Dynamics of carbon and CO<Subscript>2</Subscript> removals by Brazilian forest plantations during 1990–2016

Background

We analyzed the dynamics of carbon (C) stocks and CO₂ removals by Brazilian forest plantations over the period 1990–2016. Data on the extent of forests compiled from various sources were used in the calculations. Productivities were simulated using species-specific growth and yield simulators for the main trees species planted in the country. Biomass expansion factors, root-to-shoot ratios, wood densities, and carbon fractions compiled from literature were applied. C stocks in necromass (deadwood and litter) and harvested wood products (HWP) were also included in the calculations.

Results

Plantation forests stocked 231 Mt C in 1990 increasing to 612 Mt C in 2016 due to an increase in plantation area and higher productivity of the stands during the 26-year period. Eucalyptus contributed 58% of the C stock in 1990 and 71% in 2016 due to a remarkable increase in plantation area and productivity. Pinus reduced its proportion of the carbon storage due to its low growth in area, while the other species shared less than 6% of the C stocks during the period of study. Aboveground biomass, belowground biomass and necromass shared 71, 12, and 5% of the total C stocked in plantations in 2016, respectively. HWP stocked 76 Mt C in the period, which represents 12% of the total C stocked. Carbon dioxide removals by Brazilian forest plantations during the 26-year period totaled 1669 Gt CO_2-e.

Conclusions

The carbon dioxide removed by Brazilian forest plantations over the 26 years represent almost the totality of the country´s emissions from the waste sector within the same period, or from the agriculture, forestry and other land use sector in 2016. We concluded that forest plantations play an important role in mitigating GHG (greenhouse gases) emissions in Brazil. This study is helpful to improve national reporting on plantation forests and their GHG sequestration potential, and to achieve Brazil’s Nationally Determined Contribution and the Paris Agreement.

     

Fine-scale simulation of spatial population distribution by coupling GA–ABM and big data: A case study of Dongguan,China

Capturing spatial population distribution can offer useful information for urban planning to promote reasonable population distribution and allocate urban resource. Agent-based model (ABM) based on the modeling idea of “bottom-up” can offer the ability to simulate the complex individual behaviors that generate spatial population distribution. Previous ABMs were unable to be extended for simulation of spatial population distribution at a fine scale due to the shortage of fine characterization of the urban environment and the calibration of agents' behavior. This study filled these gaps by proposing a genetic algorithm-ABM (GA–ABM) for fine-scale simulation of spatial population distribution in a manufacturing metropolis. In this model, the employment and residential choice behaviors of agents were defined by the labor economic theory and discrete selection model. Multisource geospatial big data such as enterprise points-of-interest big data and building footprints data were used to finely characterize the labor market and urban environment to reflect the impact of agents' employment choices on their residential decision. Furthermore, the grid-scale population investigation big data were combined with the GA to calibrate the agents' residential decision behaviors. The proposed model was used in Dongguan, the typical manufacturing metropolis in China. As a comparison, the expert-experience-based method-ABM (EEBM–ABM) was also conducted by using the same data set. Through the comparison of the results produced by these two models, it was demonstrated that the model coefficient calibrated by GA could effectively reflect the agents' residential decisions. The calibrated GA–ABM is more capable than EEBM–ABM in simulating spatial population distribution in a manufacturing metropolis. Hence, the proposed model can be used to simulate spatial population distribution in a manufacturing metropolis which helps the urban planner to conduct scientific urban planning.