoundly changing way governments govern, production mode enterprises, living behavior residents. Whether can bring ecological welfare needs to be further studied. Based on panel data from 269 Chinese cities 2006 2021, this study empirically examines impact air by using two-way fixed effect model. The results show that will significantly reduce concentration fine particles in help protect atmospheric environment. are still valid after interactive model two-stage least square method robustness test causality identification. Digital also promoting green innovation, improving energy efficiency, easing market segmentation. reducing heterogeneous. plays a more substantial role resource-based areas with high degree modernization commodity supply chain. positive affected amount pollutants emitted. When PM2.5 high, protecting atmosphere strongly highlighted. This research beneficial exploration environment while building an civilization society. conclusion urban managers, public, business operators entirely use equipment such as 5G, remote sensing, Internet Things their respective fields

Language: Английский

Citations

11

Assessing the Key Factors Measuring Regional Competitiveness DOI Open Access
Amalia Kouskoura, Eleni Kalliontzi, Dimitris Skalkos

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(6), P. 2574 - 2574

Published: March 21, 2024

Today’s competitive advantage is built through sustainability. Regional competitiveness undoubtedly one of the most important components for achieving sustainability development at local level. The analysis key factors and their correlations, aimed gauging regional competitiveness, yields valuable insights into multifaceted elements that impact growth advancement underprivileged regions. However, a crucial question remains: What precisely are form foundation assessing measuring competitiveness? literature review initially identified ten frequently mentioned competitiveness. overarching aim research understanding main determining extraction propositions based on those exploring relationship between various This study’s time frame was from August 2023 to January 2024. In this research, our undertake traditional review, concentrating context doing more critical rather than systematic review. We assess evaluate published spanning last five years (2018–2023); we have emphasized central widely span domains, including (1) economy, (2) labor market, (3) poverty social inclusion, (4) healthcare, (5) educational infrastructure, (6) environmental considerations, (7) transportation (8) science technology, (9) high-tech industries, (10) innovation. Our findings these reviewed indicate following. (a) economy factor should be expanded include education, parameters, while (b) there need address youth employment differences in market. (c) Collaborative, multidimensional approaches important, together with improving health infrastructures services, improve exclusion. (d) Investments education innovation required prosperity as informed policies collaborative actions greener, healthier, sustainable future, finally, (f) well-planned investments transportation, essential link R&D, innovation, economic progress, well additional industry innovative taken permanently Overall, highlights how economic, social, intertwine shape successful societies, forming fundamental underscores interconnectedness shaping prosperous providing foundational

Language: Английский

Citations

8

Exploration and future trends on spatial correlation of green innovation efficiency in strategic emerging industries under the digital economy: A social network analysis DOI
Xuemei Li, Yuchen Zhang, Shiwei Zhou

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 359, P. 121005 - 121005

Published: May 1, 2024

Language: Английский

Citations

8

How does the digital economy affect ecological well-being performance? Evidence from three major urban agglomerations in China DOI Creative Commons
Liu Yang,

Zhili Ma,

Yang Xu

et al.

Ecological Indicators, Journal Year: 2023, Volume and Issue: 157, P. 111261 - 111261

Published: Nov. 15, 2023

The booming digital economy (DIG) has deeply altered the pattern of people's lives and production plays a key role in increasing human well-being achieving urban sustainability. This study investigates relationship between DIG ecological performance (EWP) agglomerations Yangtze River Delta (YRD), Beijing-Tianjin-Hebei (BTH) Pearl (PRD) from 2010 to 2020. main results are as follows. Overall, can significantly enhance EWP. In terms spatial spillover effect, there is no effect BTH YRD agglomerations, negative PRD agglomerations. overall that promotes growth EWP region, while increase neighboring regions will hinder enhancement region. regional heterogeneity, size direct contribution "PRD > BTH". Finally, technological innovation industrial structure optimization mechanism variables. offers direction for improving

Language: Английский

Citations

17

How does digital economy empower pollution mitigation and carbon reduction? Evidence from Chinese cities DOI
Jun Zhao, Yuying Wang, Yalin Lei

et al.

Urban Climate, Journal Year: 2024, Volume and Issue: 55, P. 101946 - 101946

Published: May 1, 2024

Language: Английский

Citations

6

Economic digitalization and energy transition for green industrial development pathways DOI

Zhou Zou,

Munir Ahmad

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102323 - 102323

Published: Oct. 4, 2023

Language: Английский

Citations

16

A comparative analysis of the levels and drivers of regional coordinated development in the Yangtze River Economic Belt and Yellow River Basin, China DOI Creative Commons
Xiaolin Yang, Zengwei Feng, Yiyan Chen

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(5), P. e26513 - e26513

Published: Feb. 20, 2024

Positioned in the era of transformation China's primary social contradictions, this study delves into new connotations regional coordinated development(RCD) from perspective "factors" coordination within region and constructs an RCD evaluation system five subsystems economic coordination(REC), urban-rural coordination(URC), coordination(EASC), resource environmental coordination(RAEC), material spiritual civilization coordination(MASCC). Then, Entropy weight-TOPSIS model is used to evaluate levels 19 provinces located Yangtze River Economic Belt(YREB) Yellow Basin(YRB) 2010 2019, two-way fixed-effects employed illustrate driving mechanisms various influencing factors on YRB YREB. The results show that:(1)the YREB a fluctuating u trend during however, both regions have low levels, as seen by mean indices for YRB, which are only 0.433 0.309, respectively. (2) level higher than that YEB. In "coordinated" account 37.50% 81.82% total number basins, respectively, "uncoordinated" "low coordinated" all YRB. (3) significantly improved REC, URC RAEC, but not positively MASCC or EASC, insufficient development main contradiction limiting increase while EASC has become obstacle (4)Finally, based varying impact degrees directions different YREB, recommendations promote proposed.

Language: Английский

Citations

5