Combating climate change by increasing urban carbon storage is one of the critical issues which urban policymakers must address. Understanding the characteristics and driving factors of carbon storage changes during urbanization can assist urban managers in formulating responsive land use policies. This study employs the INVEST model to evaluate carbon storage in Shanghai from 2000 to 2020, analyzing land use changes and their carbon impacts. It analyzes the transformation of land use in Shanghai during the same period and its impact on carbon storage. Using a 1 km grid for sampling, this study examines the spatiotemporal distribution patterns of carbon storage in Shanghai. Furthermore, it employs linear regression to discuss the social and economic drivers influencing carbon storage in the city. Carbon storage in Shanghai, predominantly from cultivated land and artificial surfaces, increased from 16.78 Mt in 2000 to 18.40 Mt in 2020, with an annual rise of 0.81 Mt. The spatial distribution of carbon storage exhibited a stable southeast-northwest pattern, with variations in dispersion between the north-south and east-west directions. The distribution of carbon storage shifted from a bimodal to a unimodal pattern, indicating an overall increase. There was a significant positive correlation between carbon storage and both the per capita green space area and the industrial output value, which can be attributed to Shanghai’s policies on green industrial development. This research aids in formulating land use policies to enhance urban carbon storage.

Under the context of rapid urbanization and climate change, urban ecosystem services (ES) have undergone dramatic transformations. Elucidating the trade-off and synergy relationships among ES and quantifying how urbanization mediates these relationships are critical to achieving urban sustainability. Focusing on Shanghai during 2000–2020, we quantified three climate-related ES—water yield (WY), urban cooling (Heat Mitigation Index, HMI) and carbon storage (CS)—with the InVEST model. We then examined the spatio-temporal evolution of these services, analyzed their trade-offs and synergies, and examined the underlying urbanization drivers. Results show that total WY increased by 76%, with peak volumes concentrated in the central districts; HMI declined, with low-value zones spreading inward; CS rose and became spatially more homogeneous. WY–HMI trade-offs intensified, whereas CS–HMI were synergistic (r = 0.33–0.61) except in core districts where built-up expansion created trade-offs. CS–WY trade-offs weakened, becoming synergistic in most districts by 2020. HMI loss was driven by GDP and industrial output (p < 0.05). Per-capita green-space area was positively correlated with HMI but exerted no significant influence on CS or WY, highlighting the limitations of ecological interventions focused on single ES enhancement.

Due to the intensification of global climate change, agricultural production faces significant challenges such as frequent extreme weather events, exacerbated water scarcity, and rising temperatures which pose significant threats to crop growth and food security. This study evaluates the vulnerability of agriculture in Bulgaria, mainly maize and wheat production in the capital city of Sofia, under climate change scenarios based on multi-model ensemble data from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). By analyzing the spatiotemporal trends of temperature and precipitation from 2025 to 2100, the study reveals that the Sofia region will experience a fluctuating upward trend in temperature and a fluctuating downward trend in precipitation. Precipitation is identified as the primary factor influencing grain yield, while the impact of temperature changes is relatively minor. The yields of maize and wheat are projected to decline across three future periods (2025–2050, 2050–2075, and 2075–2100), with the most rapid decline occurring between 2025 and 2050. The study proposes adaptive strategies such as strengthening water resource management and promoting climate-smart agriculture to enhance the resilience and sustainability of agricultural systems and ensure food security. Keywords: climate change, agricultural vulnerability, CMIP6, precipitation variability, grain yield