| 摘要: |
| 在“双碳”目标驱动下，本研究构建碳
排放福利绩效（WPCE）模型，建立碳排放与国
土空间综合福利的关联分析框架。基于江苏省64
个县域单元，运用空间自相关分析和三维矩阵模
型，揭示碳排放福利绩效空间分异特征并划定低
碳竞争力类型。主要结论显示：①WPCE呈现显
著区域差异，苏南（29.12万元/吨）、苏中（8.44
万元/吨）、苏北（6.53 万元/吨）形成梯度分布，
苏南以22.96%碳排放贡献55.01%福利效益体
现“领跑优势”；②空间结构呈“南高北低-双核
集聚”特征，高值集聚区（H-H）形成南京（浦
口—江宁）和苏州—无锡（张家港—江阴）两
大核心，低值集聚区（L-L）集中于淮安、宿迁
等苏北地区；③低碳竞争力划分为四类：高竞
争型（11 个）、机遇型（5个）、挑战型（27个）、
低竞争型（21个），其中挑战型占比42.18%成为
主导类型。研究成果为国土空间低碳治理提供
“绩效评估—空间识别—分类调控”的系统性方法
支撑，对实现碳排放约束下的国土空间优化具有
实践指导价值。 |
| 关键词: 碳排放福利绩效 生态福利绩效 碳达峰碳中和 生态系统服务价值 江
苏省 |
| DOI：10.13791/j.cnki.hsfwest.20240430002 |
| 分类号: |
| 基金项目:国家自然科学基金项目（51908390，42301205）；江苏省研究生科研与实践创新计划项目（SJCX23_1697）；江苏高校哲学社会科学研究项目（2024SJYB1038，
2023SJYB1433）；江苏高校优势学科（城乡规划学）建设工程项目资助；国家一流专业（城乡规划）建设项目资助；江苏省“双创计划”项目资助 |
|
| Construction of well-being performance of carbon emissions model and its low-carboncompetitiveness evaluation under the background of territorial spatial planning: A casestudy of Jiangsu Province |
|
LIU Yushu,LIN Bing,WANG Zhenyu,ZHU Lika,LU Jiancheng
|
| Abstract: |
| Under the goals of carbon peaking and carbon neutrality (“dual carbon”), achieving
greater socio-economic well-being benefits within limited carbon emission constraints has
emerged as a critical challenge for low-carbon development in territorial spatial planning. This
study addresses this challenge by integrating the theoretical framework of “ecological well-being
performance” with ecosystem service value measurement to construct a carbon emission wellbeing
performance (WPCE) model, which establishes an analytical linkage between carbon
emissions and the comprehensive well-being level of territorial spaces. Focusing on 64 countylevel
units in Jiangsu Province—a region characterized by pronounced economic disparities and
ecological diversity—the research employs spatial autocorrelation analysis and a threedimensional
matrix model to explore the spatial differentiation characteristics of WPCE and
classify low-carbon competitiveness types, thereby providing actionable insights for optimizing
territorial spatial planning and advancing dual carbon goals. The findings reveal significant
structural disparities in WPCE across the province, with Southern Jiangsu (Sunan) demonstrating
a “leading” performance by generating 55.01% of the total well-being benefits while accounting
for only 22.96% of provincial carbon emissions, translating to WPCE values of 291 200 yuan/ton,
which starkly contrasts with central Jiangsu (suzhong) and northern Jiangsu (subei), where WPCE
values drop to 84 400 yuan/ton and 65 300 yuan/ton, respectively. These regional imbalances are
further compounded by distinct spatial clustering patterns, as WPCE exhibits a “south-high, northlow”
distribution with dual-core agglomerations: high-high (H-H) clusters concentrate in the
Pukou District-Jiangning District corridor of Nanjing and the Zhangjiagang-Jiangyin industrial
belt spanning Suzhou and Wuxi, forming two innovation-driven cores characterized by advanced
manufacturing and service-oriented economies, while low-low (L-L) clusters dominate Huai’an,
Suqian, and surrounding areas in subei, reflecting structural challenges in ecological-economic
efficiency due to reliance on energy-intensive industries and fragmented land-use planning. To
systematically address these disparities, the study introduces a low-carbon competitivenesstypology based on the "emission-performance-well-being" nexus, classifying counties into four categories: High-competitive (Class I, 11 units) such as Pukou
and Kunshan, which exemplify low emissions, high WPCE, and high well-being through integrated green infrastructure and circular economy practices;
Opportunistic (Class II, five units) like Jiangning and Jingjiang, which exhibit mixed traits with transitional potential due to proximity to innovation hubs but
face institutional barriers to decarbonization; Challenged (Class III, 27 units), representing 42.18% of counties, including Xinyi and Suyu, where high
emissions and low performance stem from outdated industrial structures and weak environmental governance; and Low-competitive (Class IV, 21 units),
prevalent in Subei (e. g., Suining and Xiangshui), marked by multi-dimensional deficiencies in technology, policy coherence, and public participation.
Methodologically, the study advances existing approaches by integrating ecosystem service valuation—quantified through equivalence factor and functional
value methods—with spatial econometrics, enabling a granular assessment of county-level welfare-carbon dynamics that captures both biophysical and socioeconomic
dimensions. The three-dimensional matrix model further decouples emission efficiency, welfare output, and spatial competitiveness, offering a novel
framework for diagnosing systemic bottlenecks and prioritizing interventions. For instance, the model identifies that Subei’s low WPCE is driven not only by
inefficient energy use but also by undervalued ecosystem services, such as carbon sequestration in wetlands and agricultural lands, which remain excluded from
regional accounting systems. Policy implications emphasize the urgency of cross-regional synergy, leveraging sunan’s technological and financial expertise to
foster green infrastructure investment and knowledge transfer in suzong and subei, particularly in sectors like renewable energy integration and smart
agriculture. Additionally, functional zoning strategies are proposed to designate innovation hubs in H-H clusters for piloting carbon-neutral industrial parks
while establishing ecological restoration zones in L-L areas to rehabilitate degraded ecosystems and monetize their service value. The study also advocates for
smart spatial governance through digital tools, such as geospatial AI platforms, to enable real-time monitoring of emissions and dynamic resource allocation,
thereby enhancing adaptive management. By operationalizing the dual carbon agenda into measurable territorial strategies, this research contributes to the
global discourse on sustainable spatial planning, with Jiangsu’s empirical insights offering a replicable framework for regions undergoing similar transitions.
The integration of ecosystem service valuation with low-carbon competitiveness metrics not only enriches the theoretical understanding of ecological wellbeing
performance but also provides policymakers with a data-driven roadmap to reconcile economic growth with emission reduction targets, ultimately
fostering inclusive and resilient development pathways in the era of climate urgency. |
| Key words: well being performance of carbon emissions ecological well-being performance carbon peak carbon neutralization ecosystem service value Jiangsu Province |
