Wen Zhang

The spatial differentiation of street-block vitality and its built-environment correlates provide an empirical basis for implementing refined street-block governance in the context of the renewal of existing urban areas. Based on the human–environment relationship theory, this study constructs an analytical framework of “built environment condition–street-block vitality response” and establishes a built environment indicator system based on a five-dimensional (5D) framework encompassing density, diversity, design, destination accessibility, and distance to transit. Empirically, street-block vitality is measured by the block-level average heat value derived from Baidu Huiyan data, representing the relative intensity of population activity within each street block. By integrating objective data—such as point-of-interest (POI) data and road networks—with residents’ subjective perceptions, this study employs spatial autocorrelation analysis, hot spot analysis, and multiscale geographically weighted regression (MGWR) models to identify the spatial patterns of street-block vitality in Baoding during the observation week of 8–14 April 2024 and their local associations with built environment factors. Model performance was evaluated through model comparison, sensitivity analysis, and spatial holdout validation. The results indicated that street-block vitality exhibited significant spatial clustering. High–high clusters were primarily distributed in urban development zones and their adjacent areas in the central-eastern, northeastern, and southwestern parts of the city, whereas low–low clusters were mainly concentrated in ecological protection and ecological control zones in the western, northwestern, and northern parts of the city. The density of scenic facilities (Den_sce), facility-density principal component (PC_FAC), road network density (Den_road), and distance to the nearest public transit stop (Dist_transit) generally showed a positive association with street-block vitality, whereas satisfaction with public transit stop placement (Convenience_sat) showed a negative association. The direction of the association between satisfaction with traditional cityscape design (Style_sat) and street-block vitality varied across locations, and spatial differences were evident in the local coefficients, the spatial extent of statistical significance, and optimal bandwidths for each variable. The research findings provide empirical evidence for formulating differentiated street-block renewal strategies based on territorial spatial functional zoning, street-block vitality types, and local association patterns.

Identifying new drivers of urban green technology innovation is important for sustainable development. Drawing on Marx’s theory of productive forces, this study examines how data-element development relates to the quantity and quality of urban green technology innovation, using a balanced panel of 297 Chinese prefecture-level and above cities over 2011–2023, a city-level data-element development index based on the entropy weighting method, and two-way fixed-effects models. Data-element development is positively associated with both dimensions, and this association holds across a series of robustness checks. Mechanism analysis links data-element development to lower resource misallocation, which raises both dimensions, and to stronger university–industry research collaboration, with a relatively larger contribution to quality. Environmental regulation intensity reinforces the effect on both dimensions; the interaction with government internet-service responsiveness is significant over 2015–2023, the period for which the measure is consistently recorded, and is reported as supplementary evidence. Heterogeneity tests show larger effects in less-developed cities for both dimensions; across the Hu Huanyong Line and by population size, the between-group differences are significant for the quantity dimension. The results are consistent with the effect depending on whether a city has the complementary conditions in place that make data usable rather than on economic development level alone.

The building sector accounts for 40% of global energy consumption and 39% of carbon emissions, with climate change-induced extreme weather exacerbating energy demands and indoor habitability. Addressing this challenge is crucial for achieving the United Nations Sustainable Development Goals 11 and 13. However, existing research predominantly focuses on single-objective optimizations, lacking systematic multi-objective approaches. This study develops a synergistic optimization model integrating microclimate simulations (ENVI-MET and Grasshopper) with non-dominated sorting genetic algorithm II to simultaneously balance thermal comfort, energy consumption, and economic cost. A case study in Hebei Province, China—a temperate monsoon climate region with significant urbanization challenges—demonstrates compelling results: 2–3 °C indoor temperature reduction, 15%–20% energy savings, with only 5%–8% construction cost increase. This research provides a robust, replicable framework empowering architects and planners to optimize sustainable and climate-resilient residential building design.

Enhancing blue infrastructure is a critical pathway to strengthening urban water resilience and improving living environments. However, divergent perceptions and demands among multiple stakeholders may lead to misalignment between governance priorities and implementation pathways, thereby limiting governance effectiveness. Recognizing and addressing these differences has become essential for enhancing the performance of blue infrastructure governance and public satisfaction. Taking Shaanxi Province as a case study, this research systematically identifies core issues and disparities in public demands regarding water governance of blue infrastructure by analyzing governmental documents and public demands. The study aims to support a shift in governance strategy from a “provision-driven” to a “demand-driven” approach. A “topic identification–demand extraction–problem diagnosis” framework is adopted: first, the LDA model is used to analyze government platform texts and derive a macro-level thematic framework; subsequently, the BERTopic model is applied to mine public comments and identify micro-level demands; finally, the Jaccard similarity algorithm is employed to compare the two sets of topics, revealing the gap between policy provisions and public demands. The findings indicate the following: first, government agendas are highly concentrated on macro-level strategies (the topic “Integrated Water Ecosystem Management and Strategic Planning” accounts for 72.91% of weighting), whereas public appeals focus on specific, micro-level daily concerns such as infrastructure quality, drinking water safety, and drainage blockages; second, the Jaccard semantic correlation between the two is generally low (ranging from 6.05% to 14.62%), confirming a significant “topic-term overlap”; third, spatial analysis further reveals a geographical mismatch, particularly in core urban areas, which exhibit a “system-lag” type of misalignment characterized by high public demand but insufficient governmental attention. The research aims to clarify governance discrepancies, providing a basis for optimizing policy priorities and enabling targeted governance, while also offering insights for establishing a sustainable water resource management system.

Smart cities offer new opportunities for urban governance and sustainable development. However, at the current stage, the construction and development of smart cities generally exhibit a technology-driven tendency, neglecting real resident demand, which contradicts the “human-centric” principle. Traditional top-down methods of demand collection struggle to capture the dynamics and heterogeneity of public demand. At the same time, government service platforms, as one dimension of smart city construction, have accumulated massive amounts of user-generated data, providing new solutions for this challenge. This paper aims to construct a big data-driven analytical framework for dynamically identifying and accurately forecasting core resident demand. The study uses Xi’an City, Shaanxi Province, China, as a case study, utilising user messages from People.cn spanning 2011 to 2023. These messages cover various domains, including urban construction, healthcare, education, and transportation, as the data source. The People.cn message board is China’s most significant nationwide online political platform. Its institutionalised feedback mechanism ensures data content focuses on highly representative specific grievances, rather than the broad emotional expressions on social media. The study employs user messages from People.cn from 2011 to 2023 as its data source, encompassing urban construction, healthcare, education, and transportation. First, a large language model (LLM) was used to preprocess and clean the raw data. Subsequently, the BERTopic model was applied to identify ten core demand themes and construct their monthly time series, thereby overcoming the limitations of traditional methods in short-text semantic recognition. Finally, by integrating variational mode decomposition (VMD) with support vector machines (SVMs), a hybrid demand forecasting model was established to mitigate the risk of overfitting in deep learning when forecasting small-sample time series. The empirical results show that the proposed LLM-BERTopic-VMD-SVM framework exhibits excellent performance, with the goodness-of-fit (R2) on various demand themes ranging from 0.93 to 0.96. This study proposes an effective analytical framework for identifying and forecasting resident demand. It provides a decision-support tool for city managers to achieve proactive and fine-grained governance, thereby offering a viable empirical pathway to promote the transformation of smart cities from technology-centric to human-centric.