The UN's Sustainable Development Goal 11 aims for sustainable cities and human settlements. Rapid urban land expansion due to population growth, however, poses significant challenges, including air pollution and resource depletion, particularly in developing nations. This study addresses the lack of research considering the trade-offs between land expansion and population changes (TLEPC) in the context of sustainable development. Previous research often focused on accommodating population growth without adequately considering ecological impacts. The study aims to bridge this knowledge gap by quantifying how different global land expansion options contribute to SDG 11 compliance. Specifically, it investigates the significance and impact of a TLEPC strategy on the ecological service system and food production under different SSP scenarios (sustainability, middle-of-the-road, regional rivalry, inequality, and fossil-fuel development). The research also analyzes the most sustainable global development path using the TLEPC strategy and identifies regions where land consumption improvements are most impactful. Existing studies on urban land expansion have limitations, including a lack of consideration for trade-offs and insufficient monitoring data hindering the exploration of spatial interactions between global population and land use changes. This study uses a framework driven by multi-source remote satellite images to explore these interactions from 2010 to 2100.

Existing literature on global urban land expansion predominantly focuses on accommodating population growth without fully considering the trade-offs with ecological services. Studies highlighting the negative impacts of rapid urbanization on air quality, resource depletion, and biodiversity are reviewed. The limitations of past approaches are noted, including the lack of comprehensive monitoring data and insufficient consideration of the spatial interactions between population growth and land use changes. The Shared Socioeconomic Pathways (SSPs) framework is introduced as a crucial tool for projecting future scenarios, enabling a more nuanced analysis than previous studies relying on single-scenario projections. Studies exploring the impact of SSPs on land use change, habitat loss, and biodiversity are discussed, highlighting the complex interplay between urban expansion, agricultural expansion, and their effects on natural habitats. The unequal distribution of land expansion across different economic levels and its implications for the greening of built-up areas are also reviewed. The study builds upon previous research exploring the relationship between future land expansion and population change, but at a global scale, incorporating an improved data-driven simulation framework.

This study employs a data-based simulation framework to analyze the trade-off between land expansion and population change (TLEPC) under different Shared Socioeconomic Pathways (SSPs). The framework uses multi-objective functions to minimize urban land consumption while maximizing per capita built-up areas (PBUAs), employing Monte Carlo simulation to achieve Pareto optimality. The impact of the trade-off index on the total loss of non-built-up areas is quantified through scenario simulations at regional and national levels in the 21st century. Policy implications of the coordination index are thoroughly discussed. Multiple data sources are integrated for robust findings. Data on future population, urbanization rates, and GDP were collected from the SSP database (https://tntcat.iiasa.ac.at/SspDb), encompassing 153 countries. Land use change data was obtained from (https://doi.org/10.7910/DVN/85PJ1D), providing 1/8° grid cell resolution predictions from the CLUE-S model. Built-up area boundaries were downloaded from (https://www.naturalearthdata.com/). The modeling framework integrates data on population, land expansion, and economic factors, simulating land use and population changes at a 1/8° grid cell resolution. Monte Carlo simulations were conducted for five SSP scenarios, and a trade-off model was developed to optimize urban expansion and population, aiming to minimize land consumption and maximize per capita built-up areas. The Pareto frontier theory was used to identify optimal solutions. Factor analysis, using SPSS software, explored the relationships between population change rate (PCR), land change rate (LCR), GDP change rate (GCR), per capita GDP change rate (APGDP), population density change rate (ΔPPOP), and per capita GDP/land change rate (APLGDP). The land change rate (LCR) was calculated using the formula: LCR = (BUA_i - BUA_i² / n - 1) × 100%, where BUA represents built-up area, and n is the time interval. PCR and GCR were calculated similarly. The multi-objective optimization problem was solved using Monte Carlo simulations, with the objective function aiming to minimize urban land consumption (g1) and maximize per capita built-up areas (g2). Constraints were considered in terms of PCR, LCR, and PCR/LCR. For developed countries, the focus was on minimizing land consumption while maintaining adequate human settlement; for rapidly urbanizing regions, the Pareto frontier was determined for the TLEPC scheme.

The study's analysis reveals significant spatial inequality in per capita built-up areas (PBUAs) across countries. High-income countries exhibit significantly higher PBUAs than middle- and low-income countries. The distribution of PBUAs is uneven, with a concentration in Europe for middle-income countries and Africa and Asia for low-income countries. Projections of urban expansion and population growth under SSPs show varying trends. The ratios of population to land expansion differ significantly across SSPs (SSP1: 3.8977; SSP2: 2.3452; SSP3: 4.0865; SSP4: 3.3264; SSP5: 2.8133). PBUAs evolve differently under each SSP; SSP1 reaches its lowest point around 2040-2050, while others increase rapidly after 2040. Factor analysis identified three groups of synergistic driving factors: land use and population, land use and GDP, and population and GDP. The trade-off development strategy is shown to be beneficial across all five SSP scenarios. SSP1 is identified as the most sustainable path, leading to the least land consumption. Simulations demonstrate substantial reductions in land consumption under all SSPs (SSP1: 8.07%; SSP2: 28.2%; SSP3: 13.76%; SSP4: 23.92%; SSP5: 55.28%). The trade-off strategy, particularly under SSP1, increases carbon sequestration potential significantly by converting land to forest or grassland, although some regions might experience a decrease to improve human settlement. The strategy also improves cereal yield compared to baseline SSPs. Under SSP1, this can be increased by 19.798 million tons (MT) in 2030. In 2050, the PBUAs of founding European Union states are expected to be significantly higher than those of BRICS nations under all SSP scenarios.

The study's findings highlight the potential of trade-off strategies between land expansion and population growth to achieve sustainable urban development. The significant reduction in land consumption under various SSPs demonstrates the effectiveness of the proposed approach in mitigating the environmental impacts of urbanization while accommodating population growth. The increase in carbon sequestration potential underscores the ecological benefits of implementing these strategies, supporting efforts towards climate change mitigation. Improved cereal yields demonstrate the potential for enhanced food security. The results are particularly relevant to policymakers in developing nations, where rapid urbanization often leads to unsustainable land use practices. The research confirms the importance of integrated planning that considers both population dynamics and environmental sustainability. The spatial variations in PBUAs emphasize the need for targeted policies considering income levels and geographic locations. The identified synergistic driving factors can inform policy decisions for balanced development. The study's framework can be applied at smaller scales for more localized analyses.

This study demonstrates that incorporating trade-off strategies between urban expansion and population growth can significantly reduce future land consumption, enhance carbon sequestration, and improve food security. The SSP1 scenario, emphasizing sustainability, emerges as the most promising path. The findings underscore the need for integrated urban planning considering population dynamics and ecological sustainability. Future research should focus on smaller-scale applications, improved data integration and the development of intelligent decision-making support systems for optimal PBUA planning globally.

The study's limitations include data availability, which restricts the analysis of socio-economic and ecological factors. The focus on national and regional scales may not fully capture the nuances of smaller geographic areas such as cities or rural regions. The reliance on existing SSP scenarios introduces uncertainty associated with future projections, and the lack of detailed socio-economic data may limit understanding of local-level dynamics. Strategies for improving urbanization rates in resource-constrained countries require further exploration. Future research needs to consider the application of more fine-grained data, including mobile signal and point-of-interest data, to refine the model and understand local-level population dynamics and land expansion drivers.