EGU2020-22104
https://doi.org/10.5194/egusphere-egu2020-22104
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Investigating the GDP-CO2 relationship using a neural network approach

Sebastian Jensen, Eric Hillebrand, and Mikkel Bennedsen
Sebastian Jensen et al.
  • Aarhus University, Aarhus BSS, Department of Economics and Business Economics, Denmark (smjensen@econ.au.dk)

Exploiting a national-level panel of per capita CO2 emissions and GDP data, we investigate the GDP-CO2 relationship, using a data-driven approach. We conduct an in-sample analysis in which we investigate the shape of the GDP-CO2 relationship. Utilizing the shape of the GDP-CO2 relationship learned, we project CO2 emissions through 2100, using the same set of GDP and population growth scenarios as used by the Intergovernmental Panel of Climate Change (IPCC) for their sixth assessment report due for release in 2021-22. Our analysis is carried out at two levels: at a global, and at the level of five large regions of the world. We consider a semiparametric model specification which places no restrictions on the functional relationship between GDP and CO2, but which allows for country and time specific fixed effects. The nonparametric component of our model is specified as a feedforward neural network, ensuring universal approximation capabilities, theoretically. In a simulation study, we show that our model is able to capture various complex relationships in finite samples of realistic sizes.

How to cite: Jensen, S., Hillebrand, E., and Bennedsen, M.: Investigating the GDP-CO2 relationship using a neural network approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22104, https://doi.org/10.5194/egusphere-egu2020-22104, 2020

Display materials

Display file

Comments on the display material

AC: Author Comment | CC: Community Comment | Report abuse

Display material version 1 – uploaded on 04 May 2020, no comments