Research on the Influence Mechanism of Factor Misallocation on the Transformation Efficiency of Resource-Based Cities Based on the Optimization Direction Function Calculation Method DOI Open Access
Runqun Yu,

Zhuoyang Luo

Sustainability, Journal Year: 2023, Volume and Issue: 15(12), P. 9800 - 9800

Published: June 19, 2023

Reasonable evaluation of the transformation efficiency resource-based cities can provide a reliable basis for correcting factor misallocation and optimizing allocation. This study improves directional distance function from aspects direction vector endogeneity, relative exogenous weight. Based on improved model, data China’s prefecture-level 2003 to 2018 are used measure compare non-resource-based cities. By setting different weights, considering total only energy obtained. Further comparative analysis shows that two efficiencies lower than those cities, keep same change trend. Whether it is city or city, level considers factors lower. Further, this decomposes according three dimensions finds efficiency, output environmental different, in conservation, economic growth friendliness also different.

Language: Английский

City-level environmental performance and the spatial structure of China's three coastal city clusters DOI Creative Commons
Dan Wu,

Yuying Lie,

Li Liu

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 422, P. 138591 - 138591

Published: Aug. 30, 2023

Cities' environmental efficiency (measured in terms of green total factor productivity, GTFP) have been found the existing literature to correlate spatially. However, spatial structure interrelations between cities' GTFP not yet determined. In this study, we attempted characterize city-level networks by investigating China's major three coastal city clusters: Jing-Jin-Ji (JJJ), Yangtze River Delta (YRD), and Pearl (PRD). We estimated 38 cities within these clusters applying a super-efficiency slack-based measure (SBM) data envelopment analysis (DEA) approach, constructed network for each cluster based on gravity model, traced out centralities subgroups conducting social analysis, then investigated socioeconomic factors correlating interrelation cities. The results are as follows: First, average increased from 2010 2021, did industrial value added regions. addition, most PRD region relative other two clusters. Second, centers exist These Beijing, Tianjin, Langfang JJJ region; Wuxi, Suzhou, Yangzhou YRD Guangzhou Foshan region. regions, composition containing with greater closeness underwent significant changes whereas subgroup change much. Third, differences distance key underlying that influence characteristics imply improving balance central noncentral alleviate misallocations economic resources could result an increase overall growth improvement.

Language: Английский

Citations

10

Green efficiency loss caused by economic growth goals: Evidence from an emerging economy DOI

Ying Zhai,

Wenzhi Wang, Liying Zhou

et al.

Economic Analysis and Policy, Journal Year: 2024, Volume and Issue: 81, P. 983 - 995

Published: Jan. 26, 2024

Language: Английский

Citations

3

Evolutionary characteristics and influencing mechanisms of green development efficiency in Chinese urban agglomerations: Analysis of the Yangtze River Delta urban agglomeration DOI
Yu Guo, Zihao Tong, Zhenbo Wang

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124236 - 124236

Published: Jan. 24, 2025

Language: Английский

Citations

0

Rural Financial Development, Agricultural Mechanization, and Total Factor Productivity DOI
Min Wang

Finance research letters, Journal Year: 2025, Volume and Issue: unknown, P. 107288 - 107288

Published: March 1, 2025

Language: Английский

Citations

0

How Does the Digital Innovation Ecosystem Enable Green Regional Development? A Dynamic QCA Study in China DOI Creative Commons

L H Li,

Yong Zeng, De Xia

et al.

Systems, Journal Year: 2024, Volume and Issue: 12(12), P. 551 - 551

Published: Dec. 11, 2024

The impact of digital empowerment on green innovation is increasingly evident, enabling various subjects to improve the integration elements and enhance efficacy across a broader temporal spatial scope. A comprehensive examination mechanisms that underlie this process required. This paper constructs ‘elements-subjects-environments’ research framework ecosystems, collecting data from 30 provinces in China 2017 2021 using total factor productivity (GTFP) evaluate level regional development. In study, dynamic qualitative comparative analysis (QCA) method employed analyze intricate causal configurations development are driven by ecosystems both perspectives. results show that: (1) requires interaction multiple elements, subjects, environment, single condition does not constitute necessary condition; (2) there four pathways with different for high-level development: elements-driven enterprise application innovation, enterprise-user co-creation, multi-collaborative environment-driven university basic innovation; (3) dimensions China’s heterogeneous: significance fostering increasing; configuration facilitating eastern central regions, whereas western northeastern regions progressing at relatively slow pace. study provides theoretical practical insights promote

Language: Английский

Citations

3

Spatial–temporal dynamics of land use carbon emissions and drivers in 20 urban agglomerations in China from 1990 to 2019 DOI

Xuefu Pu,

Qingping Cheng, Hongyue Chen

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(49), P. 107854 - 107877

Published: Sept. 23, 2023

Language: Английский

Citations

4

Analysis of Spatial-Temporal Evolution and Its Influencing Factors of Cities’ Green Economic Efficiency: A Case Study of Shandong Province, China DOI Open Access
Meixia Zhang,

Fang Li,

Ying Li

et al.

Polish Journal of Environmental Studies, Journal Year: 2024, Volume and Issue: 33(5), P. 5473 - 5483

Published: May 22, 2024

Based on panel data from 16 prefecture-level cities in Shandong Province 2011 to 2020, the paper utilizes super-efficiency SBM model with undesirable outputs measure green economic efficiency of each city.Spatial autocorrelation analysis and natural breaks method are applied analyze spatial-temporal evolution efficiency.Lastly, a Tobit is used factors affecting efficiency.The study's outcomes as outlined below: (1) The economy showed an overall increasing trend 2020.However, there noticeable disparity among cities, developed exhibiting relatively higher levels efficiency.(2) proportion high steadily increases, these highefficiency regions gradually cluster around provincial capital eastern coastal areas.While heterogeneous clustering efficiency, degree this heterogeneity decreases over time.(3) Social security, development, technological advancement significantly enhance whereas industrial structure noticeably impedes efficiency.Environmental regulations urbanization have less pronounced impact efficiency.Drawing findings, chapter presents targeted policy recommendations.

Language: Английский

Citations

1

How Does Regional Cooperation Affect Green Total Factor Productivity?—Evidence from the Guangdong-Hong Kong-Macao Greater Bay Area in China DOI Creative Commons

Peng Qiu,

Chenghui Tang,

Xiaofang Tu

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e38598 - e38598

Published: Sept. 27, 2024

As a key regional development strategy, cooperation affects the sustainable of city-regions. This study uses social network analysis, spatial and negative binomial regression analysis to investigate impacts on green total factor productivity (GTFP) in Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The main findings are as follows. First, is growing closer, unfolding trend networked polycentric patterns GBA. Second, GTFP most cities exhibits an upward trend, indicating continuous improvement economic Third, different types have heterogeneous GTFP, with being essential for GTFP. Overall, this provides systematic comprehensive environmental effects cooperation.

Language: Английский

Citations

1

Spatiotemporal coupling analysis between green total factor productivity and urban e-commerce development in China’s eight urban clusters DOI Creative Commons
Yue Liu, Xiaoming Qi, Yan Guo

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 1, 2024

Language: Английский

Citations

1

Urban sprawl and firm green total factor productivity: Evidence from China DOI
Mufang Xie,

Changbiao Zhong,

Binbin Yu

et al.

Papers of the Regional Science Association, Journal Year: 2024, Volume and Issue: 104(1), P. 100066 - 100066

Published: Dec. 4, 2024

Language: Английский

Citations

0