识别冷链物流与绿色金融之间的耦合协调关系及其驱动因素:来自中国的证据

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL Stochastic Environmental Research and Risk Assessment Pub Date : 2024-09-06 DOI:10.1007/s00477-024-02811-2
Beifei Yuan, Fengming Tao, Hongfei Chen, Xinyi Zhu, Sha Lai, Yao Zhang
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引用次数: 0

摘要

实现冷链物流与绿色金融的协调共生,对于促进区域绿色可持续发展至关重要。然而,现有关于冷链物流与绿色金融耦合协调关系及其驱动因素的研究仍然有限,缺乏深入分析。本研究利用多源数据和基于最优参数的地理检测器,从计量、空间格局和驱动因素等角度对中国冷链物流与绿色金融耦合协调度(CCD)进行了描述。结果表明,中国的耦合协调度(CCD)总体呈波动上升趋势,区域差异明显。CCD 的空间分布呈现正相关性,以 H-H 和 L-L 聚类为特征。CCD 的空间格局是东部、南部地区高,西部、北部地区低,这种东西部差距逐渐缩小,南北部差距不断扩大。这种空间格局主要体现在基础设施、经济要素、人力资本、能源强度、技术要素和自然要素等方面。值得注意的是,人力资本、金融市场和数字智能技术之间的互动影响有助于进一步融合,单个因素的影响范围从 7.11% 到 632.79%。该研究为政策制定者和物流企业的可持续发展提供了有价值的启示,并为新兴国家提供了经验性的见解。
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Identifying the coupling coordination relationship between cold chain logistics and green finance and its driving factors: evidence from China

Achieving the coordination and symbiosis of cold chain logistics and green finance is notably critical for promoting regional green and sustainable development. However, The existing research on the coupling coordination relationship between cold chain logistics and green finance, as well as its driving factors, remains limited and lacks in-depth analysis. This study portrays the coupling coordination degree (CCD) from the perspectives of measurement, spatial patterns, and driving factors in China with multi-source data and the optimal parameters-based geographical detector. Results show that the CCD in China demonstrates an overall increasing trend of fluctuations, along with obvious regional differences. The spatial distribution of the CCD demonstrates a positive correlation, characterized by H-H and L-L clustering. The spatial pattern of the CCD is high in the eastern, southern regions and low in the western, northern regions, this gap is gradually narrowing between the east and west, south and north gap is widening. This spatial pattern is marked by infrastructure, economic factors, human capital, energy intensity, technological factors, and natural factors. Notably, the interactive impact among human capital, financial markets, and digital intelligence technology contributes to further integration, with the impact of individual factors ranging from 7.11 to 632.79%. It offers valuable implications for policymakers and logistics companies for sustainable development, and contributes empirical insights to emerging countries.

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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
期刊介绍: Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.
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