Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin

The coordinated development of the economy and low-carbon transportation in ecologically sensitive areas has always been a persistent challenge for urban development. Optimizing the low-carbon transportation system is critical for achieving high-quality economic development in a typical ecologically...

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Main Authors: Wang Wei, Mo Yuan-rui, Guan Lei, Gong Liang
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Ecological Indicators
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Online Access:http://www.sciencedirect.com/science/article/pii/S1470160X25004510
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author Wang Wei
Mo Yuan-rui
Guan Lei
Gong Liang
author_facet Wang Wei
Mo Yuan-rui
Guan Lei
Gong Liang
author_sort Wang Wei
collection DOAJ
description The coordinated development of the economy and low-carbon transportation in ecologically sensitive areas has always been a persistent challenge for urban development. Optimizing the low-carbon transportation system is critical for achieving high-quality economic development in a typical ecologically sensitive region, the backward U-shaped bend metropolitan area (BUBMA) of the Yellow River Basin. With the acceleration of China’s Dual Carbon targets, understanding the interactive relationship between low-carbon transportation (LCT) and high-quality economic development (HQED) is an essential prerequisite for advancing ecological protection and implementing high-quality development strategies. This study explores the coupling coordination problem and its prediction and regulatory strategies during the spatiotemporal evolution of LCT and HQED in the BUBMA by employing methods such as the Coupling Coordination Degree (CCD), Monte Carlo simulation (MC), and Grey prediction.The findings reveal the following: (1) The comprehensive indices of 15 cities exhibit significant disparities during the entire research period, characterized by high values in the central region and low values at both ends for LCT, while HQED demonstrates a transition from an evident “olive-shaped” spatial pattern to one marked by high values in the south and low values in the north. (2) The proposed CCD prediction framework can offer an objective reflection of future trends. The CCDs of these 15 cities have shown a growth trend, with the average CCD projected to reach the stage of barely coordinated development(0.554)by 2030. (3) Four key indicators,which include the share of non-fossil energy consumption, highway network density, air passenger traffic per 10,000 people and high-speed rail mileage per 10,000 people, have significant positive driving effects on the development of LCT. The comprehensive regulatory strategies for these influencing factors are proposed based on forecast and target scenarios. The empirical findings provide important insights for optimizing the CCD and its regulation strategies of these two systems, thus providing guidance for formulating coordinated development policies and sustainable governance strategies.
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spelling doaj-art-634135a1ad5d430dae8f4ffcbc6363c22025-08-20T02:29:20ZengElsevierEcological Indicators1470-160X2025-06-0117511352110.1016/j.ecolind.2025.113521Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River BasinWang Wei0Mo Yuan-rui1Guan Lei2Gong Liang3School of Business, North Minzu University, Yinchuan 750021 Ningxia, China; Corresponding author.School of Business, North Minzu University, Yinchuan 750021 Ningxia, ChinaSchool of Business, North Minzu University, Yinchuan 750021 Ningxia, ChinaSchool of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070 Gansu, ChinaThe coordinated development of the economy and low-carbon transportation in ecologically sensitive areas has always been a persistent challenge for urban development. Optimizing the low-carbon transportation system is critical for achieving high-quality economic development in a typical ecologically sensitive region, the backward U-shaped bend metropolitan area (BUBMA) of the Yellow River Basin. With the acceleration of China’s Dual Carbon targets, understanding the interactive relationship between low-carbon transportation (LCT) and high-quality economic development (HQED) is an essential prerequisite for advancing ecological protection and implementing high-quality development strategies. This study explores the coupling coordination problem and its prediction and regulatory strategies during the spatiotemporal evolution of LCT and HQED in the BUBMA by employing methods such as the Coupling Coordination Degree (CCD), Monte Carlo simulation (MC), and Grey prediction.The findings reveal the following: (1) The comprehensive indices of 15 cities exhibit significant disparities during the entire research period, characterized by high values in the central region and low values at both ends for LCT, while HQED demonstrates a transition from an evident “olive-shaped” spatial pattern to one marked by high values in the south and low values in the north. (2) The proposed CCD prediction framework can offer an objective reflection of future trends. The CCDs of these 15 cities have shown a growth trend, with the average CCD projected to reach the stage of barely coordinated development(0.554)by 2030. (3) Four key indicators,which include the share of non-fossil energy consumption, highway network density, air passenger traffic per 10,000 people and high-speed rail mileage per 10,000 people, have significant positive driving effects on the development of LCT. The comprehensive regulatory strategies for these influencing factors are proposed based on forecast and target scenarios. The empirical findings provide important insights for optimizing the CCD and its regulation strategies of these two systems, thus providing guidance for formulating coordinated development policies and sustainable governance strategies.http://www.sciencedirect.com/science/article/pii/S1470160X25004510BUBMALow-carbon transportationHigh-quality economic developmentCoupling coordinationMonte Carlo simulationDriving factors
spellingShingle Wang Wei
Mo Yuan-rui
Guan Lei
Gong Liang
Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin
Ecological Indicators
BUBMA
Low-carbon transportation
High-quality economic development
Coupling coordination
Monte Carlo simulation
Driving factors
title Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin
title_full Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin
title_fullStr Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin
title_full_unstemmed Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin
title_short Prediction analysis and control strategies on coupling coordination between low-carbon transportation and high-quality economic development in the backward U-shaped bend metropolitan area of the Yellow River Basin
title_sort prediction analysis and control strategies on coupling coordination between low carbon transportation and high quality economic development in the backward u shaped bend metropolitan area of the yellow river basin
topic BUBMA
Low-carbon transportation
High-quality economic development
Coupling coordination
Monte Carlo simulation
Driving factors
url http://www.sciencedirect.com/science/article/pii/S1470160X25004510
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