The Pearl River Delta (PRD) is one of the most dynamic economic regions in the Asia-Pacific region. At present, it still faces many problems, such as the over-exploitation of urban land and unbalanced development. Through the study of the spatial–temporal characteristics of the development intensity of the PRD region and its driving factors, the key points and difficulties of urban development can be intuitively found. In previous studies, geodetector was widely used to determine the impact of driving factors. This paper uses several different research methods, including the Moran index, the semi-variability index, hot and cold spots, etc., based on the land use data of the PRD region in 1990, 2000, 2010, and 2020 to analyze the spatial–temporal characteristics of the development intensity. Combined with the socio-economic data of the statistical yearbook, factor detection and interaction detection of the 10 driving factors of development intensity are carried out based on geodetector, and reasonable optimization suggestions are put forward for the current situation of the region. The main conclusions are as follows: (1) The overall development intensity of the PRD region shows an upward trend, showing a “core periphery” spatial pattern of high center and low periphery centered around the Pearl River estuary. (2) The spatial distribution of cold and hot spots shows agglomeration, mainly in the form of high aggregation and low aggregation. (3) The driving factors for the development intensity for the PRD region in the past 30 years mainly include population agglomeration level, industrial structure level, economic strength level, terrain slope, etc. Among them, any two factors have a stronger interaction than a single factor, and all are enhanced by two factors. The dominant factors of interaction in different periods are different.

Urbanization have been significantly reshaping the form of urban areas and natural landscapes, leading to complex urban morphologies. In 2012, the Local Climate Zone (LCZ) classification was proposed to address this issue and has since been widely adopted in urban climate studies globally. Despite its prevalence, literature on dynamic mapping of urban morphology remains sparse, making it difficult to delve into the study of urban renewal year by year. In this study, we compared different training scales, producing dynamic mappings of urban morphology with a spatial resolution of 100 meters spanning from 2000 to 2022 in major Chinese cities, based on the LCZ scheme. The results demonstrate strong inter-year consistency, and the accuracy of urban morphology change mapping is overall higher than 70%. Additionally, our results exhibit good alignment with other LCZ mapping datasets, more suitable for the current development situation in China, and effectively discriminate between building heights and densities across different LCZ types. This dataset holds significant potential for enhancing urban morphology monitoring and advancing urban climate research.

Urbanization has dramatically reshaped the urban landscape and intensified the urban heat island (UHI) effect in recent decades. This study investigates the spatiotemporal dynamics of UHI across 31 major Chinese cities from 2000 to 2020, using the local climate zone (LCZ) framework, Moderate Resolution Imaging Spectrometer-derived land surface temperature (LST), and machine learning models. Existing research has found significant differences in the level of UHI variation among different cities, but few studies attributing it from the perspective of urban morphology changes. We quantify urbanization processes through nine LCZ-derived indicators and assess their influence on daytime and nighttime UHI in summer. Results show that UHI intensity increased in 80.6% of cities during the day and in 77.4% at night, with over half experiencing statistically significant intensification (p < 0.05). Daytime UHI was stronger in southern cities (mean: 3.12 ^∘C), while nighttime UHI was more prominent in northern regions (mean: 1.86 ^∘C). Urban expansion (UE) and vegetation reduction were identified as the primary contributors to UHI intensification, with random forest and Shapley Additive exPlans analyses indicating that UE accounted for up to 28.3% of UHI variance. Functional transformation of urban areas showed mixed thermal effects, with stronger nighttime impact. High-rise (LCZ4) and mid-rise (LCZ5) building expansions were particularly linked to elevated LST. Moreover, three land use indices exhibited significant correlations with UHI trends, especially in cities with significant UHI growth. This study provides a comprehensive attribution analysis of UHI dynamics at the national scale, offering empirical evidence to guide sustainable urban planning and climate-adaptive city design.

Rapid urbanization exacerbates the Urban Heat Island (UHI) effect, posing challenges to the United Nations' Sustainable Development Goals amidst global warming. Attributing UHI impacts to urban morphology is increasingly crucial, yet few studies incorporate Local Climate Zone (LCZ) in this context, especially when combined with the interpretability of machine learning in diverse cities and building structures, leading to unclear rule and conclusion among them. This study integrates various dimensional indicators based on multi-source remote sensing data with Land Use and Land Cover (LULC) and employs an integrated machine learning model to predict UHI in Beijing and Tianjin, generalizing the model to Shijiazhuang. The SHapley Additive exPlanations (SHAP) method is used to analyze the influence of each feature at global, local, and LCZ scales. Results indicate that eXtreme gradient boosting (XGBoost) achieves the highest accuracy compared to other models, with its accuracy being improved by 10% or more; Compared with general land classification, LCZ-based classification has more than doubled the validation accuracy, smoothing sharp contrasts while preserving details. The factors affecting UHI in different cities and architectural form have partial consistency, with population growth and vegetation reduction being identified as major factors contributing to significant UHI increases. Our findings provide scientific references for understanding UHI drivers across multiple scales and regions and optimizing urban morphology to mitigate urban thermal environments.

Rapid urbanization has intensified urban surface thermal stress, yet how blue–green spaces (BGs) are associated with land surface temperature (LST) under different urban morphological contexts remains insufficiently understood. Using Suzhou, China, as a case study, this study integrates Landsat imagery from five representative years (2000, 2005, 2010, 2016, and 2022) with a 100 m local climate zone (LCZ) dataset to examine BGs–LST relationships over time. Two BGs indicators are considered: BGs proportion and the within-grid local dispersion of BGs, represented by BGs_std. The results show that LST in Suzhou’s built-up area exhibits a “rise–decline–rise” pattern during the study period, whereas BGs proportions evolve differently across LCZ types. Regression slope analysis shows that higher BGs proportion is generally associated with lower LST across most LCZ types and study years. Relatively stable negative associations are observed in LCZ 2, LCZ 3, LCZ 6, LCZ 9, and LCZ 10. Pearson correlation analysis further shows that BGs_std is generally positively associated with LST and that this relationship tends to strengthen over time. Relatively stronger associations are observed in LCZ 1, LCZ 3, LCZ 5, and LCZ 6 in some years. These findings suggest that BGs–LST relationships should be interpreted not only in terms of BGs proportion, but also in relation to urban form and within-unit BGs organization. This study provides an LCZ-based empirical perspective on BGs–LST associations in the context of a rapidly urbanizing city.