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.