| 摘要: |
| “双碳”背景下，以徐州市
县域为例，研究资源转型城市工业用
地集聚度与碳排放的关系。通过景观
格局分析与因子系数法测度工业用地
集聚度与企业碳排放量，并运用多因
子回归模型分析两者关联。结果表
明：工业用地集聚度与碳排放呈倒U
型关系，目前处于正向促进阶段，企
业密度与用地规模越大，碳排放越
高， 拐点分别为0.0424 与556.048
hm2；道路密度、联通性与碳排放显
著负相关，提升连通性有助于减碳；
工业用地边缘复杂度与碳排放正相
关，斑块连通性则负相关，但集聚度
尚未跨越规模门槛。建议推进园区整
合与分类管控，优化工业用地形态与
连通性，以降低碳排放。 |
| 关键词: 资源转型城市县域 用地集
聚度 碳排放 相关性 |
| DOI：10.13791/j.cnki.hsfwest.20240904002 |
| 分类号: |
| 基金项目:中央高校基本科研业务费专项资金资助（2024QN11085）；江苏高校哲学社会科学项目（2025SJYB0789）；江苏省社科应用研究精品工程社会教育（社科普
及）专项（25SJC-13） |
|
| Study on the correlation between industrial land agglomeration and carbon emission incounty of resource transition Cities: Taking Xuzhou County as an example |
|
SONG Limei,CHANG Jiang,ZHU Wenlong
|
| Abstract: |
| Against the backdrop of the dual carbon goals, clarifying the extent to which industrial
agglomeration in county-level areas of resource-transition cities impacts carbon emissions, and exploring
pathways for adjusting low-carbon spatial patterns of industrial land use alongside emission reduction
strategies in this new phase, constitutes a critically important proposition of profound practical significance.
As a typical resource-transition city, Xuzhou exhibits a high proportion of carbon emissions attributable to
energy consumption in production. Moreover, since 2017, carbon emissions from its county-level areas have
progressively surpassed those of its urban districts. The escalating carbon emissions from county-level
industries warrant urgent attention. This study examines general industrial sectors within county areas,
employing the ‘industrial agglomeration intensity – spatial form – carbon emissions’ causal framework. An
evaluation system was developed to characterize county-level industrial agglomeration intensity across three
dimensions: infrastructure support, morphological regularity, and spatial layout concentration. Landscape
pattern analysis and factor coefficient methods were employed to measure industrial land agglomeration and
corporate carbon emissions respectively within Xuzhou’s counties. A multiple regression model was then
applied to analyze their correlation. Findings indicate: 1) Industrial land in Xuzhou’s counties exhibits relative
agglomeration, yet industrial parks are dispersed, resulting in low overall corporate concentration. Industrial
land layout correlates with industrial park establishment timing and enterprise type. The spatial pattern of
industrial carbon emissions in county-level areas largely corresponds with the distribution density of
conventional industrial enterprises, and carbon emission intensity on industrial land is directly related to
industrial type. Xuzhou’s county-level areas remain in the transitional phase from mid- to late-stage
industrialization, with a ‘small-scale, dispersed’ industrial layout pattern still dominant. Conventional
industrial enterprises in these areas are predominantly in a development stage characterized by ‘scale
expansion as the primary driver, supplemented by technological upgrading’, with no significant technologydriven
differentiation observed. 2) Presently, the number of enterprises, enterprise density, industrial electricity
consumption area, and the quantity of industrial land landscape patches exhibit a high correlation with
enterprise carbon emissions. Conversely, indicators of overall industrial land morphology, connectivity, and
dispersion show relatively low correlation with industrial carbon emissions. This indicates that for industrial
land in Xuzhou’s counties, the impact of spatial layout concentration on enterprise carbon emissions has not
yet crossed the scale threshold; 3) The relationship between industrial factor concentration and carbon
emissions in Xuzhou’s counties exhibits an inverted U-shaped curve. Currently, industrial factor concentration
positively promotes carbon emissions, meaning that larger industrial land areas and higher enterprise densities
correlate with greater carbon emissions from industrial enterprises. The inflection point for enterprise density
is 0.042 4, with a confidence interval of [0.038, 0.047], while the inflection point for industrial land scale is
556.048 hectares, with a confidence interval of [520.3, 591.8] hectares. Given that only 8% of townships have
reached peak industrial land area and 5% have achieved peak enterprise density, most townships remain belowthe scale threshold. Xuzhou’s county-level industrial carbon emissions will require considerable time to surpass the scale effect and achieve carbon peaking,
indicating substantial growth potential for industrial enterprise scale. 4) A certain correlation exists between the spatial agglomeration intensity of industrial
land use and carbon emissions in Xuzhou’s counties. However, relative to industrial type and scale, spatial concentration exerts a comparatively minor
influence on carbon emissions. Specifically, the complexity of industrial land edge morphology exhibits a clear positive correlation with carbon emissions,
while the connectivity of industrial land patches shows a negative correlation. 5) Addressing the contradiction of ‘overall concentration but internal dispersion’
in county-level industrial land requires optimization centered on morphological regularization and layout intensification, advancing park consolidation and
categorized management. Under the ‘strong industry’ development strategy, counties must first control the disorderly expansion of industrial land scale to meet
industrial growth demands, then focus on ‘incremental clustering’ of industrial enterprises, guiding land use towards regularized and compact layouts.
Addressing the current realities of large-scale industrial parks in Xuzhou’s counties with low floor area ratios and insufficient land use intensity, spatial form
optimization of industrial parks should be undertaken to enhance connectivity between industrial land patches. |
| Key words: resource transition city counties land use agglomeration carbon emissions correlation |
