Urban Development Impacts on Ecosystem Services: Modeling and Valuation of Carbon Storage in the Tehran Metropolis

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This study aimed to model and value changes in carbon storage in Tehran's ecological metabolism over three decades (1994–2024). Multi-temporal Landsat satellite images, advanced processing in Google Earth Engine, and the Random Forest algorithm were used for land use/cover classification. In addition, 66 urban soil samples were collected and analyzed to estimate soil organic carbon. The InVEST model was used to simulate changes in four main carbon pools (aboveground biomass, belowground biomass, soil, and dead organic matter). In addition, using economic valuation frameworks and global market reference rates, losses and benefits from carbon changes were calculated in the form of monetary indices. The results showed that over the study period, the area of ​​green spaces decreased from 14.19% in 1994 to 11.87% in 2024. These changes, along with the conversion of wasteland to construction, led to a significant reduction in carbon storage. Specifically, about 280,000 tonnes of carbon (equivalent to $ 7.62 million) were lost between 1994 and 2004, and more than 151,000 tonnes of carbon (equivalent to $ 4.11 million) between 2014 and 2024. Field-based models revealed several localized zones of carbon decline along with areas that could feasibly be restored. The results suggest that rapid urban growth and the steady loss of vegetation cover have greatly weakened the city’s capacity to retain carbon, and in turn, reduced its economic and ecological value. These observations highlight the importance of incorporating ecosystem service valuation into broader planning practices, ensuring that urban development does not undermine environmental integrity and contributes to climate resilience in large cities. Carbon Storage Ecosystem Services Economic Valuation Green Infrastructure Land Use Change Machine Learning Tehran Metropolis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Modern metropolitan regions face a growing set of interconnected challenges, many of which are rooted in human activity. Rapid population increases, unplanned urban expansion, and the overuse of natural resources have together placed severe stress on urban environments (Talkhabi et al., 2024 ). These pressures appear in multiple ways—from worsening air and water pollution to heavier traffic congestion and the gradual loss of ecological systems. Over the past few decades, Tehran, the capital and largest city of Iran, has shown a clear decline in environmental quality, a trend that reflects the cumulative effects of these human-driven changes (Mohaqeq et al., 2020 ). Urbanization remains one of the main causes of land use and land cover change (LULCC). Such changes reshape both the structure and function of ecosystems, particularly in fast-growing cities such as Tehran (Feddema et al., 2005 ; Newbold et al., 2015 ; Li et al., 2019 ). Expansion of the built environment alters landscapes through habitat fragmentation, increased greenhouse gas emissions, and the disturbance of hydrological cycles—each of which weakens the natural resilience of urban ecosystems (Zipperer et al., 2012 ). Ecosystems possess their own structures and processes that depend on the interaction between living organisms and physical conditions (Meng et al., 2025 ). These interactions give rise to a wide range of ecosystem services—the direct and indirect benefits people obtain from nature—which play an essential role in maintaining human well-being (Costanza et al., 1998 ). An important regulating services is carbon sequestration, which contributes to climate stabilization by capturing atmospheric CO₂ through photosynthetic processes in vegetation (Georgiou et al., 2025 ; Sharma et al., 2020 ; Baro et al ., 2015). Urban and peri-urban green spaces, particularly trees, parks, and forests play a vital role in the delivery of ecosystem services for city dwellers (Bolund and Hunhammar, 1999 ; Gomez-Baggethun and Barton 2013; Berghlin and Gomez-Baggethun 2021), including carbon sequestration and climate regulation, (McPherson 1998 ; Nowak and Crane 2002 ; Baró et al . 2012; Estruch et al., 2024 ; Hazrati et al ., 2019; Lisboa et al., 2024 ; Yin et al., 2022 ). However, these carbon stocks are vulnerable to anthropogenic disturbances such as deforestation, land degradation, and LULC changes (Khachoo et al., 2024 ; Lal et al., 2024 ; Zhang et al ., 2025). Over the last three decades, Tehran has undergone substantial urban expansion, resulting in a significant decline in its natural vegetation cover. According to available data, between 1994 and 2024, green spaces in the city were experienced a substantial loss—a trend largely attributed to construction booms, suburban sprawl, and deforestation (Talkhabi et al., 2024 ; Malekzadeh et al., 2025 ). These land use changes have disrupted microclimates and diminished Tehran’s ecological potential for absorbing atmospheric carbon. Another pressing environmental issue in Tehran is the continuous rise of carbon dioxide concentrations, particularly within its crowded residential districts and industrial zones. Since the mid-2000s, measurements from several urban monitoring stations have shown CO₂ levels consistently exceeding the limits proposed by the World Health Organization (WHO), with little sign of meaningful improvement over time (Hazrati et al ., 2019). When combined with the steady loss of vegetation and open space, this trend highlights why Tehran serves as a critical example for examining carbon sequestration strategies and potential policy interventions. Understanding how land use transformations influence carbon storage is central to evaluating the city’s environmental trajectory. By linking remote sensing and GIS analyses with ecological and economic modeling, researchers can explore how different patterns of urban growth shape the capacity of ecosystems to capture and store carbon (Felzer, 2025 ). Such integrated approaches also help visualize future scenarios, revealing where targeted management or restoration efforts might offer the greatest benefit. InVEST (Integrated Valuation of Ecosystem Services and Trade-Offs) is an open-source software model for mapping and valuing ecosystem services that uses biophysical data to examine how ecosystem change affects ecosystem services (Arcidiacono et al., 2015 ). The InVEST model has emerged as a widely used tool for assessing carbon storage due to its simplicity, spatial explicitness, and ability to integrate with land use simulation models like CA–Markov and PLUS (He et al., 2016 ; Lei et al., 2024 ; Sharp et al., 2015 ). This model also allows for scenario-based analysis, enabling policymakers to evaluate trade-offs and synergies between development and conservation (Wang et al., 2025 ; Tariq and Mumtaz, 2023 ). Moreover, the economic valuation of carbon storage—using tools such as the Social Cost of Carbon (SCC)—provides a framework for comparing ecological benefits with development costs (Stern, 2007 ; Tol, 2005 ). This approach supports evidence-based decision-making and can inform strategies for internalizing environmental costs (Nguyen et al., 2024 ). Despite growing interest in urban ecosystem services, few studies have comprehensively modeled and economically valued the impacts of land use change on carbon storage in rapidly urbanizing cities like Tehran (Malekzadeh et al., 2025 ; Varshney et al., 2022 ). Most existing research has focused on either current carbon stocks or historical land use trends, with limited attention to future scenarios and integrated valuation approaches. To fill existing gaps in understanding how rapid urbanization affects carbon storage over time and space in megacities such as Tehran, this study applies a cloud-based analytical framework designed to improve both accuracy and scalability. In semiarid urban regions like Tehran, where the pace of development has often outstripped integrated modeling efforts, such an approach is particularly valuable. The analysis was conducted using the Google Earth Engine (GEE) platform, which is well known for its ability to combine large volumes of satellite data with machine learning algorithms while avoiding the heavy computational demands of conventional desktop processing (Phan et al., 2020 ). Land use and land cover (LULC) changes were identified across four decades—1994, 2004, 2014, and 2024—by applying the Random Forest classifier to high-resolution Landsat imagery, enhanced through spectral indices and topographic variables. Compared with traditional supervised methods such as maximum likelihood classification, the Random Forest approach achieved notably higher accuracy, consistent with findings from other urban studies (Amani et al., 2020 ). Beyond improving processing efficiency, this workflow supports rapid and automated assessment of Tehran’s landscape dynamics and provides insight into challenges related to informal urban expansion and climate vulnerability. Building on this foundation, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model is utilized to simulate variations in carbon stocks across key reservoirs—aboveground and belowground biomass, soil organic matter, and dead organic material—under evolving urban scenarios (Sharp et al., 2020 ). Economic valuation is then performed by incorporating global carbon market benchmarks, translating biophysical changes into monetary terms to highlight trade-offs and opportunities for low-carbon development. Outputs are visualized through detailed spatiotemporal maps for each period and LULC category, facilitating granular insights into urban expansion's impacts on ecosystem services. This holistic approach advances beyond static inventories by providing a predictive, policy-oriented toolset that empowers urban planners and decision-makers to embed sustainability into growth strategies, ultimately fostering resilience in emerging megacities. The primary objective of this study is to develop and validate a replicable framework for assessing and valuing the effects of urban development on carbon storage services, with a specific focus on Tehran. To achieve this, the research integrates remote sensing and machine learning techniques within the Google Earth Engine platform to produce multi-temporal land use and land cover maps, capturing long-term patterns of urban expansion. The InVEST carbon model is then employed to simulate the dynamics of carbon pools and estimate both sequestration potential and associated losses. In parallel, the study aims to derive economic valuations and actionable recommendations, such as incentive-based green infrastructure strategies, to mitigate environmental degradation while advancing broader climate and urban sustainability goals. Materials and methods Study area Tehran, the capital of Iran, lies between the rugged Alborz Mountains to the north and the vast Markazi Desert to the south, forming an urban landscape that has evolved through both its distinctive topography and a long historical process of expansion. The city now covers approximately 733 square kilometers, and its elevation varies sharply—from about 900 meters in the southern plains to nearly 1,800 meters in the foothills. This steep gradient produces noticeable differences in climate and environmental conditions between the northern and southern districts, shaping patterns of development, vegetation, and air quality across the metropolis (Zarandi et al., 2021 ; Afarideh et al., 2023 ). Figure 1 illustrates the geographic location of the Tehran metropolis that situated between 35° 35′ to 35° 51′ N latitude and 51° 4′ to 51° 33′ E longitude, Tehran is flanked by the plains of Shahriar and Varamin to the south and southwest, and bordered by steep mountain ranges in the north and east (Ramyar et al., 2019 ). Hydrologically, the Karaj and Jajroud rivers demarcate the western and eastern edges of the urban expanse, converging toward the southeastern drylands near the Namak Desert. Tehran’s evolution from a modest pre-Qajar town into Iran’s largest city has been marked by rapid demographic growth. Historical census data reflects a surge from approximately 1.56 million inhabitants in 1946 to 9.1 million residents by 2024, as reported by the Tehran Province Planning and Management Organization (Management and Planning Organization, 2024 ; Iran Statistical Center, 2016 ). Tehran contains over 56,000 hectares of green spaces, both within and surrounding its built-up areas. Presently, urban green space per capita stands at 16.33 m², complemented by 6.9 m² per capita of parks and recreational zones distributed among 2,366 public spaces across the metropolis (Tehran City Statistical Yearbook, 2024). This distinctive interplay between natural topography, urban expansion, and ecological structures situates Tehran as a pivotal subject for research in metropolitan resilience, urban planning, and sustainability—particularly within the context of rapidly transforming Middle Eastern megacities. Research methodology This research is conducted based on the stages and phases outlined briefly in Fig. 2 . The model and methods used are summarized below. Preparation of Land Use/Land Cover map To construct a comprehensive Land Use/Land Cover (LU/LC) profile for Tehran spanning the period 1994 to 2024, a multi-temporal analysis of Landsat satellite datasets was performed using Google Earth Engine (GEE). This included imagery from the Thematic Mapper (TM) for 1994 and 2004, Enhanced Thematic Mapper Plus (ETM+) for 2014, and Operational Land Imager (OLI) for 2024, all accessed via the United States Geological Survey (USGS) Earth Explorer catalog integrated within GEE (Table 1 ). Table 1 Image processing information Year Image source Sensor Training sample number Kappa cofficient 1994 USGS Landsat 5 Level 2, Collection 2, Tier 1 TM 3536 0.8 ALOS DSM: Global 30m v4.1 2004 USGS Landsat 5 Level 2, Collection 2, Tier 1 TM 36525 0.92 ALOS DSM: Global 30m v4.1 2014 USGS Landsat 7 Level 2, Collection 2, Tier 1 ETM+ 3579 0.86 ALOS DSM: Global 30m v4.1 2024 USGS Landsat 8 Level 2, Collection 2, Tier 1 OLI/TIRS 2059 0.94 ALOS DSM: Global 30m v4.1 The preprocessing stage was carried out entirely within the Google Earth Engine (GEE) environment. It began with automated atmospheric correction using the Surface Reflectance (SR) products supplied by the USGS. These datasets apply the LEDAPS algorithm for Landsat 5/7 and LaSRC for Landsat 8/9, helping to normalize pixel values and reduce the influence of atmospheric noise (Shrestha et al., 2019 ; Vermote et al., 2016 ; Balist et al., 2022 ). To further refine image quality, cloud masking was performed using the CFMask procedure, followed by temporal compositing to create median, cloud-free mosaics and geometric co-registration to maintain spatial alignment among scenes (Gorelick et al., 2017 ). Together, these steps ensured that the multi-decadal imagery was consistent and reliable for subsequent machine-learning analysis. Following preprocessing, an initial unsupervised classification using the Iterative Self-Organizing Data Analysis Technique (ISODATA) was applied within GEE to delineate preliminary LU/LC clusters. To refine feature extraction, index-based segmentation was performed leveraging multiple spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Bare Soil Index (BSI) and Normalized Difference Water Index (NDWI), facilitating the accurate identification of Regions of Interest (RoIs) for training samples across ecological and urban land features. Following the preprocessing stage, supervised classification was carried out using machine learning techniques, with a particular focus on the Random Forest (RF) algorithm. This model, originally developed by Breiman ( 2001 ), uses an ensemble of decision trees to assign pixels to land-cover categories based on their spectral, textural, and topographic attributes (Bilotta et al., 2025 ; Bill Donatien et al., 2024 ). Within the GEE JavaScript API, the RF model was trained using a balanced set of regions of interest (RoIs)—typically ranging from 500 to 1,000 samples per class—derived from high-resolution reference imagery. The model employed 100 decision trees, a minimum leaf size of five, and out-of-bag error estimation to fine-tune performance and minimize overfitting. This approach allowed for the clear separation of four dominant land-use categories: urban built-up areas, barren or unvegetated lands, vegetated green zones, and water bodies. Classification outputs were validated through a protocol, cross-referencing with high-resolution Google Earth imagery, geolocated GPS checkpoints, and existing LU/LC maps for Tehran. Accuracy assessments, including overall accuracy, user's and producer's accuracies, and the Kappa coefficient, were computed using GEE's built-in confusion matrix tools. Kappa values exceeding 0.85 indicated strong agreement between the machine learning-derived maps and ground truth data, validating the robustness of the GEE-based workflow (Tesfaye et al., 2024 ; Shoja et al., 2025 ). This section details the modeling of land use and land cover changes within the Tehran metropolitan area. The analysis for the years 1994, 2004, 2014, and 2024 was conducted using the Google Earth Engine and a machine learning approach. Carbon Reservoirs and Soil Organic Carbon Estimation To investigate the spatial distribution of soil organic carbon (SOC) reservoirs across Tehran’s urban landscape, a total of sixty-six soil samples were systematically collected from various public parks and green spaces, as depicted in the accompanying Fig. 4 . These samples served as the foundation for generating a detailed SOC mapping layer. The organic carbon content of each sample was quantified under controlled laboratory conditions and expressed as mass per unit surface area (tons per hectare). SOC content was calculated as Eq. 1 (Yu et al., 2009 ): SOC (ton/ha) = \(\:\frac{\%OC*BD*D}{100}\) *10,000 m 2 ha − 1 \(\:\left(1\right)\) Where: %OC refers to the percentage of organic carbon in the soil sample, BD denotes the bulk density of the soil (measured in Mg/m²), D represents soil depth (in millimeters). This equation ensures the integration of both compositional and physical soil characteristics, enabling precise estimation of carbon stocks within the surface soil layer. The derived SOC values contribute to understanding the carbon sequestration capacity of urban green spaces and their potential role in climate mitigation strategies. Carbon sequestration and storage ecosystem service model Terrestrial carbon storage potential is predominantly determined by four primary reservoirs: above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter. The InVEST carbon sequestration and storage model quantifies carbon stored within these pools based on user-supplied Land Use/Land Cover (LU/LC) maps and classifications. Above-ground biomass comprises all living vegetation above the soil surface, including stems, branches, and leaves. Below-ground biomass consists of living root systems. Soil organic carbon, representing the most substantial terrestrial carbon reservoir, reflects the organic fraction of soil. Dead organic matter includes decomposing litter, fallen leaves, and standing deadwood. Leveraging spatial land cover data, the model estimates the net carbon accumulation over time and its monetized ecosystem value, accounting for changes in land cover types and carbon content per reservoir. However, it operates under simplifying assumptions, such as a linear rate of sequestration, a fixed discount rate, and exclusion of biophysical parameters like photosynthetic intensity or soil microbial activity. These omissions may constrain ecological accuracy. Raster-based LU/LC inputs such as forest, cropland, and pasture enable pixel-level estimation of carbon storage density. The model outputs include: (1) total carbon stock, (2) net carbon sequestration, (3) economic valuation, and (4) aggregated raster maps. Accuracy improves when data on multiple reservoirs are available for each LU/LC class. The model compares current and future landscape scenarios to compute sequestration dynamics, producing per-pixel change maps that reflect carbon stock fluctuations and their associated societal valuation. Model Equations Carbon inventory estimation follows established carbon accounting principles (Ortas et al., 2016 ). The annual change in carbon stock across all LU/LC categories is calculated as Eq. 2: $$\:{\varDelta\:\complement\:}_{AFOLU\:=}{\varDelta\:\complement\:}_{FL}+{\varDelta\:\complement\:}_{CL}+{\varDelta\:\complement\:}_{GL}+{\varDelta\:\complement\:}_{WL}+{\varDelta\:\complement\:}_{SL}+{\varDelta\:\complement\:}_{OL}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ Where: ΔC = Total annual carbon change AFOLU = Agriculture, Forestry, and Other Land Uses FL, CL, GL, WL, SL, OL = Land use categories: Forest, Cultivation, Grassland, Wetland, Settlement, and Others Carbon change per LU/LC type is determined via reservoir-level aggregation is calculated as Eq. 3: $$\:{\varDelta\:\complement\:}_{LUi}={\varDelta\:\complement\:}_{AB}+{\varDelta\:\complement\:}_{BB}+{\varDelta\:\complement\:}_{DW}+{\varDelta\:\complement\:}_{LI}+{\varDelta\:\complement\:}_{SO}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ AB = Above-ground biomass BB = Below-ground biomass DW = Dead wood LI = Leaf litter SO = Soil organic matter Net carbon sequestration across a temporal span is calculated using the inventory-difference technique as Eq. 4: $$\:\varDelta\:\complement\:=\frac{\left({\complement\:}_{t2}+{\complement\:}_{t1}\right)}{\left({t}_{2}-{t}_{1}\right)}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)\:\:\:\:\:\:\:\:$$ Where: ΔC = Annual carbon change (tons/year) Cₜ₁, Cₜ₂ = Carbon inventory at the start and end of the period Required Input Data All spatial datasets must be in projected coordinate systems and use meters for linear units. Current LU/LC Raster: A geospatial raster where each pixel is assigned an integer representing a distinct LU/LC class. These codes must match entries in the Carbon Pool table. Reference Calendar Year: Indicates the temporal scope of the current LU/LC data for valuation and sequestration calculation. Carbon Reservoir CSV Table: Contains carbon densities for four reservoirs for each LU/LC class. If certain data are unavailable, placeholder zeroes may be used (Table 2). Table 3 CSV Table Column Description Unit LUcode Unique integer per land cover class Integer c_above Above-ground biomass density Mg/hectare c_below Below-ground biomass density Mg/hectare c_soil Soil organic carbon density Mg/hectare c_dead Dead organic matter density Mg/hectare For valuation purposes, a base price of US $ 35 per tonne of carbon was chosen. This figure was chosen based on average global values ​​for carbon and was consistent with previous studies in developing countries (Goulder and Williams., 2012). A discount rate of 2% (Rennert & Kingdon., 2019) was used to account for the time value of money as well as perceptions of intergenerational equity (i.e., the value of money today compared to the future). It was also assumed that the price of a unit of carbon would increase by 3% annually (Pache et al., 2020 )—an assumption adjusted for global trends of increasing carbon regulations, the growth of carbon markets, and likely inflation. With this set of assumptions, the economic value of carbon storage is reflected in a way that takes into account both current market conditions and future expectations. Results and discussion Land use/Land cover change Figure 3 presents a visual representation of land use and land cover (LU/LC) dynamics across the study period from 1994 to 2024. Table 3 provides a quantitative breakdown of LU/LC transitions over four decadal intervals. During this thirty-year period, green spaces in Tehran experienced a noticeable decline—from 14.19% of the total urban area in 1994 to 11.87% in 2024. This reduction was more pronounced in the initial decades: approximately 700 hectares were lost between 1994 and 2004, followed by a similar loss over the second ten-year interval. However, in the final decade (2014–2024), the rate of decline slowed considerably, with green space reduction amounting to only 10 hectares. These findings suggest that the pressure on green spaces was highest during the early phases of urban expansion. Barren lands, primarily located in peri-urban zones and interstitial spaces with minimal vegetation, also diminished considerably over the study period. These areas were progressively repurposed to accommodate urban infrastructure, becoming the principal source for land conversion. Notably, approximately 7,000 hectares of barren land underwent transition to built-up uses between 1994 and 2004. This figure decreased to 4,000 hectares between 2004 and 2014, and slightly increased to 4,300 hectares in the final period, indicating continued but gradually tapering urban land demand. Table 3 Land use changes in Tehran city from1994-2024 1994 ha % 2004 ha % 2014 ha % 2024 ha % Water Bodies 59 0.09 95 0.15 41 0.06 168 0.2 Green Areas 8706 14.1 8012 13 7295 11.8 7284 11.8 Built-Up Area 22907 37.3 30677 50 35520 57.9 39726 64.7 Bare land 29673 48.3 22560 36.7 18488 30.1 14166 23 Sum 61345 100 61345 100 61345 100 61345 100 Urban expansion into previously undeveloped or ecologically functional land has been the predominant driver of LU/LC transformation. Built-Up areas expanded by approximately 7,500 hectares in the first decade (1994–2004), followed by 5,000 hectares in the second, and 4,000 hectares in the final ten-year interval. Apart from minor allocations of land to water bodies, most land conversion activities drew from barren lands initially, with increasing encroachment into green areas—particularly private gardens and semi-natural zones—in later years. These patterns reveal the cumulative impact of rapid urbanization on natural and semi-natural landscapes, highlighting the need for robust land management strategies to safeguard remaining green infrastructure in Tehran metropolis. Carbon sequestration model Figure 4 illustrates the spatial distribution of soil organic carbon (SOC) across the study area, modeled as a function of soil depth, bulk density, and organic carbon percentage. These input parameters were derived from empirical data. Carbon Reservoirs and Land Cover Typologies Table 4 outlines the estimated carbon densities within four principal carbon reservoirs—above-ground biomass, below-ground biomass, soil organic matter, and dead organic material—across distinct land use/land cover (LU/LC) classes. Each value is expressed in metric tons per hectare (Mg/ha). Table 4 Carbon Pool Densities per LULC Class (Mg/hectare) LUcode LU/LC class C_above (Mg/ha) C_below (Mg/ha) C_soil (Mg/ha) C_dead (Mg/ha) 1 Water 0 5.00 0 0 2 Green area 11.00 2.00 65.00 1.10 3 Built-up 4.00 5.00 15.00 1.00 4 Bareland 0.40 0.83 58.00 0 The outputs of the InVEST carbon storage and sequestration model are presented in raster format, with each pixel representing a cell-specific carbon stock expressed in Mg/cell. These maps reveal the spatial variation in carbon sequestration potential throughout the Tehran metropolis. Values ranged from 0.45 Mg/cell at the lower end, typically associated with barren or impervious surfaces, to 7.119 Mg/cell in areas with dense vegetation and high organic soil content. Comparative analysis of the carbon maps highlights areas of significant carbon accumulation versus regions with minimal sequestration capacity. These spatial patterns are crucial for identifying priority zones for conservation and strategic urban planning, especially in the context of enhancing urban carbon sinks (Fig. 5 ). Spatiotemporal Variations in Carbon Sequestration Figure 6 illustrates the dynamic changes in carbon sequestration across four distinct intervals between 1994 and 2024. The quantified values—expressed in metric tons—reflect both positive and negative fluctuations. Positive values denote net carbon uptake through vegetative or ecological restoration processes, whereas negative values indicate carbon loss attributable to anthropogenic activities such as deforestation, land conversion, or urban expansion. Spatial distribution patterns of carbon change closely correspond with significant alterations in land use and land cover. Areas exhibiting the most extreme values—either positive or negative—highlight regions of pronounced ecological transition. As demonstrated in Fig. 6 , green-shaded zones represent enhanced carbon sequestration and storage, while red-shaded areas reveal net reductions in stored carbon due to environmental degradation. This analysis provides critical insight into the influence of land management practices on atmospheric carbon dynamics and offers an evidence-based foundation for targeted climate mitigation strategies at the regional scale. Changes in the Economic Value of Carbon Sequestration As mentioned in the methodology section, the economic valuation of carbon stocks was based on $ 35 per ton, which is consistent with regional conditions and the global average for carbon markets. This value was considered to be the midpoint between the highly variable global market prices—from over $ 80 in the formal EU markets to less than $ 1 in voluntary markets—to maintain comparability with international studies. Also, to account for the time dimension of carbon value, a discount rate of 2% and an annual growth rate of 3% were used to reflect changes in economic value over the assessment period (Goulder & Williams, 2012 ; Pache et al., 2020 ; World Bank, 2023 ). Figure 7 presents the spatial distribution of changes in carbon-related economic value across the study intervals. The map highlights areas where the monetary value of carbon stocks has fluctuated due to ecological transitions and land-use dynamics. Purple-shaded regions denote a decline in economic value, either from reduced carbon storage or complete carbon loss. Across the landscape, these changes span a spectrum between USD − 181 and USD + 181, reflecting variations in sequestration performance and ecosystem service delivery. This spatially explicit evaluation provides crucial insight into the interplay between environmental management and carbon market economics. Despite using an economic valuation approach to quantitatively compare changes in ecosystem services, it should be noted that monetizing these services is a controversial issue and cannot replace broader environmental and social values. As Gómez-Baggethun and Ruiz-Pérez ( 2011 ) have pointed out, linking ecosystem management to market logic may ignore non-economic aspects of sustainability. Therefore, the results of this study should be interpreted within a balanced and multidimensional framework in which economic value is only one dimension of environmental decision-making. Impacts of Land Use Dynamics on Carbon Loss and Economic Valuation Over the three-decade span from 1994 to 2024, extensive land use transformations within Tehran have resulted in substantial carbon emissions. Specifically, the release of > 280,000 metric tonnes of carbon, corresponding to an economic loss of approximately USD 7,600,000, has been recorded. During the 2004–2014 interval, ~ 177,000 tonnes of carbon were emitted, translating into USD > 4,800,000 in foregone sequestration value. The period from 2014 to 2024 saw an additional loss of 151,000 tons, valued at > USD 4,100,000 (Table 5 ). These figures underscore the profound influence of unregulated urban expansion and construction activities, which have systematically encroached upon green spaces and previously undeveloped lands. Despite municipal initiatives aimed at restoring urban vegetation and promoting green infrastructure, the scale and intensity of built-area development have largely offset potential ecological gains. The data presented herein reinforces the urgency of implementing land use policies that prioritize carbon-sensitive planning and integrate ecosystem service valuation into urban development frameworks. Without corrective action, the economic and environmental costs associated with unchecked expansion may continue to rise, undermining Tehran’s climate resilience strategies. Table 5 Decadal Trends in Carbon Sequestration and Its Economic Valuation Description/year 1994 2004 2014 2024 Total Sequestered Carbon (1000 Mg of C) 3017 2737 2560 2409 Change in Carbon (1000 Mg of C) -280 -176 -151 Net present value (USD) -7626000 -4814000 -4112000 The results of this study, compared with similar studies in other cities around the world, show that the reduction of green spaces and the subsequent decline in carbon sequestration and storage capacity is a global phenomenon, but its severity is more significant in Tehran. In this study, the reduction in the share of green spaces from 14.19% to 11.87% over three decades (1994 to 2024) caused a significant decrease in carbon storage and an economic loss of more than $ 7.6 million. In Shenzhen, China, Wang et al. ( 2024 ) used the CASA and InVEST models to show that the total carbon storage of green spaces between 2008 and 2022 was in the range of 7.17 to 7.42 million tons, with significant spatial variations between the western and eastern parts of the city; while the InVEST model is less accurate in built-up areas. In Noida, India, Sharma et al. ( 2024 ) reported using multitemporal satellite imagery and the InVEST model that the rapid expansion of construction between 2011 and 2019 significantly reduced the carbon sequestration capacity and its economic value in the projected scenario of 2027. In Beijing, Cao et al. ( 2025 ) showed that the carbon density of urban green spaces is strongly dependent on the vegetation structure, and increasing the density of trees and shrubs improves the accuracy of modeling and carbon estimation. Overall, while cities such as Shenzhen, Noida, and Beijing have also faced a decrease or fluctuation in carbon storage due to urban development, the intensity of the conversion of wasteland to construction and the gradual encroachment of development into private gardens and semi-natural spaces in Tehran, along with heavy economic losses, indicate a more critical situation for this city. The findings of this study, in line with global literature, emphasize the need for careful land use policymaking and the development of green infrastructure to restore urban carbon sinks, especially in semi-arid regions. Moreover, the application of the InVEST model allowed for a spatially explicit valuation of carbon reservoirs and highlighted zones where conservation or restoration efforts could yield tangible climate benefits. The sharp decline in net present value of carbon stocks—exceeding USD 7.6 million over the study period—illustrates how development-driven carbon emissions erode not only environmental stability but also economic opportunities tied to ecosystem services. The consistency of carbon loss in areas undergoing artificial land conversion further reinforces the importance of strategic planning and data-driven environmental management to balance urban growth with climate mitigation objectives. Conclusions This study provides a comprehensive understanding of the dynamics of land use change across the Tehran urban region over a 30-year period. The accelerated decline of green and barren lands, particularly between 1994 and 2014, coincided with rapid population growth, rural-to-urban migration, and intense demand for urban expansion. The geographical constraints imposed by mountainous terrain, limited land availability, and saturation of development capacity have emerged as critical challenges for horizontal growth in recent years. Consequently, urban vertical expansion—through high-rise construction—has gained prominence as a compensatory strategy. Moreover, demographic shifts and economic pressures have further impacted the city’s developmental trajectory, with slower population growth and fiscal constraints contributing to reduced construction intensity. In parallel, the increasing prioritization of urban green spaces reflects a strategic pivot toward environmental sustainability. Green infrastructures not only mitigate air pollution and heat island effects, but they also enhance overall urban livability. A key contribution of this study lies in its application of the InVEST carbon sequestration and storage model, which spatially quantifies ecosystem services and highlights the value of vegetated landscapes. These patterns reveal the cumulative impact of rapid urbanization on natural and semi-natural landscapes, highlighting the need for robust land management strategies to safeguard remaining green infrastructure in Tehran metropolis. The substantial decrease in carbon stock and its economic valuation underscore the pressing need for data-driven urban planning strategies that integrate climate resilience. As terrestrial ecosystems act as critical carbon reservoirs, their preservation plays a pivotal role in achieving sustainable development goals. Hence, the findings of this research can inform land use scenarios, policy interventions, and ecological restoration efforts that reinforce Tehran’s capacity to adapt to climate challenges and maintain ecosystem functionality. Declarations Conflict of Interest The authors declare no conflict of interest. References Afarideh F, Ramasht MH, Mortyn G (2023) Air pollution and topography in Tehran. 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01:42:20","extension":"xml","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157683,"visible":true,"origin":"","legend":"","description":"","filename":"IJERD25019361structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/f3de3d4e3f38d63bc6a64274.xml"},{"id":95877423,"identity":"33b70d83-c2d7-4e51-9a38-6c50d5c164f3","added_by":"auto","created_at":"2025-11-14 01:42:20","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166342,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/948c3d30857b8a8c859345de.html"},{"id":96242787,"identity":"e2b3d81b-d94d-46e6-b6af-05ad4bfbd4c1","added_by":"auto","created_at":"2025-11-19 07:14:20","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174782,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the study area and Tehran’s 22 urban districts\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/c0de5fbbdb03c48f3890e564.jpeg"},{"id":95877391,"identity":"4d16bf43-6964-4f39-9334-a5ed9f224184","added_by":"auto","created_at":"2025-11-14 01:42:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91284,"visible":true,"origin":"","legend":"\u003cp\u003econceptual research model\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/b1b3e05e1f72b282f38bd550.jpg"},{"id":95877394,"identity":"1fa22e12-9dbe-4fb7-b869-69eec443977e","added_by":"auto","created_at":"2025-11-14 01:42:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1299779,"visible":true,"origin":"","legend":"\u003cp\u003eLand use maps of Tehran city from 1994 to 2024\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/f3d8f6a9d7b7baf665bc179b.jpeg"},{"id":96242034,"identity":"de0c4942-f764-43ea-9cb9-f3c690b85e33","added_by":"auto","created_at":"2025-11-19 07:11:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114857,"visible":true,"origin":"","legend":"\u003cp\u003eSoil organic carbon map in Tehran city\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/3d79b45b182691b435bdd123.jpeg"},{"id":95877405,"identity":"a3d0eef3-6404-4f8f-a2c3-70cc1979cb74","added_by":"auto","created_at":"2025-11-14 01:42:19","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1142942,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon sequestration potential map in Tehran city from 1994 to 2024\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/96d03b7ead72154acdb7681b.jpeg"},{"id":96242390,"identity":"7f463df8-a11d-4bcf-a7ce-930d8a8d8984","added_by":"auto","created_at":"2025-11-19 07:12:54","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1147256,"visible":true,"origin":"","legend":"\u003cp\u003eDecadal Carbon changes over every two consecutive periods\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/4e30f64fbdbaad5a84dd64a1.jpeg"},{"id":96242931,"identity":"14fa38fa-cd51-4fae-ba84-56642e397e67","added_by":"auto","created_at":"2025-11-19 07:14:53","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1120649,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of changes in economic value of sequestrated carbon\u003c/p\u003e","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/283f5cf7ab91bcefd02608c0.jpeg"},{"id":96452739,"identity":"a53131b2-4f7c-4aeb-8359-a8daa4a376e6","added_by":"auto","created_at":"2025-11-21 09:40:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6025352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/3b41c844-5e7d-4f7e-8565-2e6e50f8b3c4.pdf"},{"id":96241665,"identity":"8b1e582a-f578-4ec7-a8bc-12bd505d825c","added_by":"auto","created_at":"2025-11-19 07:11:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15365,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-7656394/v1/ba8f03b8ee857c80201d2901.docx"}],"financialInterests":"","formattedTitle":"Urban Development Impacts on Ecosystem Services: Modeling and Valuation of Carbon Storage in the Tehran Metropolis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eModern metropolitan regions face a growing set of interconnected challenges, many of which are rooted in human activity. Rapid population increases, unplanned urban expansion, and the overuse of natural resources have together placed severe stress on urban environments (Talkhabi et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These pressures appear in multiple ways\u0026mdash;from worsening air and water pollution to heavier traffic congestion and the gradual loss of ecological systems. Over the past few decades, Tehran, the capital and largest city of Iran, has shown a clear decline in environmental quality, a trend that reflects the cumulative effects of these human-driven changes (Mohaqeq et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUrbanization remains one of the main causes of land use and land cover change (LULCC). Such changes reshape both the structure and function of ecosystems, particularly in fast-growing cities such as Tehran (Feddema et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Newbold et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Expansion of the built environment alters landscapes through habitat fragmentation, increased greenhouse gas emissions, and the disturbance of hydrological cycles\u0026mdash;each of which weakens the natural resilience of urban ecosystems (Zipperer et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEcosystems possess their own structures and processes that depend on the interaction between living organisms and physical conditions (Meng et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These interactions give rise to a wide range of ecosystem services\u0026mdash;the direct and indirect benefits people obtain from nature\u0026mdash;which play an essential role in maintaining human well-being (Costanza et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). An important regulating services is carbon sequestration, which contributes to climate stabilization by capturing atmospheric CO₂ through photosynthetic processes in vegetation (Georgiou et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Baro \u003cem\u003eet al\u003c/em\u003e., 2015).\u003c/p\u003e\u003cp\u003eUrban and peri-urban green spaces, particularly trees, parks, and forests play a vital role in the delivery of ecosystem services for city dwellers (Bolund and Hunhammar, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Gomez-Baggethun and Barton 2013; Berghlin and Gomez-Baggethun 2021), including carbon sequestration and climate regulation, (McPherson \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Nowak and Crane \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bar\u0026oacute; \u003cem\u003eet al\u003c/em\u003e. 2012; Estruch et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hazrati \u003cem\u003eet al\u003c/em\u003e., 2019; Lisboa et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, these carbon stocks are vulnerable to anthropogenic disturbances such as deforestation, land degradation, and LULC changes (Khachoo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lal et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang \u003cem\u003eet al\u003c/em\u003e., 2025).\u003c/p\u003e\u003cp\u003eOver the last three decades, Tehran has undergone substantial urban expansion, resulting in a significant decline in its natural vegetation cover. According to available data, between 1994 and 2024, green spaces in the city were experienced a substantial loss\u0026mdash;a trend largely attributed to construction booms, suburban sprawl, and deforestation (Talkhabi et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Malekzadeh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These land use changes have disrupted microclimates and diminished Tehran\u0026rsquo;s ecological potential for absorbing atmospheric carbon.\u003c/p\u003e\u003cp\u003eAnother pressing environmental issue in Tehran is the continuous rise of carbon dioxide concentrations, particularly within its crowded residential districts and industrial zones. Since the mid-2000s, measurements from several urban monitoring stations have shown CO₂ levels consistently exceeding the limits proposed by the World Health Organization (WHO), with little sign of meaningful improvement over time (Hazrati \u003cem\u003eet al\u003c/em\u003e., 2019). When combined with the steady loss of vegetation and open space, this trend highlights why Tehran serves as a critical example for examining carbon sequestration strategies and potential policy interventions.\u003c/p\u003e\u003cp\u003eUnderstanding how land use transformations influence carbon storage is central to evaluating the city\u0026rsquo;s environmental trajectory. By linking remote sensing and GIS analyses with ecological and economic modeling, researchers can explore how different patterns of urban growth shape the capacity of ecosystems to capture and store carbon (Felzer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such integrated approaches also help visualize future scenarios, revealing where targeted management or restoration efforts might offer the greatest benefit.\u003c/p\u003e\u003cp\u003eInVEST (Integrated Valuation of Ecosystem Services and Trade-Offs) is an open-source software model for mapping and valuing ecosystem services that uses biophysical data to examine how ecosystem change affects ecosystem services (Arcidiacono et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The InVEST model has emerged as a widely used tool for assessing carbon storage due to its simplicity, spatial explicitness, and ability to integrate with land use simulation models like CA\u0026ndash;Markov and PLUS (He et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lei et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sharp et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This model also allows for scenario-based analysis, enabling policymakers to evaluate trade-offs and synergies between development and conservation (Wang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tariq and Mumtaz, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, the economic valuation of carbon storage\u0026mdash;using tools such as the Social Cost of Carbon (SCC)\u0026mdash;provides a framework for comparing ecological benefits with development costs (Stern, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tol, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This approach supports evidence-based decision-making and can inform strategies for internalizing environmental costs (Nguyen et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite growing interest in urban ecosystem services, few studies have comprehensively modeled and economically valued the impacts of land use change on carbon storage in rapidly urbanizing cities like Tehran (Malekzadeh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Varshney et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Most existing research has focused on either current carbon stocks or historical land use trends, with limited attention to future scenarios and integrated valuation approaches.\u003c/p\u003e\u003cp\u003eTo fill existing gaps in understanding how rapid urbanization affects carbon storage over time and space in megacities such as Tehran, this study applies a cloud-based analytical framework designed to improve both accuracy and scalability. In semiarid urban regions like Tehran, where the pace of development has often outstripped integrated modeling efforts, such an approach is particularly valuable. The analysis was conducted using the Google Earth Engine (GEE) platform, which is well known for its ability to combine large volumes of satellite data with machine learning algorithms while avoiding the heavy computational demands of conventional desktop processing (Phan et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLand use and land cover (LULC) changes were identified across four decades\u0026mdash;1994, 2004, 2014, and 2024\u0026mdash;by applying the Random Forest classifier to high-resolution Landsat imagery, enhanced through spectral indices and topographic variables. Compared with traditional supervised methods such as maximum likelihood classification, the Random Forest approach achieved notably higher accuracy, consistent with findings from other urban studies (Amani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Beyond improving processing efficiency, this workflow supports rapid and automated assessment of Tehran\u0026rsquo;s landscape dynamics and provides insight into challenges related to informal urban expansion and climate vulnerability.\u003c/p\u003e\u003cp\u003eBuilding on this foundation, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model is utilized to simulate variations in carbon stocks across key reservoirs\u0026mdash;aboveground and belowground biomass, soil organic matter, and dead organic material\u0026mdash;under evolving urban scenarios (Sharp et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Economic valuation is then performed by incorporating global carbon market benchmarks, translating biophysical changes into monetary terms to highlight trade-offs and opportunities for low-carbon development. Outputs are visualized through detailed spatiotemporal maps for each period and LULC category, facilitating granular insights into urban expansion's impacts on ecosystem services. This holistic approach advances beyond static inventories by providing a predictive, policy-oriented toolset that empowers urban planners and decision-makers to embed sustainability into growth strategies, ultimately fostering resilience in emerging megacities.\u003c/p\u003e\u003cp\u003eThe primary objective of this study is to develop and validate a replicable framework for assessing and valuing the effects of urban development on carbon storage services, with a specific focus on Tehran. To achieve this, the research integrates remote sensing and machine learning techniques within the Google Earth Engine platform to produce multi-temporal land use and land cover maps, capturing long-term patterns of urban expansion. The InVEST carbon model is then employed to simulate the dynamics of carbon pools and estimate both sequestration potential and associated losses. In parallel, the study aims to derive economic valuations and actionable recommendations, such as incentive-based green infrastructure strategies, to mitigate environmental degradation while advancing broader climate and urban sustainability goals.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eTehran, the capital of Iran, lies between the rugged Alborz Mountains to the north and the vast Markazi Desert to the south, forming an urban landscape that has evolved through both its distinctive topography and a long historical process of expansion. The city now covers approximately 733 square kilometers, and its elevation varies sharply\u0026mdash;from about 900 meters in the southern plains to nearly 1,800 meters in the foothills. This steep gradient produces noticeable differences in climate and environmental conditions between the northern and southern districts, shaping patterns of development, vegetation, and air quality across the metropolis (Zarandi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Afarideh et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the geographic location of the Tehran metropolis that situated between 35\u0026deg; 35\u0026prime; to 35\u0026deg; 51\u0026prime; N latitude and 51\u0026deg; 4\u0026prime; to 51\u0026deg; 33\u0026prime; E longitude, Tehran is flanked by the plains of Shahriar and Varamin to the south and southwest, and bordered by steep mountain ranges in the north and east (Ramyar et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hydrologically, the Karaj and Jajroud rivers demarcate the western and eastern edges of the urban expanse, converging toward the southeastern drylands near the Namak Desert. Tehran\u0026rsquo;s evolution from a modest pre-Qajar town into Iran\u0026rsquo;s largest city has been marked by rapid demographic growth. Historical census data reflects a surge from approximately 1.56\u0026nbsp;million inhabitants in 1946 to 9.1\u0026nbsp;million residents by 2024, as reported by the Tehran Province Planning and Management Organization (Management and Planning Organization, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Iran Statistical Center, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Tehran contains over 56,000 hectares of green spaces, both within and surrounding its built-up areas. Presently, urban green space per capita stands at 16.33 m\u0026sup2;, complemented by 6.9 m\u0026sup2; per capita of parks and recreational zones distributed among 2,366 public spaces across the metropolis (Tehran City Statistical Yearbook, 2024). This distinctive interplay between natural topography, urban expansion, and ecological structures situates Tehran as a pivotal subject for research in metropolitan resilience, urban planning, and sustainability\u0026mdash;particularly within the context of rapidly transforming Middle Eastern megacities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch methodology\u003c/h3\u003e\n\u003cp\u003eThis research is conducted based on the stages and phases outlined briefly in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The model\u003c/p\u003e\u003cp\u003eand methods used are summarized below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePreparation of Land Use/Land Cover map\u003c/h3\u003e\n\u003cp\u003eTo construct a comprehensive Land Use/Land Cover (LU/LC) profile for Tehran spanning the period 1994 to 2024, a multi-temporal analysis of Landsat satellite datasets was performed using Google Earth Engine (GEE). This included imagery from the Thematic Mapper (TM) for 1994 and 2004, Enhanced Thematic Mapper Plus (ETM+) for 2014, and Operational Land Imager (OLI) for 2024, all accessed via the United States Geological Survey (USGS) Earth Explorer catalog integrated within GEE (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eImage processing information\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImage source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSensor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTraining sample number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKappa cofficient\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSGS Landsat 5 Level 2,\u0026nbsp;Collection 2,\u0026nbsp;Tier 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALOS DSM: Global 30m v4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSGS Landsat 5 Level 2,\u0026nbsp;Collection 2,\u0026nbsp;Tier 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e36525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALOS DSM: Global 30m v4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSGS Landsat 7 Level 2,\u0026nbsp;Collection 2,\u0026nbsp;Tier 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eETM+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALOS DSM: Global 30m v4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSGS Landsat 8 Level 2, Collection 2, Tier 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOLI/TIRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALOS DSM: Global 30m v4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe preprocessing stage was carried out entirely within the Google Earth Engine (GEE) environment. It began with automated atmospheric correction using the Surface Reflectance (SR) products supplied by the USGS. These datasets apply the LEDAPS algorithm for Landsat 5/7 and LaSRC for Landsat 8/9, helping to normalize pixel values and reduce the influence of atmospheric noise (Shrestha et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vermote et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Balist et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To further refine image quality, cloud masking was performed using the CFMask procedure, followed by temporal compositing to create median, cloud-free mosaics and geometric co-registration to maintain spatial alignment among scenes (Gorelick et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Together, these steps ensured that the multi-decadal imagery was consistent and reliable for subsequent machine-learning analysis.\u003c/p\u003e\u003cp\u003eFollowing preprocessing, an initial unsupervised classification using the Iterative Self-Organizing Data Analysis Technique (ISODATA) was applied within GEE to delineate preliminary LU/LC clusters. To refine feature extraction, index-based segmentation was performed leveraging multiple spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Bare Soil Index (BSI) and Normalized Difference Water Index (NDWI), facilitating the accurate identification of Regions of Interest (RoIs) for training samples across ecological and urban land features.\u003c/p\u003e\u003cp\u003eFollowing the preprocessing stage, supervised classification was carried out using machine learning techniques, with a particular focus on the Random Forest (RF) algorithm. This model, originally developed by Breiman (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), uses an ensemble of decision trees to assign pixels to land-cover categories based on their spectral, textural, and topographic attributes (Bilotta et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bill Donatien et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within the GEE JavaScript API, the RF model was trained using a balanced set of regions of interest (RoIs)\u0026mdash;typically ranging from 500 to 1,000 samples per class\u0026mdash;derived from high-resolution reference imagery. The model employed 100 decision trees, a minimum leaf size of five, and out-of-bag error estimation to fine-tune performance and minimize overfitting. This approach allowed for the clear separation of four dominant land-use categories: urban built-up areas, barren or unvegetated lands, vegetated green zones, and water bodies.\u003c/p\u003e\u003cp\u003eClassification outputs were validated through a protocol, cross-referencing with high-resolution Google Earth imagery, geolocated GPS checkpoints, and existing LU/LC maps for Tehran. Accuracy assessments, including overall accuracy, user's and producer's accuracies, and the Kappa coefficient, were computed using GEE's built-in confusion matrix tools. Kappa values exceeding 0.85 indicated strong agreement between the machine learning-derived maps and ground truth data, validating the robustness of the GEE-based workflow (Tesfaye et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shoja et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This section details the modeling of land use and land cover changes within the Tehran metropolitan area. The analysis for the years 1994, 2004, 2014, and 2024 was conducted using the Google Earth Engine and a machine learning approach.\u003c/p\u003e\n\u003ch3\u003eCarbon Reservoirs and Soil Organic Carbon Estimation\u003c/h3\u003e\n\u003cp\u003eTo investigate the spatial distribution of soil organic carbon (SOC) reservoirs across Tehran\u0026rsquo;s urban landscape, a total of sixty-six soil samples were systematically collected from various public parks and green spaces, as depicted in the accompanying Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These samples served as the foundation for generating a detailed SOC mapping layer.\u003c/p\u003e\u003cp\u003eThe organic carbon content of each sample was quantified under controlled laboratory conditions and expressed as mass per unit surface area (tons per hectare). SOC content was calculated as Eq.\u0026nbsp;1 (Yu et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e):\u003c/p\u003e\u003cp\u003eSOC (ton/ha) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\%OC*BD*D}{100}\\)\u003c/span\u003e\u003c/span\u003e*10,000 m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(1\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003e%OC\u003c/b\u003e refers to the percentage of organic carbon in the soil sample,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e denotes the bulk density of the soil (measured in Mg/m\u0026sup2;),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eD\u003c/b\u003e represents soil depth (in millimeters).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis equation ensures the integration of both compositional and physical soil characteristics, enabling precise estimation of carbon stocks within the surface soil layer. The derived SOC values contribute to understanding the carbon sequestration capacity of urban green spaces and their potential role in climate mitigation strategies.\u003c/p\u003e\n\u003ch3\u003eCarbon sequestration and storage ecosystem service model\u003c/h3\u003e\n\u003cp\u003eTerrestrial carbon storage potential is predominantly determined by four primary reservoirs: above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter. The InVEST carbon sequestration and storage model quantifies carbon stored within these pools based on user-supplied Land Use/Land Cover (LU/LC) maps and classifications.\u003c/p\u003e\u003cp\u003eAbove-ground biomass comprises all living vegetation above the soil surface, including stems, branches, and leaves. Below-ground biomass consists of living root systems. Soil organic carbon, representing the most substantial terrestrial carbon reservoir, reflects the organic fraction of soil. Dead organic matter includes decomposing litter, fallen leaves, and standing deadwood.\u003c/p\u003e\u003cp\u003eLeveraging spatial land cover data, the model estimates the net carbon accumulation over time and its monetized ecosystem value, accounting for changes in land cover types and carbon content per reservoir. However, it operates under simplifying assumptions, such as a linear rate of sequestration, a fixed discount rate, and exclusion of biophysical parameters like photosynthetic intensity or soil microbial activity. These omissions may constrain ecological accuracy.\u003c/p\u003e\u003cp\u003eRaster-based LU/LC inputs such as forest, cropland, and pasture enable pixel-level estimation of carbon storage density. The model outputs include: (1) total carbon stock, (2) net carbon sequestration, (3) economic valuation, and (4) aggregated raster maps. Accuracy improves when data on multiple reservoirs are available for each LU/LC class.\u003c/p\u003e\u003cp\u003eThe model compares current and future landscape scenarios to compute sequestration dynamics, producing per-pixel change maps that reflect carbon stock fluctuations and their associated societal valuation.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eModel Equations\u003c/h2\u003e\u003cp\u003eCarbon inventory estimation follows established carbon accounting principles (Ortas et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The annual change in carbon stock across all LU/LC categories is calculated as Eq.\u0026nbsp;2:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varDelta\\:\\complement\\:}_{AFOLU\\:=}{\\varDelta\\:\\complement\\:}_{FL}+{\\varDelta\\:\\complement\\:}_{CL}+{\\varDelta\\:\\complement\\:}_{GL}+{\\varDelta\\:\\complement\\:}_{WL}+{\\varDelta\\:\\complement\\:}_{SL}+{\\varDelta\\:\\complement\\:}_{OL}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eΔC\u0026thinsp;=\u0026thinsp;Total annual carbon change\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAFOLU\u0026thinsp;=\u0026thinsp;Agriculture, Forestry, and Other Land Uses\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFL, CL, GL, WL, SL, OL\u0026thinsp;=\u0026thinsp;Land use categories: Forest, Cultivation, Grassland, Wetland, Settlement, and Others\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCarbon change per LU/LC type is determined via reservoir-level aggregation is calculated as Eq.\u0026nbsp;3:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\varDelta\\:\\complement\\:}_{LUi}={\\varDelta\\:\\complement\\:}_{AB}+{\\varDelta\\:\\complement\\:}_{BB}+{\\varDelta\\:\\complement\\:}_{DW}+{\\varDelta\\:\\complement\\:}_{LI}+{\\varDelta\\:\\complement\\:}_{SO}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAB\u0026thinsp;=\u0026thinsp;Above-ground biomass\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBB\u0026thinsp;=\u0026thinsp;Below-ground biomass\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDW\u0026thinsp;=\u0026thinsp;Dead wood\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLI\u0026thinsp;=\u0026thinsp;Leaf litter\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSO\u0026thinsp;=\u0026thinsp;Soil organic matter\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eNet carbon sequestration across a temporal span is calculated using the inventory-difference technique as Eq.\u0026nbsp;4:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:\\complement\\:=\\frac{\\left({\\complement\\:}_{t2}+{\\complement\\:}_{t1}\\right)}{\\left({t}_{2}-{t}_{1}\\right)}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eΔC\u0026thinsp;=\u0026thinsp;Annual carbon change (tons/year)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCₜ₁, Cₜ₂ = Carbon inventory at the start and end of the period\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRequired Input Data\u003c/h3\u003e\n\u003cp\u003eAll spatial datasets must be in projected coordinate systems and use meters for linear units.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCurrent LU/LC Raster: A geospatial raster where each pixel is assigned an integer representing a distinct LU/LC class. These codes must match entries in the Carbon Pool table.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReference Calendar Year: Indicates the temporal scope of the current LU/LC data for valuation and sequestration calculation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCarbon Reservoir CSV Table: Contains carbon densities for four reservoirs for each LU/LC class. If certain data are unavailable, placeholder zeroes may be used (Table\u0026nbsp;2).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCSV Table\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColumn\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUcode\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnique integer per land cover class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInteger\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec_above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbove-ground biomass density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMg/hectare\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec_below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBelow-ground biomass density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMg/hectare\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec_soil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil organic carbon density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMg/hectare\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ec_dead\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDead organic matter density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMg/hectare\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor valuation purposes, a base price of US\u003cspan\u003e$\u003c/span\u003e35 per tonne of carbon was chosen. This figure was chosen based on average global values ​​for carbon and was consistent with previous studies in developing countries (Goulder and Williams., 2012). A discount rate of 2% (Rennert \u0026amp; Kingdon., 2019) was used to account for the time value of money as well as perceptions of intergenerational equity (i.e., the value of money today compared to the future). It was also assumed that the price of a unit of carbon would increase by 3% annually (Pache et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u0026mdash;an assumption adjusted for global trends of increasing carbon regulations, the growth of carbon markets, and likely inflation. With this set of assumptions, the economic value of carbon storage is reflected in a way that takes into account both current market conditions and future expectations.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLand use/Land cover change\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a visual representation of land use and land cover (LU/LC) dynamics across the study period from 1994 to 2024.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides a quantitative breakdown of LU/LC transitions over four decadal intervals. During this thirty-year period, green spaces in Tehran experienced a noticeable decline\u0026mdash;from 14.19% of the total urban area in 1994 to 11.87% in 2024. This reduction was more pronounced in the initial decades: approximately 700 hectares were lost between 1994 and 2004, followed by a similar loss over the second ten-year interval. However, in the final decade (2014\u0026ndash;2024), the rate of decline slowed considerably, with green space reduction amounting to only 10 hectares. These findings suggest that the pressure on green spaces was highest during the early phases of urban expansion.\u003c/p\u003e\u003cp\u003eBarren lands, primarily located in peri-urban zones and interstitial spaces with minimal vegetation, also diminished considerably over the study period. These areas were progressively repurposed to accommodate urban infrastructure, becoming the principal source for land conversion. Notably, approximately 7,000 hectares of barren land underwent transition to built-up uses between 1994 and 2004. This figure decreased to 4,000 hectares between 2004 and 2014, and slightly increased to 4,300 hectares in the final period, indicating continued but gradually tapering urban land demand.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand use changes in Tehran city from1994-2024\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1994 ha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2004 ha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2014 ha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2024 ha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater Bodies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreen Areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilt-Up Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e39726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e64.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBare land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e61345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e61345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUrban expansion into previously undeveloped or ecologically functional land has been the predominant driver of LU/LC transformation. Built-Up areas expanded by approximately 7,500 hectares in the first decade (1994\u0026ndash;2004), followed by 5,000 hectares in the second, and 4,000 hectares in the final ten-year interval. Apart from minor allocations of land to water bodies, most land conversion activities drew from barren lands initially, with increasing encroachment into green areas\u0026mdash;particularly private gardens and semi-natural zones\u0026mdash;in later years.\u003c/p\u003e\u003cp\u003eThese patterns reveal the cumulative impact of rapid urbanization on natural and semi-natural landscapes, highlighting the need for robust land management strategies to safeguard remaining green infrastructure in Tehran metropolis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCarbon sequestration model\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the spatial distribution of soil organic carbon (SOC) across the study area, modeled as a function of soil depth, bulk density, and organic carbon percentage. These input parameters were derived from empirical data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCarbon Reservoirs and Land Cover Typologies\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e outlines the estimated carbon densities within four principal carbon reservoirs\u0026mdash;above-ground biomass, below-ground biomass, soil organic matter, and dead organic material\u0026mdash;across distinct land use/land cover (LU/LC) classes. Each value is expressed in metric tons per hectare (Mg/ha).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCarbon Pool Densities per LULC Class (Mg/hectare)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLUcode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLU/LC class\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC_above (Mg/ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC_below (Mg/ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC_soil (Mg/ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC_dead (Mg/ha)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGreen area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuilt-up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBareland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe outputs of the InVEST carbon storage and sequestration model are presented in raster format, with each pixel representing a cell-specific carbon stock expressed in Mg/cell. These maps reveal the spatial variation in carbon sequestration potential throughout the Tehran metropolis. Values ranged from 0.45 Mg/cell at the lower end, typically associated with barren or impervious surfaces, to 7.119 Mg/cell in areas with dense vegetation and high organic soil content. Comparative analysis of the carbon maps highlights areas of significant carbon accumulation versus regions with minimal sequestration capacity. These spatial patterns are crucial for identifying priority zones for conservation and strategic urban planning, especially in the context of enhancing urban carbon sinks (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSpatiotemporal Variations in Carbon Sequestration\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the dynamic changes in carbon sequestration across four distinct intervals between 1994 and 2024. The quantified values\u0026mdash;expressed in metric tons\u0026mdash;reflect both positive and negative fluctuations. Positive values denote net carbon uptake through vegetative or ecological restoration processes, whereas negative values indicate carbon loss attributable to anthropogenic activities such as deforestation, land conversion, or urban expansion. Spatial distribution patterns of carbon change closely correspond with significant alterations in land use and land cover. Areas exhibiting the most extreme values\u0026mdash;either positive or negative\u0026mdash;highlight regions of pronounced ecological transition. As demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, green-shaded zones represent enhanced carbon sequestration and storage, while red-shaded areas reveal net reductions in stored carbon due to environmental degradation. This analysis provides critical insight into the influence of land management practices on atmospheric carbon dynamics and offers an evidence-based foundation for targeted climate mitigation strategies at the regional scale.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eChanges in the Economic Value of Carbon Sequestration\u003c/h2\u003e\u003cp\u003eAs mentioned in the methodology section, the economic valuation of carbon stocks was based on \u003cspan\u003e$\u003c/span\u003e35 per ton, which is consistent with regional conditions and the global average for carbon markets. This value was considered to be the midpoint between the highly variable global market prices\u0026mdash;from over \u003cspan\u003e$\u003c/span\u003e80 in the formal EU markets to less than \u003cspan\u003e$\u003c/span\u003e1 in voluntary markets\u0026mdash;to maintain comparability with international studies. Also, to account for the time dimension of carbon value, a discount rate of 2% and an annual growth rate of 3% were used to reflect changes in economic value over the assessment period (Goulder \u0026amp; Williams, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pache et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the spatial distribution of changes in carbon-related economic value across the study intervals. The map highlights areas where the monetary value of carbon stocks has fluctuated due to ecological transitions and land-use dynamics. Purple-shaded regions denote a decline in economic value, either from reduced carbon storage or complete carbon loss. Across the landscape, these changes span a spectrum between USD \u0026minus;\u0026thinsp;181 and USD\u0026thinsp;+\u0026thinsp;181, reflecting variations in sequestration performance and ecosystem service delivery.\u003c/p\u003e\u003cp\u003eThis spatially explicit evaluation provides crucial insight into the interplay between environmental management and carbon market economics. Despite using an economic valuation approach to quantitatively compare changes in ecosystem services, it should be noted that monetizing these services is a controversial issue and cannot replace broader environmental and social values. As G\u0026oacute;mez-Baggethun and Ruiz-P\u0026eacute;rez (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) have pointed out, linking ecosystem management to market logic may ignore non-economic aspects of sustainability. Therefore, the results of this study should be interpreted within a balanced and multidimensional framework in which economic value is only one dimension of environmental decision-making.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImpacts of Land Use Dynamics on Carbon Loss and Economic Valuation\u003c/h2\u003e\u003cp\u003eOver the three-decade span from 1994 to 2024, extensive land use transformations within Tehran have resulted in substantial carbon emissions. Specifically, the release of \u0026gt;\u0026thinsp;280,000 metric tonnes of carbon, corresponding to an economic loss of approximately USD 7,600,000, has been recorded. During the 2004\u0026ndash;2014 interval, ~\u0026thinsp;177,000 tonnes of carbon were emitted, translating into USD\u0026thinsp;\u0026gt;\u0026thinsp;4,800,000 in foregone sequestration value. The period from 2014 to 2024 saw an additional loss of 151,000 tons, valued at \u0026gt;\u0026thinsp;USD 4,100,000 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese figures underscore the profound influence of unregulated urban expansion and construction activities, which have systematically encroached upon green spaces and previously undeveloped lands. Despite municipal initiatives aimed at restoring urban vegetation and promoting green infrastructure, the scale and intensity of built-area development have largely offset potential ecological gains.\u003c/p\u003e\u003cp\u003eThe data presented herein reinforces the urgency of implementing land use policies that prioritize carbon-sensitive planning and integrate ecosystem service valuation into urban development frameworks. Without corrective action, the economic and environmental costs associated with unchecked expansion may continue to rise, undermining Tehran\u0026rsquo;s climate resilience strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDecadal Trends in Carbon Sequestration and Its Economic Valuation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDescription/year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1994\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2004\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Sequestered Carbon (1000 Mg of C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChange in Carbon (1000 Mg of C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNet present value (USD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-7626000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-4814000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-4112000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of this study, compared with similar studies in other cities around the world, show that the reduction of green spaces and the subsequent decline in carbon sequestration and storage capacity is a global phenomenon, but its severity is more significant in Tehran. In this study, the reduction in the share of green spaces from 14.19% to 11.87% over three decades (1994 to 2024) caused a significant decrease in carbon storage and an economic loss of more than \u003cspan\u003e$\u003c/span\u003e7.6\u0026nbsp;million. In Shenzhen, China, Wang et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used the CASA and InVEST models to show that the total carbon storage of green spaces between 2008 and 2022 was in the range of 7.17 to 7.42\u0026nbsp;million tons, with significant spatial variations between the western and eastern parts of the city; while the InVEST model is less accurate in built-up areas. In Noida, India, Sharma et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported using multitemporal satellite imagery and the InVEST model that the rapid expansion of construction between 2011 and 2019 significantly reduced the carbon sequestration capacity and its economic value in the projected scenario of 2027. In Beijing, Cao et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed that the carbon density of urban green spaces is strongly dependent on the vegetation structure, and increasing the density of trees and shrubs improves the accuracy of modeling and carbon estimation. Overall, while cities such as Shenzhen, Noida, and Beijing have also faced a decrease or fluctuation in carbon storage due to urban development, the intensity of the conversion of wasteland to construction and the gradual encroachment of development into private gardens and semi-natural spaces in Tehran, along with heavy economic losses, indicate a more critical situation for this city. The findings of this study, in line with global literature, emphasize the need for careful land use policymaking and the development of green infrastructure to restore urban carbon sinks, especially in semi-arid regions.\u003c/p\u003e\u003cp\u003eMoreover, the application of the InVEST model allowed for a spatially explicit valuation of carbon reservoirs and highlighted zones where conservation or restoration efforts could yield tangible climate benefits. The sharp decline in net present value of carbon stocks\u0026mdash;exceeding USD 7.6\u0026nbsp;million over the study period\u0026mdash;illustrates how development-driven carbon emissions erode not only environmental stability but also economic opportunities tied to ecosystem services. The consistency of carbon loss in areas undergoing artificial land conversion further reinforces the importance of strategic planning and data-driven environmental management to balance urban growth with climate mitigation objectives.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a comprehensive understanding of the dynamics of land use change across the Tehran urban region over a 30-year period. The accelerated decline of green and barren lands, particularly between 1994 and 2014, coincided with rapid population growth, rural-to-urban migration, and intense demand for urban expansion. The geographical constraints imposed by mountainous terrain, limited land availability, and saturation of development capacity have emerged as critical challenges for horizontal growth in recent years. Consequently, urban vertical expansion\u0026mdash;through high-rise construction\u0026mdash;has gained prominence as a compensatory strategy. Moreover, demographic shifts and economic pressures have further impacted the city\u0026rsquo;s developmental trajectory, with slower population growth and fiscal constraints contributing to reduced construction intensity.\u003c/p\u003e\u003cp\u003eIn parallel, the increasing prioritization of urban green spaces reflects a strategic pivot toward environmental sustainability. Green infrastructures not only mitigate air pollution and heat island effects, but they also enhance overall urban livability. A key contribution of this study lies in its application of the InVEST carbon sequestration and storage model, which spatially quantifies ecosystem services and highlights the value of vegetated landscapes. These patterns reveal the cumulative impact of rapid urbanization on natural and semi-natural landscapes, highlighting the need for robust land management strategies to safeguard remaining green infrastructure in Tehran metropolis. The substantial decrease in carbon stock and its economic valuation underscore the pressing need for data-driven urban planning strategies that integrate climate resilience. As terrestrial ecosystems act as critical carbon reservoirs, their preservation plays a pivotal role in achieving sustainable development goals. Hence, the findings of this research can inform land use scenarios, policy interventions, and ecological restoration efforts that reinforce Tehran\u0026rsquo;s capacity to adapt to climate challenges and maintain ecosystem functionality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfarideh F, Ramasht MH, Mortyn G (2023) Air pollution and topography in Tehran. AUC Geogr 58(2):157\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14712/23361980.2023.12\u003c/span\u003e\u003cspan address=\"10.14712/23361980.2023.12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlibakhshi Z, Ahmadi M, Farajzadeh Asl M (2020) Modeling biophysical variables and land surface temperature using the GWR model: case study\u0026mdash;Tehran and its satellite cities. 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Urban Ecosyst 15:533\u0026ndash;544. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11252-012-0238-3\u003c/span\u003e\u003cspan address=\"10.1007/s11252-012-0238-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-environmental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"IJER","sideBox":"Learn more about [International Journal of Environmental Research](https://www.springer.com/journal/41742)","snPcode":"41742","submissionUrl":"https://www.editorialmanager.com/ijer/default2.asp...\n","title":"International Journal of Environmental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Carbon Storage, Ecosystem Services, Economic Valuation, Green Infrastructure, Land Use Change, Machine Learning, Tehran Metropolis","lastPublishedDoi":"10.21203/rs.3.rs-7656394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7656394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid growth of urbanization in Iranian metropolises, especially Tehran, has caused significant changes in land use and cover, and consequently, a decrease in ecosystem services, including carbon storage. This study aimed to model and value changes in carbon storage in Tehran's ecological metabolism over three decades (1994\u0026ndash;2024). Multi-temporal Landsat satellite images, advanced processing in Google Earth Engine, and the Random Forest algorithm were used for land use/cover classification. In addition, 66 urban soil samples were collected and analyzed to estimate soil organic carbon. The InVEST model was used to simulate changes in four main carbon pools (aboveground biomass, belowground biomass, soil, and dead organic matter). In addition, using economic valuation frameworks and global market reference rates, losses and benefits from carbon changes were calculated in the form of monetary indices. The results showed that over the study period, the area of ​​green spaces decreased from 14.19% in 1994 to 11.87% in 2024. These changes, along with the conversion of wasteland to construction, led to a significant reduction in carbon storage. Specifically, about 280,000 tonnes of carbon (equivalent to \u003cspan\u003e$\u003c/span\u003e7.62\u0026nbsp;million) were lost between 1994 and 2004, and more than 151,000 tonnes of carbon (equivalent to \u003cspan\u003e$\u003c/span\u003e4.11\u0026nbsp;million) between 2014 and 2024. Field-based models revealed several localized zones of carbon decline along with areas that could feasibly be restored. The results suggest that rapid urban growth and the steady loss of vegetation cover have greatly weakened the city\u0026rsquo;s capacity to retain carbon, and in turn, reduced its economic and ecological value. These observations highlight the importance of incorporating ecosystem service valuation into broader planning practices, ensuring that urban development does not undermine environmental integrity and contributes to climate resilience in large cities.\u003c/p\u003e","manuscriptTitle":"Urban Development Impacts on Ecosystem Services: Modeling and Valuation of Carbon Storage in the Tehran Metropolis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 01:42:14","doi":"10.21203/rs.3.rs-7656394/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-11-21T08:52:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T11:18:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T19:08:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Environmental Research","date":"2025-10-27T16:30:29+00:00","index":"","fulltext":""},{"type":"decision","content":"Major revisions","date":"2025-09-27T05:09:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-environmental-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"IJER","sideBox":"Learn more about [International Journal of Environmental Research](https://www.springer.com/journal/41742)","snPcode":"41742","submissionUrl":"https://www.editorialmanager.com/ijer/default2.asp...\n","title":"International Journal of Environmental Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f91632b1-c35c-49e5-98d8-f6786fde8bda","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-07T09:45:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 01:42:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7656394","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7656394","identity":"rs-7656394","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}