| 摘要: |
| 构建城市高质量社区空间对中国式
现代化城镇化发展具有重要创新意义。其中
经济、社会、环境等多维协同整合是重要的
内涵表达。然而传统社区更新研究常割裂经
济、社会、环境等维度，缺乏对动态协同机
制的研究。本文的核心研究问题是：基于高
质量系统模型（High-quality System Integration
Model，以下简称HSIM），构建了“要素识
别—测度分析—技术评价”多维指标项协同
技术框架，实现城市社区更新中经济、社会、
环境等多维度要素的动态协同。研究显示：
以天津滨海社区为例，在开发强度可控的条
件下，因子测度动态优化使规划方案系统综
合绩效提升19.6%，其中工程建造与生态环境
维度的贡献度分别增加21.3%与18.7%。关键
绩效指标呈现显著改善：区域整体碳排放强
度降低23.8%， 公共服务可达性平均提升
31.5%。说明该方案揭示了多维度协同更新的
动态机制，为城市社区更新提供了系统化的
理论工具与可操作的技术路径。 |
| 关键词: HSIM 技术优化测度 多维度协同
更新 天津滨海社区 |
| DOI：10.13791/j.cnki.hsfwest.20240929001 |
| 分类号: |
| 基金项目:国家自然科学基金面上项目（71874027）；上海市哲学社会科学规划“研究阐释党的二十届四中全会精神”专项项目（2026VQH011）；
上海市哲学社会科学规划课题一般项目（2025BCK008） |
|
| Multidimensional collaborative path of urban communities driven by technologicaloptimization from the perspective of HSIM: Taking Tianjin Binhai Community renewal asan example |
|
ZHOU Qi,GAO Changchun
|
| Abstract: |
| The construction of high-quality urban communities holds significant innovative
importance for the urbanization process aligned with China’s modernization goals. Among the core
dimensions of such development, the integration of economic, social, and environmental factors is
essential. Traditional community renewal studies often treat these dimensions in isolation, lacking a
systematic understanding of their dynamic synergy. This study addresses this gap by proposing a
multidimensional collaborative framework based on the High-quality System Integration
Model (HSIM). The framework follows a three-stage structure— “element identification,
measurement analysis, and technical evaluation”—to achieve the dynamic coordination of economic,
social, environmental, spatial, and technological dimensions in urban community renewal.
Theoretically, this study advances the existing literature by providing a global collaborative
framework that integrates five core dimensions: engineering construction, ecological environment,
spatial function, socio-economy, and energy low-carbon. It moves beyond static indicator systems by
incorporating dynamic weighting mechanisms and empirically examining how technological
optimization drives structural shifts in dimension priorities. Methodologically, the study employs
factor analysis and dynamic weighting based on panel data from the Binhai New Area in Tianjin. A
total of 20 secondary indicators were selected across the five dimensions, following international
standards such as the Dow Jones Sustainability Index (DJSI) and ESG frameworks, as well as
domestic guidelines. Data were collected from multiple sources, including planning documents,
statistical yearbooks, and spatial analyses using Python-based data extraction and spatial syntax
methods. Empirical analysis was conducted across ten functional zones in the Binhai community,
including residential areas, eco-parks, cultural tourism zones, industrial parks, and central business
districts. The KMO measure (0.521) and Bartlett’s test of sphericity (p < 0.001) confirmed the
suitability of the data for factor analysis. The first five principal components accounted for 97.487%
of the total variance, leading to the identification of five key technical factors: engineering
construction, ecological environment, spatial function, socio-economy, and energy low-carbon.
Factor scores were calculated for each functional zone, revealing significant variation in performance
across dimensions.The findings reveal several key insights. Firstly, the study demonstrates that under
controlled development intensity, dynamic optimization of factor measurements leads to a 19.6%
improvement in the comprehensive performance of the planning scheme. Notably, the contribution
weights of the engineering construction and ecological environment dimensions increased by 21.3%
and 18.7%, respectively, indicating a structural shift from growth-oriented to quality-oriented
renewal logic. Second, key performance indicators showed substantial improvements: regional
carbon emission intensity decreased by 23.8%, while average public service accessibility increasedby 31.5%. These results validate the effectiveness of the HSIM framework in enabling measurable and comparable outcomes. Third, the factor score rankings
across functional zones reveal differentiated synergy pathways. For instance, the Blue Economic Core Area ranked highest in comprehensive
performance (score: 1.203), excelling in engineering construction and socio-economic factors, while the China-Singapore Eco-Demonstration Base performed
best in ecological environment and low-carbon energy factors. In contrast, areas such as the Tanggu Leisure Experience Zone exhibited low scores across all
dimensions, highlighting the need for targeted interventions rather than one-size-fits-all strategies. The study makes several theoretical and practical
contributions. Theoretically, it advances urban renewal research from a static, dimension-listing approach to a dynamic “synergy dynamics” paradigm. By
quantifying the shifting weights of dimensions under technological optimization, the study provides an empirically grounded model of systemic transformation
in high-quality community development. Practically, the HSIM framework offers a replicable decision-support tool for planners. It enables the diagnosis of
synergy bottlenecks, the prioritization of dimension-specific interventions, and the simulation of performance outcomes under alternative design scenarios. In
the Binhai case, the final optimized planning scheme achieved a 23.8% reduction in carbon intensity and a 31.5% increase in service accessibility,
demonstrating the operational value of the framework.In conclusion, this study confirms that multidimensional synergy in urban community renewal is not a
static configuration but a dynamic process driven by technological optimization. The HSIM framework provides both a theoretical lens and a practical
methodology for understanding and guiding this process. Future research may extend the model to diverse geographic and institutional contexts, integrate it
with urban information modeling (CIM) and national spatial planning systems, and explore its potential for real-time simulation and adaptive governance in
smart city development. |
| Key words: HSIM technology optimization measurement multidimensional collaborative renewal Tianjin Binhai Community |
