Introduction

Terrestrial ecosystem carbon storage is a vital ecological indicator, reflecting the state of ecosystem services, productivity, and climate change dynamics1,2. The sequestration of carbon in terrestrial ecosystems is crucial for climate change mitigation and forms a cornerstone of the global carbon cycle2,3. Land use and cover change (LUCC) significantly alters carbon stocks by reshaping ecosystem structure and function3,4,5, with these changes being closely tied to projected climate scenarios4,6. Understanding the terrestrial carbon cycle’s response to global climate change and land use alterations is a contemporary research priority. International agreements such as the Paris Agreement and the United Nations Sustainable Development Goals (SDGs) have underscored the urgent need for cities to adopt low-carbon development pathways7,8,9. Urbanization, in the face of ongoing climate change, has hastened shifts in land use, contributing to ecological degradation10. This degradation not only lessens vegetation productivity but also diminishes the carbon sink capacity of urban regions5,11, thereby intensifying the effects on carbon storage. Urban areas are generally characterized by lower soil carbon density, which amplifies the negative impacts of urbanization on carbon sequestration. However, biochar addition has emerged as an effective strategy for boosting soil carbon reserves12. Urban development not only depletes initial carbon stocks but also impairs the land’s long-term carbon sequestration potential13,14. While significant research has explored the relationship between urbanization and ecosystem function15,16,17, including modeling the aboveground biomass carbon loss due to urban expansion and assessing the carbon impact of urban land use in Seattle14,18, there is still a scarcity of studies that systematically examine the spatial and temporal effects of land cover changes on carbon dynamics in urban environments.

In 2020, China’s government announced the “carbon peak and carbon neutrality (CPCN)” target, which seeks to cap carbon emissions by 2030 and achieve carbon neutrality by 206019,20. Attaining these benchmarks hinges on the efficacy of land use planning that integrates carbon reduction objectives into strategic initiatives5,19. Extensive research has explored the spatial and temporal dynamics of carbon stocks in rapidly urbanizing regions under the framework of sustainable development and the CPCN mandate. Research has projected land cover and carbon stock transitions in Mexico through 20504, while other studies have simulated the repercussions of urban expansion on carbon stocks in the United Kingdom, indicating a decline over observed periods21. The integrated valuation of ecosystem services and trade-offs (InVEST) model has been instrumental in forecasting carbon stock impacts consequent to urban growth in Beijing, signaling a potentially swifter depletion of carbon reserves from 1990 to 203015. Models such as InVEST and future land use simulation (FLUS) have been harnessed to anticipate carbon stock modifications in Hainan Island’s coastal regions and the Chengdu-Chongqing urban cluster under prospective scenarios for 203022,23. These analyses are instrumental in formulating models and governance policies for urban conglomerations. Given that urban zones, though they represent a mere 2% of the Earth’s land, are responsible for a substantial 75% of global carbon emissions24, the adept administration of land use and carbon stocks is fundamental in navigating urban progression toward low-carbon paradigms. Effective land use and management could sequester a significant portion, estimated at 60–70%, of emitted carbon, under-scoring the imperative of scrutinizing land use and carbon stocks in crafting urban futures.

Shanghai, a global megacity, serves as a key economic driver in East Asia25. The acceleration of urbanization has led to a notable transformation, with natural landscapes including cultivated land, woodland, and wetlands being increasingly replaced by urban development’s17,21. This urban encroachment has caused a significant decrease in vegetation cover and a concurrent expansion of urban construction, which in turn has precipitated substantial declines in urban carbon stocks17,23. The ecological impact is especially significant in regions with swift urban growth, such as urban agglomerations and megacities2,26. In its endeavor to meet the CPCN targets, Shanghai confronts challenges arising from its extensive industrial activities, high consumption of energy and materials, and considerable emission of pollutants, all of which affect both urban and rural environments27. This scenario highlights a critical tension between environmental preservation and the need for sustainable and rapid economic growth intended to improve the quality of life for residents17,27. Moreover, current research reveals a gap in understanding the spatial dynamics of carbon stocks within urban conglomerations. Understanding Shanghai’s land use evolution is therefore crucial not only for the city’s own low-carbon transition but also for providing insights into sustainable urbanization strategies applicable to other mega-cities worldwide.

This study integrates the FLUS and InVEST models to simulate the spatial-temporal evolution of land use in Shanghai by 2030. It focuses on three development scenarios: natural trend scenario (NTS), ecological protection scenario (EPS), and high urbanization scenario (HUS), utilizing land use data from 2010, 2015, and 2020. The analysis includes an examination of changes in carbon stock during this period. The study aims to address the following scientific questions: (1) what are the spatial and temporal characteristics of land use differentiation in Shanghai from 2010 to 2020? (2) How does spatial and temporal differentiation in carbon stock respond to various scenarios of land use change in Shanghai? This study aims to fill the gap by analyzing the historical land use patterns of Shanghai and their impact on future urban ecosystem carbon storage, thereby contributing to the broader discussions on sustainable urbanization and carbon neutrality. Additionally, our research findings are expected to provide valuable insights for Shanghai’s low-carbon transition and serve as a reference for other megacities striving to balance urbanization with environmental sustainability.

Materials and methods

Study area

Shanghai, nestled on China’s eastern coast between 30°40′N to 31°53′N and 120°51′E to 122°12′E, occupies a strategic ___location that interfaces with the East China Sea and the Yellow Sea (Fig. 1). This geographic positioning has been instrumental in shaping Shanghai into a global epicenter for economic, financial, and cultural activities25. The city covers an area of approximately 6,341 km², with a topography that varies from sea level to 355 m in elevation, predominantly consisting of flat plains that stretch across the landscape. Shanghai’s subtropical monsoon climate is marked by four distinct seasons and an average annual temperature of 16 °C, which supports a wide array of economic activities, including agriculture27. Shanghai’s economy transcends agriculture, encompassing a diverse range of industries that have progressively supplanted agriculture’s traditional dominance as the city urbanizes. Notably, the burgeoning sectors of financial services, advanced manufacturing, and modern services have significantly revitalized Shanghai’s urban economy. The growth of these sectors not only fuels Shanghai’s economic expansion but also charts new trajectories and impetus for land-use transformation in its urbanization journey. With a population exceeding 24 million, Shanghai is not only China’s most populous city but also a melting pot of innovation and urban development. Its rapid growth has made it a showcase for the interplay between urbanization and environmental sustainability. The city’s diverse land use patterns, ranging from high-density urban centers to agricultural outskirts and protected natural areas, present a complex mosaic that is reflective of the trade-offs between development and conservation. Shanghai’s economy, one of the most dynamic globally, is underpinned by a robust industrial base, a thriving service sector, and a burgeoning high-tech industry. However, this economic vitality also brings environmental pressures, such as increased energy consumption and carbon emissions, which are central to the study’s focus on carbon neutrality. The city’s environmental challenges are further compounded by the effects of climate change, which include rising sea levels and changing precipitation patterns, posing risks to its coastal infrastructure and ecosystems. In response, Shanghai has been at the forefront of implementing green policies and investing in renewable energy, urban greening, and carbon capture initiatives, aligning with its commitment to the CPCN targets set by the Chinese government. Moreover, Shanghai’s multifaceted role extends beyond its regional influence, making it a pivotal player on the global stage. Its strategic ___location, coupled with its efforts to balance urban growth with environmental sustainability, positions Shanghai as a living laboratory for studying the complex dynamics of urban ecosystems and their implications for global carbon management.

Fig. 1
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Location and topography of the study area (U.S. Census Bureau California Open Data Portal, http://www.gis.ca.gov).

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Data sources

Utilizing ArcGIS 10.8, DEM data were merged, cropped, and processed to compute slope and slope direction (Table 1). Vector data encompassing railroads, highways (including national, provincial, highway and county roads) were analyzed using ArcGIS Euclidean distance to derive transportation accessibility factors. Additionally, ArcGIS was utilized to transform and interpolate raster data representing nature reserves, thereby identifying environmental constraints. Land use data28, climate factors, and socio-economic factors29,30 were standardized to the WGS_1984_UTM_48N coordinate system and resampled to a 30-meter resolution for integration into the FLUS model. ArcGIS computed land use transfer matrices and probabilities for each land use type between 2010 and 2015 and 2015–2020. This analytical approach facilitates an in-depth examination of historical land use changes and establishes foundational parameters for the FLUS model (Fig. 2).

Fig. 2
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Research framework.

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Table 1 Datasets used in this study.
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FLUS model

This study seeks to investigate the impact of rapid urbanization on regional carbon stocks by examining the spatial and temporal dynamics of land use types in Shanghai from 2010 to 2020. Using the FLUS model, the research aims to forecast the future distribution and quantities of land use types in Shanghai by 2030. Drawing upon methodologies observed in the Chengdu-Chongqing Economic Circle (CCUA)23, the study establishes three scenario models: NTS, HUS, and EPS. Each scenario model reflects varying rates of urban expansion and degrees of ecological protection. The accuracy of the FLUS model in predicting land use patterns in Shanghai was validated using 2015 land use data to simulate the 2020 scenario, achieving a Kappa coefficient of 92.84%. This validation underscores the model’s robust simulation capabilities and high accuracy in forecasting land use changes.

Based on the triennial land use data (2010, 2015, and 2020) and considering influential driving factors (Fig. 3), the study calculates the suitability probability for each land use type anticipated in 2030. This estimation leverages an artificial neural network integrated within the FLUS model, incorporating specific neighborhood factor parameters. Parameters include 0.7 for cultivated land, 0.6 for woodland, 0.4 for grassland, 0.2 for water, 1.0 for construction land, and 0.1 for unutilized land. Detailed adjustments for different scenarios are delineated in Table 2. In conclusion, this research demonstrates the FLUS model’s capacity to accurately simulate and predict land use changes in Shanghai. These predictive capabilities are essential for informing sustainable urban planning strategies and environmental management policies that harmonize economic development with ecological conservation.

Table 2 Land use transfer cost matrix under three scenarios.
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InVEST model

In this study, the InVEST model was utilized to estimate carbon stocks in Shanghai, focusing on current land use classifications. The estimation encompassed four distinct carbon pools: above-ground carbon stock, below-ground root carbon stock, soil carbon stock, and dead organic matter carbon stock. These pools were integral in calculating both the total regional carbon stock and its spatial-temporal distribution characteristics31. The formula used for calculating the carbon stock is articulated as follows:

$${{\text{C}}_{\text{i}}} = {{\text{C}}_{{\text{above}}}} + {{\text{C}}_{{\text{below}}}} + {{\text{C}}_{{\text{soil}}}} + {{\text{C}}_{{\text{dead}}}}$$
(1)
$${{\text{C}}_{{\text{total}}}} = \mathop \sum \limits_{{\text{i}}=1}^{{\text{n}}} {{\text{C}}_{\text{i}}} \times {{\text{S}}_{\text{i}}}$$
(2)

Where \(\:{C}_{i}\:\)is the overall carbon density; \(\:{C}_{above}\), \(\:{C}_{below}\), \(\:{C}_{soil}\), and \(\:{C}_{dead}\) are the density of aboveground biological carbon, underground biological carbon, soil organic carbon, and dead organic carbon, respectively (Table 3); \(\:{C}_{total}\:\)is the total carbon storage; and \(\:{S}_{i}\) is the area of land use type i. In this study, when calculating carbon storage, considering the constraints of model parameters and the characteristics of water bodies and unused land (including undeveloped industrial areas, urban construction land, coastal tidal flats, and sandy areas), we set the carbon storage values for these types of land use to 0.

Table 3 Reference values of land use carbon density in the study area (Mg/hm2).
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Fig. 3
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Drivers of Shanghai’s expansion simulation.

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Results

Analysis of land use change and scenario prediction in Shanghai

Analyzing the changes in land use in Shanghai from 2010 to 2020, this study identified the patterns of land use change (Fig. 4). The study found that the largest area of land converted over the past decade was construction land, totaling 360.56 km², of which 91.04% was transformed into construction land. The remaining small portion of land, accounting for approximately 8.96% of the total converted construction land area, was restored to agricultural use through land reclamation, restoration, and a series of agricultural planning adjustments. Apart from construction land, which saw a 6% annual increase, the areas of cultivated land, grassland, woodland, water, and unutilized land all exhibited gradual decreases (Fig. 5). These findings indicate that rapid urbanization, driving urban development, has led to significant encroachment upon surrounding cultivated land, expanding further into the periphery.

Fig. 4
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Land use spatial pattern and change in Shanghai from 2010 to 2020. (a–c) represent the land use types patterns of Shanghai in 2010, 2015, and 2020, respectively.

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Fig. 5
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Characteristics of the amount of change of land use transfer in Shanghai from 2010 to 2020.

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By integrating land use data and simulation constraints from 2010, 2015, and 2020, the study reveals that Shanghai’s predominant land use structure continues to be characterized by cultivated land and construction land, aligning with the city’s urbanization trajectory and historical land use change patterns (Fig. 6).

Fig. 6
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Land use simulation with multiple scenarios in 2030: land use in 2020 (a), natural trend scenario (b), high Urbanization scenario (c), and ecological protection scenario (d).

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The predictions for Shanghai’s 2030 land use changes (Fig. 7) indicate a decrease in cultivated land area under both the NTS and HUS scenarios (Fig. 7a, b), with the most significant reduction observed in the HUS scenario. Cultivated land is predominantly converted to construction land. Conversely, under the EPS scenario, cultivated land is notably preserved, showing an expansion of 93.15 km² (Fig. 7c). Across all scenarios, changes in grassland, water, and unused land mirror those in woodland, demonstrating relatively minor decreases. Furthermore, construction land exhibits varying degrees of expansion in all scenarios, with the HUS scenario showing the largest increase to 2619.60 km2, followed closely by the NTS scenario at 2553.31 km2. In contrast, the EPS scenario records the smallest and slowest expansion of construction land at 2378.84 km2, owing to protective measures for cultivated land and woodland, thereby reducing conversion pressures. These findings underscore the pivotal role of urbanization in Shanghai’s future development trajectory. The study emphasizes the necessity for strategic land use planning to balance urban growth with environmental conservation objectives effectively.

Fig. 7
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Land use changes in 2030 under three different scenarios. Natural trend scenario (a); high urbanization scenario (b); and ecological protection scenario (c).

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The Spatiotemporal differentiation characteristics of carbon storage

The total carbon stock in Shanghai exhibited a consistent annual decrease from 2010 to 2020, resulting in a cumulative loss of 165.06 × 104 Mg (Table 4). The year-on-year rate of decline in 2020 compared to 2010 was 4.9%. Apart from construction land, which consistently increased in carbon stock, cultivated land, woodland, grassland, and unused land all demonstrated decreasing carbon stocks. Overall, the carbon losses outweighed gains during this period. Spatially, the carbon stock patterns in Shanghai remained highly consistent across 2010, 2015, and 2020, with localized variations (Fig. 8). Notably, areas such as Chongming Island and the eastern part of Changxing Island showed significant increases in carbon stocks (Fig. 8d). These spatial changes highlight localized dynamics amidst the broader trend of declining carbon stocks. In conclusion, Shanghai experienced a substantial overall reduction in carbon stocks over the decade, driven by declines in various land types despite localized gains in specific regions. These findings underscore the complex interplay between urban development and carbon dynamics in megacity environments.

Table 4 Carbon storage of land use from 2010 to 2030 (104 Mg).
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Fig. 8
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Spatial and temporal changes in carbon storage in Shanghai from 2010 to 2020. (a–c) represents the carbon storage patterns of Shanghai in 2010, 2015, and 2020, respectively. (d) The changes carbon storage from 2010 to 2020.

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Response of carbon stocks to different scenarios of land use change

Carbon stock analysis based on different scenarios of land use changes in Shanghai reveals distinct spatial distribution characteristics of carbon stock projected for 2030, which diverge minimally from historical trends (Fig. 9). Under the NTS scenario, Shanghai’s carbon stock in 2030 is estimated at 48.33 × 106 Mg, marking a reduction of 83.15 × 106 Mg compared to 2020 (Figs. 8c and 9a; Table 4). Conversely, under HUS, the carbon stock is projected to decrease to 47.67 × 106 Mg by 2030, which is 149.29 × 106Mg lower than in 2020 (Figs. 8c and 9b). In contrast, under EPS, the carbon stock is anticipated to rise to 50.06 × 106 Mg by 2030, representing an increase of 89.95 × 106 Mg from 2020 levels (Figs. 8c and 9c). Overall, the carbon stock trends show declines under NTS and HUS scenarios, while EPS demonstrates a mitigating effect on the reduction, indicating its favorable impact on carbon stock enhancement. EPS involves a strategy where urbanization slows, and the expansion of construction land decreases marginally by 0.02% over the next decade. This ecological protection approach enhances carbon sequestration capacity, suggesting that the study area optimizes urban development spatial patterns to bolster carbon storage. Conversely, under NTS and HUS scenarios, rapid urbanization and expansive construction land growth severely diminish regional carbon stocks and degrade ecosystem services. Particularly under HUS, prioritization of economic development and rapid urbanization without adequate land use restrictions leads to excessive construction land expansion, encroaching on cultivated land and substantially reducing regional carbon stocks (Fig. 10).

Fig. 9
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Carbon storage distribution under different future scenarios in 2030. Natural trend scenario (a); high urbanization scenario (b); and ecological protection scenario (c).

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Fig. 10
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Carbon stock changes in different land types in Shanghai under three scenarios.

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Discussion

Urbanization dominates land use changes in Shanghai

Rapid urbanization has exerted a profound influence on the land use structure of Shanghai. Driven by population growth, economic expansion, and social progression10,27,32, the city has witnessed a marked transformation in its land use configuration. There has been a continuous decline in cultivated land and ecological areas such as woodlands and grasslands, while urban construction land has extended progressively from the city center. This pattern echoes the urbanization trends seen in other metropolitan areas like Beijing33 and Shenzhen34, characterized by a reduction in cultivated land and an increase in built-up areas. However, there may be significant differences in model parameters such as community factors and land use transfer costs between different cities, which can affect the generalizability of research results. At the same time, urban planning and policies play an important guiding and constraining role in land use change, but the FLUS model may not be sensitive enough to these factors. For example, the model may not be able to timely and accurately reflect adjustments to the nature of land use and development intensity in certain areas in urban planning, as well as policy guidance and restrictions on land use. Shanghai’s strategic role as an economic engine in East Asia, coupled with its economic liberalization and integration into global markets27, has led to multitudinous migration and investment. This influx has fueled the growth of commercial, industrial, and residential sectors, accelerating the transition in land use types. The intensification of urbanization has heightened land demand, extending urban development into suburban and rural zones, and increasing the land designated for construction35. Additionally, the implementation of large-scale infrastructure projects, a byproduct of urbanization, has necessitated considerable land allocation, thus inducing substantial shifts in land use27,32. Urban renewal and redevelopment have emerged as pivotal in enhancing land use efficiency. The strategic re-planning and revitalization of established urban areas has had a notable impact on the overall land use structure. For instance, in an effort to improve land use efficiency, the London municipal government initiated extensive urban renewal and redevelopment projects in the London Docklands area. However, these initiatives led to the disruption of community networks and the displacement of low-income families36. The New York High Line Park project, by transforming an abandoned elevated railway into an urban park, stimulated real estate development and commercial activities in the surrounding areas, thereby increasing land use efficiency37. In China, efforts to optimize land use ecological efficiency in the Loess Plateau urban agglomeration and promote sustainable urban block development in the Shenzhen metropolitan area have significantly enhanced local land use efficiency. However, these initiatives have also resulted in the replacement of historical and cultural landscapes with modern architectural structures36,38. Urbanization in Shanghai, therefore, not only remodels the land use landscape but also incites targeted interventions focused on optimizing land use and ensuring sustainable development. Furthermore, the present study utilizes the FLUS model to perform intricate simulations and forecasts of Shanghai’s land use dynamics, offering insights into future transformations. Aligning with previous studies18,39, the model reveals variations in the expansion of construction land under different scenarios. Notably, the EPS minimizes the encroachment of construction land on agricultural and forested areas, underscoring the potential of ecological policies to mitigate urban sprawl34. The analysis multi scenarios indicates that well-calibrated ecological protection measures can substantially slow the conversion of land for construction purposes, thereby preserving vital ecosystems and elevating the regional ecological environment’s health10,23,40. Our research findings highlight the crucial role that decision-makers can play in achieving a balance between economic progress and environmental protection during rapid urbanization by scientifically conducting land use planning and implementing strong ecological policies under an ecologically prioritized development scenario.

Response of carbon stocks to land use change

Between 2010 and 2020, Shanghai’s overall carbon stock witnessed a consistent decline across diverse land types, with the notable exception of construction land, where an annual increase in carbon stock was documented. This pattern aligns with the broader trends reported in urbanization research, which have consistently noted the adverse effects of urban sprawl on carbon stocks14,18. It is acknowledged, however, that a judicious expansion of construction land could enhance the capacity of carbon sinks40. Global urban growth has been identified as a significant contributor to the loss of biomass carbon2,3, and urbanization in specific regions, such as the Seattle metropolitan area, has been associated with similar declines in carbon stocks14. In Shanghai, rapid urbanization has been correlated with a marked reduction in carbon stocks, frequently due to the conversion of significant areas of cultivated land3,32. Cultivated land, woodland, and grassland are recognized as vital carbon sinks, and their diminishment significantly affects the regional carbon balance14,41. Notably, the expansive presence of cultivated land contributes to over 90% of the total carbon stock, underscoring the influence of land types with large areas and substantial carbon density on overall carbon reserves42. Consequently, the conservation of cultivated land is paramount for maintaining Shanghai’s future capacity for carbon storage. It is evident that without a concerted focus on ecological protection, reversing the decline in carbon stocks remains a formidable challenge10,24,25. This underscores an urgent need for the strengthening of ecological conservation efforts.

Under varying development scenarios, carbon stocks were observed to decrease notably under the NTS and HUS, whereas an increase in carbon stocks was recorded under the EPS23,24. This contrast suggests that the implementation of ecological conservation policies plays a crucial role in stemming the decline in carbon stocks. By curtailing the sprawl of construction land and preserving existing arable and forested regions, these policies reinforce the region’s carbon reserves. It demonstrates that sustainable and balanced regional development is attainable through meticulous land use planning and robust ecological protections43, concurrently fostering economic growth2,24. This result is consistent with the principle of “ecological compensation,” which means to regulate the economic interest relationship between ecological protectors, beneficiaries, and destroyers by providing compensation to ecological protectors or charging fees to environmental destroyers, in order to promote the protection and sustainable development of the ecological environment32,44. Collectively, our study emphasizes the indispensable nature of scientifically informed land use planning and ecological preservation measures in the context of rapid urbanization.

Limitations and perspectives

The land use data examined in this study, collected in the years 2010, 2015, and 2020, reflect a brief period that may not encapsulate the full spectrum of changes observable over longer timeframes. Utilizing the FLUS model, the study predicts land use transformations, capitalizing on its capability to simulate dynamics of land use change. Nonetheless, the model’s forecasts are potentially subject to biases stemming from the configuration of parameters and the constraints of the simulation scenario. To augment the precision and relevance of the study’s conclusions, future research ought to integrate high-precision land use data that covers a broader chronological span. Additionally, there is a need for refinement in the FLUS model’s parameter settings and simulation conditions. Engaging in comparative analyses with alternative land use change models is indispensable for reinforcing the dependability and robustness of the model’s predictive outcomes. Establishing long-term monitoring and feedback mechanisms is vital for the ongoing surveillance of evolving land use patterns and the assessment of ecosystem services. Such a process is instrumental in providing policymakers with prompt feedback, thereby facilitating the recalibration of policies to confront the challenges of rapid urbanization and the pressing need more effectively for ecological conservation. Addressing the limitations and expanding the scope of research will foster a more thorough and nuanced comprehension of the complex mechanisms underlying land use change and their ecological and environmental consequences. These enhancements are expected to yield a more robust scientific foundation, which is essential for the advancement of sustainable urban development strategies.

Conclusions

Using a hybrid approach integrating FLUS and InVEST models, this study aims to predict Shanghai’s land use patterns for 2030 across three distinct scenarios. Leveraging land use data from 2010, 2015, and 2020, the analysis focuses on understanding the impact of these scenarios on Shanghai’s carbon stock dynamics. The conclusions of the study are as follows:

  1. (1)

    The FLUS model demonstrates exceptional accuracy in land use types in Shanghai, achieving a Kappa coefficient of 92.84%. This highlights its reliability for forecasting future spatial patterns of land use.

  2. (2)

    Cultivated land represents one of the predominant land use types in Shanghai. However, from 2010 to 2020, there was a significant decline in cultivated land area, primarily due to its conversion into construction land, resulting in a 6% increase in construction land area. This trend indicates rapid urbanization encroaching on cultivated land and expanding towards the periphery. The simulation across three scenarios highlights that the HUS scenario projects the most extensive construction land expansion, while the EPS scenario shows the least. This suggests that ecological protection policies can moderately mitigate construction land expansion and cultivated land reduction.

  3. (3)

    Carbon stocks in Shanghai declined annually from 2010 to 2020, with a cumulative loss of 165.06 × 104 Mg. This decline was primarily attributed to construction land expansion, leading to reduced carbon stocks in cultivated land, woodland, and grassland. Projections for 2030 indicate an increase in carbon stocks only under the EPS scenario, while continuing significant decline under the NTS and HUS scenarios. This underscores that ecological conservation strategies can alleviate the reduction in carbon stocks and enhance sequestration capacity. Conversely, rapid urbanization and inadequate land use controls may diminish regional carbon stocks and degrade ecosystem services.