Global land use change and its impact on greenhouse gas emissions

We synthesized 29 years of global historical data from the Food and Agriculture Organization of the United Nations (FAO) 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 predominant human-made 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 dataset 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.

Methods
Historical data for the period of 1992–2020 were obtained from the Food and Agriculture Organization of the United Nations (FAO) and the World Bank. The data were available by country and year. We compiled land use (artificial surface, cropland, pasture, barren land, forest) areas and greenhouse gas (GHG) emissions for 191 countries and territories. 

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. 

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 was used for model training and testing. 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.