With the acceleration of urbanization, the ecosystem around cities is facing severe challenges. The drastic changes in the landscape pattern, especially in urban–suburban areas, are usually regarded as one of the main drivers. However, the spatiotemporal heterogeneous impacts of landscape patterns on the ecosystem services in this region remain unclear. To address this issue, we propose a novel framework integrating the InVEST-based ecosystem service assessment and spatiotemporal weighted regression (STWR)-based analysis of the spatiotemporal heterogeneity in urban–suburban areas, and apply it to the empirical study of Fuzhou City from 2000 to 2020. It first utilized the InVEST model to build a comprehensive ecosystem service index (CES) from five aspects (i.e., habitat quality, carbon storage, water yield, soil retention, and water purification capacity). Then, four landscape pattern indices (LPIs) (i.e., patch density (PD), area-weighted mean fractal dimension (FRAC_AM), splitting (SPLIT), and Shannon’s diversity (SHDI) index) were selected to build the STWR model. We compared and analyzed the differences in the spatial coefficient surfaces and significance tests generated by the STWR model in urban, urban–suburban, and rural areas. Results show that the following: (1) The CES in Fuzhou shows an upward trend from the urban area to the urban–suburban and rural areas, with significant gradient differences. (2) Compared with other areas, the LPIs in urban–suburban areas show more fragmentation, discreteness, and diversity, indicating more socioeconomic activities. (3) Although LPIs’ impacts on CES change over time (increasing from 2005 to 2010 and 2020 but decreasing in 2015), their effects are relatively low in urban–suburban areas, significantly lower than in urban areas. (4) Interestingly, the LPI coefficients near the urban–suburban boundary seem more significant. (5) This framework can effectively reveal the spatiotemporal heterogeneous relationships between various LPIs and CES, thus guiding concrete policies and measures that support decision-making for improving the ecosystem services surrounding cities through shaping landscape patterns.

With rapid urbanization, many cities have experienced significant changes in land use and land cover (LULC), triggered urban heat islands (UHI), and increased the health risks of citizens’ exposure to UHI. Some studies have recognized residents’ inequitable exposure to UHI intensity. However, few have discussed the spatiotemporal heterogeneity in environmental justice and countermeasures for mitigating the inequalities. This study proposed a novel framework that integrates the population-weighted exposure model for assessing adjusted thermal comfort exposure (TCEa) and the spatiotemporal weighted regression (STWR) model for analyzing countermeasures. This framework can facilitate capturing the spatiotemporal heterogeneities in the response of TCEa to three specified land-surface and built-environment parameters (i.e., enhanced vegetation index (EVI), normalized difference built-up index (NDBI), and modified normalized difference water index (MNDWI)). Using this framework, we conducted an empirical study in the urban area of Fuzhou, China. Results showed that high TCEa was mainly concentrated in locations with dense populations and industrial regions. Although the TCEa’s responses to various land-surface and built-environment parameters differed at locations over time, the TCEa illustrated overall negative correlations with EVI and MNDWI while positive correlations with NDBI. Many exciting spatial details can be detected from the generated coefficient surfaces: (1) The influences of NDBI on TCEa may be magnified, especially in rapidly urbanizing areas. Still, they diminish to some extent, which may be related to the reduction in building construction activities caused by the COVID-19 epidemic and the gradual improvement of urbanization. (2) The influences of EVI on TCEa decline, which may be correlated with the population increase. (3) Compared with NDBI, the MNDWI had more continuous and stable significant cooling effects on TCEa. Several mitigation strategies based on the spatiotemporal heterogeneous relationships also emanated. The effectiveness of the presented framework was verified. It can help analysts effectively evaluate local thermal comfort exposure inequality and prompt timely mitigation efforts.

Guangdong, Fujian, and Zhejiang (GFZ), located on China’s southeast coast, have long been economically active and rapidly growing provinces in China. However, the rising energy consumption in these provinces poses a major challenge to their carbon emissions reduction. Due to the spatial variation in the natural environment and socio-economic activities, energy carbon emissions (ECEs) and their reduction may vary among counties. The matter of scientifically formulating localized carbon reduction paths has therefore become a critical issue. This study proposed a novel path analysis framework based on exploring spatiotemporal heterogeneity using a spatiotemporal statistic model (i.e., spatiotemporal weighted regression). The path’s learning procedure was based on linking the changes in the amount of ECEs to the shifts in dominant factors, which were detected through local significance tests on the coefficients of STWR. To verify its effectiveness, we conducted a county-level empirical study considering four drivers (i.e., population (P), impervious surfaces (I), the proportion of secondary industry (manufacturing, M), and the proportion of tertiary industry (services, S)) in GFZ from 2014 to 2021. The ECEs show two different trends that may be affected by the COVID-19 pandemic and economic recession; hence, we divided them into two periods: an active period (2014–2018) and a stable period (2018–2021). Many interpretable paths and their occurrences were derived from our results, including the following: (1) P and S showed higher sensitivity to the changes in ECEs compared with I and M. Most counties (more than 50%) were dominated by P, but the dominator P may shift to I, M, and S during the active period. Many S-dominated counties reverted to being P-dominated ones during the stable period. (2) For the active period, the two most significant paths, M⁺ → S⁻ and M⁺ → P⁺ (+/− denotes positive or negative impacts of dominated driver), reduced ECEs by about 7.747 × 10⁵ tons and 3.145 × 10⁵ tons, respectively. Meanwhile, the worst path, S⁺ → P⁺, increased ECEs by nearly 1.186 × 10⁶ tons. (3) For the stable period, the best path (S⁺ → I⁺) significantly reduced ECEs by 1.122 × 10⁶ tons, while the worst two paths, M⁻ → P⁺ and I⁺ → P⁺, increased ECEs by 1.978 × 10⁶ tons and 4.107 ×10⁵ tons, respectively. These findings verify the effectiveness of our framework and further highlight the need for tailored, region-specific policies to achieve carbon reduction goals.