The main purpose of this research is to model the spatial distribution of carbon sequestration, CO₂ absorption, and oxygen production by trees within Isfahan city, Iran, in 2020. To quantify carbon sequestration, we accessed a sample group of trees with measured biophysical attributes. First, we calculated the biomass and carbon sequestration of a tree using the allometric and photosynthesis equations. Then, to model the spatial distribution of carbon sequestration, we used geographic weighted regression (GWR) method. In this model, the amount of calculated carbon sequestration was the dependent variable, whereas the difference between vegetation indexes of Excess Green Plant Index minus Excess Red Plant Index (ΔExGR) from the Worldview image was the independent variable. Subsequently, the spatial distribution map of CO₂ absorption and oxygen production was generated. The total value of annual carbon sequestration, CO₂ absorption, and O₂ production was about 7704.22, 28274.502, and 20570.16 tons, respectively. The results showed that there was a strong correlation between the ΔExGR index of the canopy with calculated carbon. Integrating the ΔExGR index from a high‐resolution image with calculated carbon can contribute to developing a fast, accurate, and low‐cost method in estimating carbon sequestration and modeling its spatial distribution in urban areas. In conclusion, the results of this research can be implemented by land‐use planners to integrate urban ecosystem service concept (i.e., carbon sequestration) in planning process toward sustainability of the cities.

Ensuring a sustainable and renewable energy supply is a critical challenge for developing nations. This study aims to identify optimal locations for wind power development in the Kurdistan Region (KRG) of Iraq by integrating remote sensing, geographic information systems (GISs), and multicriteria decision‐making (MCDM) techniques, including Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The results indicate that ~21% of the study area (8277 km ² ) demonstrates excellent and good potential for wind power generation, with a total estimated capacity exceeding 48,000 MW. Furthermore, 3332 sites with individual generation capacities of over 3 MW were identified, showcasing substantial opportunities for large‐scale wind farm development. The analysis revealed wind speeds ranging from 7 to 14 m/s in the most suitable areas, ensuring optimal energy production. This research introduces a novel framework that integrates advanced spatial analysis with MCDM methods to optimize wind farm siting, considering critical factors such as wind resource assessment, site characteristics, environmental and social impacts, geotechnical constraints, and infrastructure availability. The findings suggest that the KRG has the potential to produce 42.9 TWh of electricity annually, which could save ~5.8 million tons of natural gas and reduce 16 million tons of CO ₂ emissions each year. These results highlight the region’s potential to emerge as a regional hub for wind energy, contributing significantly to global efforts in reducing fossil fuel dependency and mitigating climate change. This study provides a robust scientific foundation for policymakers and planners, offering a comprehensive and accurate assessment of wind energy potential. By integrating multiple decision‐making models and high‐resolution spatial data, this research enhances the reliability and applicability of its findings, serving as a valuable tool for sustainable energy development.

Urban areas globally are escalating contributors to carbon dioxide (CO) emissions, challenging sustainable development. This study proposes a novel micro-scale approach utilizing GIS to quantify CO emission spatial distribution, enhancing urban sustainability assessment. Employing a “bottom-up” methodology, emissions were calculated for various sources, revealing Isfahan’s urban area emits 13,855,525 tons of CO annually. Major contributors include stationary and mobile sources such as power plants (50.61%), road and rail transport (17.18%), and residential sectors (21.78%). Spatial distribution mapping showed that 81.68% of CO emissions originate from stationary sources, notably power plants. Furthermore, mobile sources, including road transport, contribute 17.16%, with emissions concentrated in main urban arteries. Agricultural machinery adds 1.14% of emissions, spatially distributed across Isfahan’s agricultural lands. Integration of emissions maps depicts the city’s total CO emissions, highlighting sectoral contributions. Despite limitations in data granularity, this study provides valuable insights into urban CO emissions dynamics, facilitating targeted mitigation strategies. Quantitative achievements include precise CO emission quantification and spatial distribution mapping, crucial for formulating effective urban sustainability policies.

Urban greenery and cooling initiatives have become top priorities for municipalities worldwide as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period, from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology.