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. 2024 Nov 29;30(12):e17604. doi: 10.1111/gcb.17604

Global Land Use Change and Its Impact on Greenhouse Gas Emissions

Lidong Li 1,2,✉, Tala Awada 1, Yao Zhang 2, Keith Paustian 2
PMCID: PMC11607391  PMID: 39614423

ABSTRACT

Anthropogenic activities have altered approximately two‐thirds of the Earth's land surface. Urbanization, industrialization, agricultural expansion, and deforestation are increasingly impacting the terrestrial landscapes, leading to shifts of areas in artificial surface (i.e., humanmade), cropland, pasture, forest, and barren land. Land use patterns and associated greenhouse gas (GHG) emissions play a critical role in global climate change. Here we synthesized 29 years of global historical data and demonstrated how land use impacts global GHG emissions using structural equation modeling. We then obtained predictive estimates of future global GHG emissions using a deep learning model. Our results show that, from 1992 to 2020, the global terrestrial areas covered by artificial surface and cropland have expanded by 133% and 6% because of population growth and socioeconomic development, resulting in 4.0% and 3.8% of declines in pasture and forest areas, respectively. Land use was significantly associated with GHG emissions (p < 0.05). Artificial surface dominates global GHG emissions, followed by cropland, pasture, and barren land. The increase in artificial surfaces has driven up global GHG emissions through the increase in energy consumption. Conversely, improved agricultural management practices have contributed to mitigating agricultural GHG emissions. Forest, on the other hand, serves as a sink of GHG. In total, global GHG emissions increased from 31 to 46 GtCO2eq from 1992 to 2020. Looking ahead, if current trends in global land use continue at the same rates, our model projects that global GHG emissions will reach 76 ± 8 GtCO2eq in 2050. In contrast, reducing the rates of land use change by half could limit global GHG emissions to 60 ± 3 GtCO2eq in 2050. Monitoring and analyzing these projections allow a better understanding of the potential impacts of various land use scenarios on global climate and planning for a sustainable future.

Keywords: barren land, cropland expansion, deforestation, global emission prediction, pasture loss

This study explores how global land use changes, including urbanization, agricultural expansion, and deforestation, have influenced greenhouse gas (GHG) emissions. Using 29 years of data and AI modeling, the research reveals that land use changes, particularly the growth of urban areas, significantly increase GHG emissions, while sustainable practices and forest restoration can mitigate these effects. Predictions show that limiting land use change could reduce future emissions, offering insights for sustainable climate strategies.

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1. Introduction

Increase in greenhouse gas (GHG) emissions is raising global land surface temperature. Global warming and its associated intensification of hydrological cycle have led to more frequent extreme climatic events, for example, extended droughts and severe floods, across the globe (Dai 2013; Tabari 2020). Addressing climate change through mitigation efforts is urgent. A growing number of regional studies attribute the increase in GHG emissions to various anthropogenic activities (Basu et al. 2020; Cao et al. 2023; Herrero et al. 2016; Karmaker et al. 2020; Spawn, Lark, and Gibbs 2019; van Loon et al. 2019). Specifically, urbanization and industrialization have increased fossil fuel consumption, with fossil fuel combustion releasing alarming levels of GHG into the atmosphere (Basu et al. 2020; Cao et al. 2023; Karmaker et al. 2020). Agricultural land expansion and deforestation continue to contribute substantially to the rise in GHG emissions (Herrero et al. 2016; Spawn, Lark, and Gibbs 2019; van Loon et al. 2019). Conversely, forest restoration holds the potential to mitigate these emissions (He et al. 2024). Therefore, land use can be both a source and a sink of GHG, making it a key sector for mitigation efforts. While there is sufficient empirical evidence of these relationships at the regional scales (Basu et al. 2020; Cao et al. 2023; Herrero et al. 2016; Karmaker et al. 2020; Spawn, Lark, and Gibbs 2019; van Loon et al. 2019), their validity on a global scale is yet to be tested. Herein, we synthesized 29 years of global historical data from the Food and Agriculture Organization (FAO) of the United Nations and World Bank and summarized global land use change and its implication for global GHG emissions. The land use types include artificial surface (i.e., any type of land with a predominantly humanmade structure), cropland, pasture (including both natural and cultivated), barren land, and forest. The goal was to combine empirical analysis, through structural equation modeling, with predictive modeling using deep learning, to understand and forecast the impact of land use decisions on GHG emissions. More specifically, we first established and validated causal relationships between areas of different land use types and global GHG emissions. This was achieved through structural equation modeling using the historical data set consisting of 33,234 data points from 1992 to 2020. Then, we employed a deep learning approach to leverage the extensive historical data across various land use types, from the lowest to the highest GHG emitting land, to predict potential future GHG emissions under different land use scenarios from 2021 to 2050. By estimating GHG emissions for various future land use scenarios, our study intended to offer a projection approach that could assist in planning effective climate change mitigation strategies. These projections are important for developing strategies that balance sustainability with climate change mitigation.

2. Materials and Methods

2.1. Data Syntheses

Historical data for the period of 1992–2020 were obtained from FAO and World Bank. The data were available by country and year, covering up to 246 countries and territories. We compiled land use (artificial surface, cropland, pasture, barren land, and forest) areas and greenhouse gas (GHG) emissions for 191 countries and territories, capturing 96%–97% of global GHG emissions. In this paper, artificial surface is composed of any type of area with a predominantly humanmade structure; cropland is the land used for cultivation of crops; pasture is the land used permanently, that is, 5 years or more, to grow herbaceous forage crops naturally or through cultivation, for example, natural prairie and grasslands or cultivated grazing land; barren land is dominated by natural abiotic surfaces, for example, bare soil, sand, rock, with natural vegetation cover < 2%; forest is the land spanning more than 0.5 ha with trees higher than 5 m and a canopy cover of more than 10%, excluding land that is predominantly under agricultural or urban land use; GHG is composed of CO2 totals excluding short‐cycle biomass burning, for example, agricultural waste and savanna burning, but including other biomass burning, for example, forest fires, postburn decay, peat fires, and decay of drained peatlands, and all anthropogenic N2O, CH4, and fluorinated gas totals. See more detailed definitions from FAO and World Bank (https://www.fao.org/faostat/en/#data/domains_table; https://data.worldbank.org/indicator).

2.2. Statistical Analyses

Structural equation modeling was conducted using the Structural Equation Models Optimization in Python (semopy; Python 3.12) to quantify the effects of land use on GHG emissions. Structural equation modeling was conducted following procedures in Li et al. (2019). We hypothesized the effects of land use areas on GHG emissions based on empirical research (Basu et al. 2020; He et al. 2024; Li, Hong, et al. 2024, Li, Ma, et al. 2023; van Loon et al. 2019; Wall et al. 2023) and then tested if the pathways were significant using the historical data. Path coefficients were tested by the maximum likelihood estimation at a p < 0.05 level. Model fit was evaluated by the Comparative Fit Index (CFI), the goodness‐of‐fit index (GFI), and the normed fit index (NFI). The StandardScaler from scikit‐learn was used for data scaling to ensure comparability across variables.

2.3. Model Development and Generalization

Deep learning can be an effective approach for ecosystem modeling of the effects of land use change (Li, Hosseiniaghdam, et al. 2023, Li, Liang, Awada, Hiller, and Kaiser, 2024). We used the long short‐term memory (LSTM) based recurrent neural network (RNN) as the algorithm for modeling (Keras in TensorFlow; Python 3.12). The historical data were used for model training and testing. We manually tuned the hyperparameters with the 10‐fold cross‐validation for robust evaluation of model performance. The dropout regularization, early stopping callback, and learning rate reduction were applied in the LSTM layers to mitigate overfitting and handle the effects of multicollinearity. These techniques help prevent the model from becoming overly sensitive to specific variable interactions, especially in the case of correlated predictors. Model fit was evaluated by coefficient of determination (R 2 ), root mean squared error (RMSE), and loss function of mean squared error. Model uncertainty was evaluated by the standard deviation of the folds. Based on the established data‐driven relationships obtained from the model training and testing with the historical data, we predicted the future GHG emissions from 2021 to 2050 in two hypothesized scenarios using future land use areas as predictors.

The future land use areas from 2021 to 2050 in the two hypothesized scenarios were projected using linear regression with specified changing rates. We assumed linear regression is a close approximation based on the trends in historical data (Figure 1a−e). In Scenario 1, areas of artificial surface, cropland, pasture, barren land, and forest were maintained at the same changing rates as the historical period. In Scenario 2, the changing rates were half of those in the historical period.

FIGURE 1.

FIGURE 1

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Global land use areas and global greenhouse gas (GHG) emissions from 1992 to 2020. The shaded area represents a 95% confidence interval. Data were obtained from FAO and World Bank. (a) global areas of artificial surface, (b) global areas of cropland, (c) global areas of pastureland, (d) global areas of barren land, (e) global areas of forestland, (f) global greenhouse gas emissions.

3. Results

3.1. Historical Trends

During 1992–2020 (Figure 1), global areas of artificial surface increased from 0.026 to 0.060 billion ha. Global cropland increased from 1.48 to 1.58 billion ha. Global pasture decreased from 3.34 to 3.20 billion ha. Global barren land decreased from 1.95 to 1.91 billion ha. Global forest decreased from 4.22 to 4.06 billion ha. Global GHG emissions increased from 31 to 46 GtCO2eq. Model‐fit indices showed optimal model‐fit of the structural equation model (CFI = 1.00, GFI = 0.99, NFI = 0.99). The GHG emissions were significantly associated with land use areas (p < 0.05, Figure 2). Each unit of increase in areas of artificial surface, cropland, pasture, and barren land was associated with 0.64, 0.31, 0.13, and 0.02 units of increase in GHG emissions, respectively. Each unit of increase in forest was associated with 0.09 units of decrease in GHG emissions. Between 1992 and 2020, agricultural GHG emissions declined from 11.36 to 10.91 GtCO2eq, and the percentage of agricultural GHG in global emissions decreased from 37% to 24% (Figure 3).

FIGURE 2.

FIGURE 2

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Effects of global land use areas on greenhouse gas emissions from 1992 to 2020 tested by structural equation modeling. Boxes represent variables. Arrows indicate causal relationships. Green arrows indicate negative effects, and black arrows indicate positive effects. The arrow width indicates effect size. Numbers beside arrows are standardized path coefficients, that is, effect sizes. All presented relationships are significant at a p < 0.05 level. Data were obtained from FAO and World Bank, totaling 33,234 data points.

FIGURE 3.

FIGURE 3

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Agricultural greenhouse gas (GHG) emissions (a) and the percentage of agricultural GHG emissions in global GHG emissions (b) from 1992 to 2020. The shaded area represents a 95% confidence interval. Data were obtained from FAO and World Bank.

3.2. Future GHG Emission Projections

The performance metrics showed optimal model fit and narrow uncertainty (Figures S1 and S2 and Table S1). Our model explained 96% of the variance in GHG emissions (Table S1). In Scenario 1, the global areas of artificial surface, cropland, pasture, barren land, and forest were 0.14, 1.67, 3.04, 1.85, and 3.87, respectively, in 2050 (Figure 4). Based on these land use changes, the predicted global GHG emissions were 76 ± 8 GtCO2eq in 2050 (Figure 4). In Scenario 2, the global areas of artificial surface, cropland, pasture, barren land, and forest were 0.10, 1.62, 3.10, 1.86, and 3.96, respectively, in 2050 (Figure 5). Based on these land use changes, the predicted global GHG emissions were 60 ± 3 GtCO2eq in 2050 (Figure 5).

FIGURE 4.

FIGURE 4

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Scenario 1. Global land use changes (2021–2050) at the historical rates (1992–2020) and the predicted greenhouse gas (GHG) emissions. (a) global areas of artificial surface, (b) global areas of cropland, (c) global areas of pastureland, (d) global areas of barren land, (e) global areas of forestland, (f) global greenhouse gas emissions.

FIGURE 5.

FIGURE 5

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Scenario 2. Global land use changes (2021–2050) at half of the historical rates (1992–2020) and the predicted greenhouse gas (GHG) emissions. (a) global areas of artificial surface, (b) global areas of cropland, (c) global areas of pastureland, (d) global areas of barren land, (e) global areas of forestland, (f) global greenhouse gas emissions.

4. Discussion

4.1. Global Land Use Changes

Global artificial surface and cropland areas have increased by 133% and 6% from 1992 to 2020, respectively (Figure 1a,b). These increases are associated with population growth and socioeconomic development. As the population grows, the demand for housing, industry, infrastructure, and food production rises, leading to the expansion of artificial surfaces and cropland into natural ecosystems. Socioeconomic development requires more commercial areas, further increasing artificial surface. The globalization of agricultural markets incentivizes countries to expand croplands for commodity crops, exporting surpluses for economic gain (Schwarzmueller and Kastner 2022; Winkler et al. 2021). The expansion of artificial surface causes the loss of not only natural ecosystems but also cropland, with 16% of global cropland loss attributed to this expansion (Potapov et al. 2022). New artificial surface typically occupies the cropland surrounding the existing developed areas. Additionally, poor land management can degrade cropland as unsustainable agricultural practices accelerate the loss of soil organic carbon and nutrients, and result in higher soil erosion rates (Li et al. 2021). Soil erosion is one of the major degradation causes in cropland, affecting 20% of global cropland (Prăvălie et al. 2021). These cropland losses threaten food, fiber, feed, and biofuel security and create pressure for further cropland expansion into natural ecosystems (Huang et al. 2020; Potapov et al. 2022). Nearly half of the cropland expansion replaced natural vegetation cover in the first two decades of the 21st century (Potapov et al. 2022). Moreover, the expanded cropland often yields lower net primary productivity compared to the original ecosystems (Liu, Liu, and Wang 2021).

On the limited global land, multiple competing demands exist. Global pasture area increased in the 1990s (Figure 1c). During the 1990s, many developing countries experienced considerable economic growth, leading to higher income and thus increased consumption of meat and dairy products (Delgado 2003). The wave of trade liberalization in the 1990s, including the World Trade Organization (WTO) agreements and the North American Free Trade Agreement (NAFTA), facilitated the global trade of agricultural products (Imbruno 2016; Kennedy and Rosson 2002). The increasing consumption and trade for livestock products increased the demand for pastureland in the 1990s. Since 2001, global pasture areas have decreased (Figure 1c). Conversion to cropland is the main cause of global pasture loss (Kreidenweis et al. 2018; Michalk et al. 2019). Additionally, global pasture areas have decreased as pasture intensification (e.g., fertilization, irrigation, and grazing) has led to more intensive and efficient land use. However, pasture intensification does not always lead to land‐saving effects, as the export of surplus livestock products can increase simultaneously (Kreidenweis et al. 2018). Moreover, grazing intensification can potentially cause pastureland degradation (Godde et al. 2018; Michalk et al. 2019). Natural pasture is grazed by wild herbivores and domestic livestock as well (Bardgett et al. 2021). Grazing is one of the most prominent uses of both natural and cultivated pasturelands. However, excessive grazing with time removes vegetation cover, reducing root volume, carbon storage, and water retention capacity and thus increasing soil erosion risk (Bai and Cotrufo 2022; Centeri 2022; Dodd et al. 2011; Lulandala et al. 2022). This risk makes the land more vulnerable to degradation and desertification. The loss of agricultural land often leads to increased pressure to expand into natural ecosystems. The expansion of cropland, pasture, and artificial surface are major drivers of forest loss, accounting for 50%, 38%, and 6% of global deforestation, respectively (FAO 2021). Indeed, from 1992 to 2020, global forest areas decreased by 4% (Figure 1e). Deforestation has become a major global concern, resulting in reduced ecosystem services and increased GHG emissions (Hoang and Kanemoto 2021; Veldkamp et al. 2020). As global land use continues to change, each sector will face unique challenges related to GHG mitigation.

4.2. Implications for GHG Emissions

The increase in global artificial surface drove up GHG emissions (Figure 2). Global artificial surface includes urban areas, industrial zones, transportation networks, and other infrastructure, collectively making it the leading emitter on the globe because of its high energy consumption. Urban areas contribute to ~75% of global GHG emissions, with infrastructure accounting for ~40% (Dong et al. 2022). Urban areas have high energy consumption for heating, cooling, lighting, powering appliances, transportation, waste management, etc. (Goldstein, Gounaridis, and Newell, 2020; Umar et al. 2021). Industrial activities are energy‐intensive due to the production of steel, cement, chemicals, and other basic materials to meet economic demands (Lamb et al. 2021). Infrastructure construction and maintenance require substantial materials and energy (Wei et al. 2021). The energy generation for these activities usually relies on the combustion of fossil fuels, mainly coal, oil, and natural gas, releasing a surge of GHG into the atmosphere. Specifically, CO2 is the primary GHG emitted by the combustion of fossil fuels, which are responsible for 87% of global CO2 emissions (Friedlingstein et al. 2022; Hausfather and Friedlingstein 2022). Although N2O and CH4 are emitted in smaller amounts compared to CO2, their global warming potential is 298 and 34 times more potent than CO2 over a 100‐year period, respectively (IPCC 2014). Despite the development of renewable energy, fossil fuel consumption outpaced any improvements in reducing GHG emissions.

In contrast to artificial surface, agricultural land typically has lower overall energy consumption but is the second largest source of GHG globally (Figure 2). Unlike artificial surface, which are concentrated sources of GHG, agricultural activities, despite their lower emission intensities, are dispersed out across larger areas (Figure 1a−c). Consequently, agricultural land is also a substantial source of global GHG emissions (Figure 2). Agricultural emissions are mainly derived from the management and expansion of cropland and pasture. Specifically, agriculture is responsible for 48% of global N2O emissions, primarily from the application of nitrogen amendments, including synthetic fertilizer, manure, and crop residue, to cropland or pasture (Li, Hong, et al. 2024; Shcherbak, Millar, and Robertson 2014; Song et al. 2023). Irrigation also contributes to N2O emissions by promoting anaerobic soil conditions conducive to denitrification (Li, Hong, et al. 2024; Li, Ma, et al. 2023). Agriculture accounts for ~40% of global anthropogenic CH4 emissions, with ~32% from enteric fermentation in ruminant livestock (cattle, sheep, goats, etc.) and manure management systems, and ~8% from rice cultivation (Ocko et al. 2021). The most significant source of agricultural CO2 emissions is cropland expansion into forests and other natural ecosystems (Hong et al. 2021). Global forest stores ~45% of terrestrial carbon (Bonan 2008). Upon converting to cropland, forest usually loses most of its biomass carbon within a year and 31%–52% of the soil organic carbon with time (Goldstein, Turner, et al. 2020; Wei et al. 2014). Agricultural CO2 also comes from soil disturbance through tillage, burning of crop residues, and the use of fossil fuel in machinery and irrigation systems. With cropland expansion and the increases in agricultural production (Figures 1b and S3), GHG emissions from global agricultural land, surprisingly, decreased from 11.4 to 10.9 GtCO2eq during 1992–2020 (Figure 3a). The contribution of agricultural land to global GHG emissions decreased by 36% in 2020 compared with that in 1992 (Figure 3b). This decrease is credited to the unprecedented global promotion of conservation management practices since the 1990s. These practices include efficient fertilizer use, nitrification inhibitors, fertigation, deficit irrigation, livestock diet optimization and selective breeding, anaerobic digestion of manure, rice variety selection and noncontinuous flooding, reduced or no tillage, and other climate‐smart practices.

Another effective mitigation strategy is forestation. Global forest served as a sink for GHG (Figure 2). Planting forests can initiate tangible carbon storage benefits both aboveground and belowground through the removal of CO2 from the atmosphere during photosynthesis. Forest biomass growth and the subsequent transfer of biomass carbon to soil organic carbon have the potential to mitigate GHG emissions. Similar benefits could be realized when pasture is preserved or sustainably managed. On the contrary, in barren land, soil organic carbon is more susceptible to disturbances without vegetation cover, allowing carbon loss as CO2 to the atmosphere. These processes have immediate implications for preserving and restoring ecosystems in future scenarios.

4.3. Future Scenarios

The demand for land to support socioeconomic development and food, feed, fiber, and fuel production will continue to increase because of the increasing global population. An estimated 0.4 billion ha of additional urban area and 0.5 billion ha of additional cropland will be needed by 2050 in response to the growing global population and food demand (Angel et al. 2011; Meng et al. 2023; Mogollón et al. 2021). The impact of land use can lead to forecasted quantifiable consequences on GHG emissions. Our predicted results show that if global land use continues to change at historical rates (i.e., 1992–2020), global GHG emissions would increase to 76 ± 8 GtCO2eq in 2050 (Figure 4). This projection can serve as a benchmark for the evaluation of alternative mitigation strategies. For instance, if the rates of land use change could be reduced by half, it would deliver a 20% ± 9% mitigation benefit by 2050 (Figure 5). With the ambition of limiting the global temperature increase to 1.5°C or 2°C above preindustrial levels by 2050, the time window is narrowing for all sectors (Lamb et al. 2021). Ecosystem conservation and restoration can be strategies to offset, although only partially, the current GHG emissions from fossil fuel combustion. Relying solely on land use is probably insufficient to meet the mitigation goal, but it allows time to develop newer and cleaner technology and energy sources to reduce GHG emissions through fossil fuel savings. Ecosystem restoration might compete with socioeconomic development and food production for land. A more sustainable development pathway is needed to balance socioeconomic development, food production, and ecosystem restoration. Ecosystem conservation and restoration are integral to global strategies for GHG mitigation and are among the most effective strategies to date for mitigating climate change.

4.4. Model Limitations and Advantages

Our model relies on historical data to learn patterns and make predictions, so its accuracy is contingent on the quality and representativeness of the training data. Any biases, gaps, or inconsistencies in historical land use or emission data can lead to inaccurate predictions. This limitation highlights the importance and the need for improved temporal and spatial resolutions in the training data, given that GHG emissions are highly variable in space and time. As more data become available, it will be possible to update the model and improve its accuracy over time.

Despite its limitations, our LSTM‐based RNN model provides several key advantages for predicting future GHG emissions from land use change. First, LSTM is particularly suited for temporal, sequential data as it can learn temporal dependencies, making it ideal for time‐series data. By capturing these dependencies, the model can better account for the delayed effects of land use change on GHG emissions, which may not be immediately apparent. This capability can enhance accuracy in future predictions. Second, our model also provides flexible scenario analyses. Incorporating hypothetical scenarios based on historical trends, it allows for scenario‐specific forecasting, which is useful for exploring “what‐if” cases. Third, our model is scalable. The model is adaptable to global datasets but can also be scaled down for regional studies. This flexibility enables regional predictions as new land use and emissions data are collected. Fourth, our model is transparent in scenario assumptions and interpretations. The model maintains a clear assumption structure, enabling users to easily interpret and directly replicate the modeling logic for different land use strategies. Overall, the model's ability to provide realistic forward‐looking predictions supports better preparedness and fosters sustainable land use planning to balance ecological preservation with socioeconomic needs.

Author Contributions

Lidong Li: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing – original draft, writing – review and editing. Tala Awada: funding acquisition, project administration, resources, supervision, writing – review and editing. Yao Zhang: funding acquisition, project administration, resources, supervision. Keith Paustian: funding acquisition, project administration, resources, supervision.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1.

GCB-30-e17604-s001.docx (989.4KB, docx)

Acknowledgments

We thank Christopher Misar for pasture management consultation. We thank FAO and World Bank for the publicly available databases. This study was supported by the Walmart Foundation. We also acknowledge support from the Partnership on Data Innovation (PDI) of the Agricultural Research Service (ARS), United States Department of Agriculture (USDA).

Funding: This work was supported by Partnership on Data Innovation (PDI) of the Agricultural Research Service (ARS), United States Department of Agriculture (USDA) and Walmart Foundation.

Data Availability Statement

The data source from FAO and World Bank can be accessed at https://www.fao.org/faostat/en/#data and https://data.worldbank.org/indicator. The data and code that support the findings of this study are openly available in Dryad at https://doi.org/10.5061/dryad.4j0zpc8n3.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1.

GCB-30-e17604-s001.docx (989.4KB, docx)

Data Availability Statement

The data source from FAO and World Bank can be accessed at https://www.fao.org/faostat/en/#data and https://data.worldbank.org/indicator. The data and code that support the findings of this study are openly available in Dryad at https://doi.org/10.5061/dryad.4j0zpc8n3.

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