摘要
文章运用地理加权随机森林(GWRF)与机器学习模型输出解释方法(SHAP),解析2011—2023年长三角地区城市数字经济与碳排放的时空耦合特征及影响因素。研究发现:1)城市群耦合协调度由0.411升至0.505,形成上海单核引领、苏浙跟进、安徽快增但临界失调的多级联动格局。2)城市层面耦合协调度呈现“中枢—走廊—边缘”的梯度扩散模式,与长三角一体化战略及基础设施互联的区域发展方向高度契合。3)结合SHAP结果发现,数字要素对数字经济与碳排放之间的耦合协调具有正向效应,且自核心城市向制造节点与新兴产业区扩散;与之相反,碳排放要素则抑制二者协同。研究表明,数字经济与人力资本集聚在产业升级和碳减排中具有核心驱动作用,为长三角优化资源配置、制定差异化低碳政策提供了科学依据,助力区域在数字经济与绿色竞争中实现高质量发展。
Against the strategic backdrop of"Digital-China"and the"Dual-Carbon"goals,the synergistic advancement of digital economy and carbon emission reduction is crucial for achieving high-quality,sustainable development.As a leading region in China's economic and digital transformation,the Yangtze River Delta(YRD)urban agglomeration provides a critical-case study for examining the complex interplay between digital growth and decarbonization.In this study,we aimed to systematically analyze the spatiotemporal-coupling characteristics and underlying influence mechanisms between the digital economy and carbon emissions in the YRD region from 2011 to 2023.Moving beyond aggregate-analysis and linear-assumptions,this study seeks to reveal the spatial heterogeneity,nonlinear-relationships,and threshold-effects to provide a nuanced empirical basis for differentiated-regional policymaking.Methodologically,we integrated the Geographically Weighted Random Forest(GWRF)model with SHapley Additive exPlanations(SHAP).We constructed comprehensive evaluation systems for both the digital economy and carbon emissions,and calculates the coupling coordination degree(D)between these two systems for 41 cities.The core analytical approach uses the GWRF model,which embeds a spatial-weight matrix into the Random Forest algorithm to simulate the spatially-varying and nonlinear effects of multiple influencing factors on the degree of coordination.Subsequently,the SHAP framework was applied to interpret the GWRF"black-box model and quantify the global-importance,directional-contribution,and potential nonlinear or threshold-behavior of each explanatory variable.This study yielded several key findings.Regarding temporal evolution,the overall coupling coordination degree of the YRD urban agglomeration shows a clear upward trend,increasing from 0.411 in 2011 to 0.505 in 2023,marking a transition from an"imminentimbalance"to a"barely-coordinated"stage.However,this progression is not monotonic;the significant dip observed in 2021 reflects dynamic tension and potential lagged-adaptation between technological-advancement cycles and stringent emission-reduction targets.In terms of spatial patterns,a distinct hierarchical"core-corridorperiphery"radial structure has formed.Shanghai,leveraging its advanced technological foundation and institutional advantages,remains at the forefront,achieving"high-quality coordination"by 2023.The provinces of Jiangsu and Zhejiang exhibit follow-up growth,entering the"barely-coordinated"stage.In contrast,Anhui province,despite exhibiting the fastest growth rate,remains at the threshold of"imminent-imbalance,"highlighting persistent regional disparities within the agglomeration.At the city level,high-coordination cores were concentrated along the Shanghai-Nanjing-Hefei-Hangzhou development axis,with coordination levels gradually diffusing along major transport corridors and weakening in northern Anhui and southwestern Zhejiang.Concerning the model validation and identification of key drivers,the GWRF model demonstrated significantly superior explanatory power and predictive accuracy compared to the standard-Random Forest model,confirming its efficacy in capturing spatial-non-stationarity.The SHAP analysis identified variables from the digital economy subsystem,specifically,the number of mobile phone subscribers,employees in information transmission and software services,and postal business volume,as important positive drivers.Their intensity-of-influence exhibited a spatial-diffusion pattern,radiating outward from core metropolitan areas to key manufacturing nodes and emerging industrial zones.Conversely,variables from the carbon emissions subsystem,particularly carbon emissions intensity and per-capita carbon emissions,act as primary inhibitors of coupling coordination.In summary,this study elucidates a dual-path mechanism,wherein the agglomeration of digital elements drives synergistic improvements,whereas high-carbon economic structures exert inhibitory pressure.This study makes substantive contributions to both the theoretical and methodological fronts.Theoretically,it provides robust empirical evidence for the complex,nonlinear-interdependencies between digital and green transitions,challenging simplistic linear-assumptions and enriching the understanding of their coupling dynamics in a regional context.Methodologically,the integrated GWRF-SHAP framework was validated as a powerful tool for dissecting high-dimensional and spatially-heterogeneous problems in urban and regional studies,offering a replicable-analytical pathway.These findings provide actionable-insights for policymakers to advocate tailoredstrategies that reinforce positive digital diffusion,especially in lagging areas,while implementing targeted measures to decouple economic growth from carbon emissions in high-pressure zones.Ultimately,this approach aims to foster a more balanced and synergistic development pathway for the YRD and similar regions.
作者
张嵌玮
席广亮
Zhang Qianwei;Xi Guangliang(School of Architecture and Urban Planning,Nanjing University,Nanjing 210093,China;Jiangsu Smart City Planning and Digital Governance Engineering Research Center,Nanjing 210093,China)
出处
《热带地理》
北大核心
2026年第1期110-128,共19页
Tropical Geography
基金
国家自然科学基金项目(42471245)。
关键词
地理加权随机森林(GWRF)
SHAP
数字经济
碳排放
耦合协调
长三角城市群
Geographically Weighted Random Forest(GWRF)
SHAP
digital economy
carbon emission
coupled coordination
Yangtze River Delta Urban Agglomerations
