meta_analysis_supplementary_material

data_source.xlsx

Excel file contains the collected data for the meta-analysis. It includes 6 spreedsheets as:

• data_source: Selected publications with detailed information about: source, model caracteristic, intervention type, macro-indicators, geographical and temporal dimension, and method/data transparency
• gdp: Range of projections for GDP scenarios per study
• job: Range of projections for job creation scenarios per study
• co2: Range of projections for CO2 emissions scenarios per study
• figure_1: A flowchart of the inclusion of selected publications, and specific queries used in the systematic review
• table_1: Overview of models used by the selected 28 publications

time_ser.py

Phyton script retrieves the time series of changes in GDP, job creation ,and CO2 emissions respect to BAU scenarios from 2020 to 2050, per scenario category. This module contains:

• main(): Loads data source and runs scenario(data, title) function
• scenario(data, title): Retrieves graphs and dataframes for the time series of scenarios per study. Inputs: data=dataset for specific indicator as pandas dataframe; title= text as string
• mean(df, scen_type): Retrieves mean values and statistcal summary for a specific scenario type. Inputs: df=dataset for specific indicator as pandas dataframe; scen_type= 'amb' or 'mod' as string
• save(): Saves dataframes from main() in an Excel file

boxplot.py

Phyton script retrieves the boxplot of changes in GDP, job creation ,and CO2 emissions respect to BAU, per year and scenario category. This module contains:

• main(): Loads data source and runs boxplot(data, year, title) function
• boxplot(data, year, title): Retrieves boxplot graphs from the statistical analysis. Inputs: data=dataset for specific indicator as pandas dataframe; year= specific year as integer; title= text as string
• group(df, group_name, degree, title): Retrieves mean values and statistcal summary for a specific group region. Inputs: df=dataset for specific indicator as pandas dataframe; group_name= country/region group name as list; title= text as string

corr.py

Phyton script retrieves the correlation analysis of changes in GDP, job creation ,and CO2 emissions respect to BAU per year using Pearson Method. This module contains:

• main(): Loads data source and runs corr(data, year, title) function
• corr(gdp, co2, job, year): Retrieves correlation analysis using Pearson Method. Inputs: gdp=dataset for GDP indicator as pandas dataframe; job=dataset for job creation indicator as pandas dataframe; co2=dataset for CO2 emissions indicator as pandas dataframe; year= specific year as integer
• group(df, group_name, degree, title): Retrieves mean values and statistcal summary for a specific group region. Inputs: df=dataset for specific indicator as pandas dataframe; group_name= country/region group name as list; title= text as string
• save(): Saves dataframes from main() in an Excel file

results_time_ser.xlsx

Excel file contains the summary of results from time_ser.py

results_corr.xlsx

Excel file contains the summary of results from corr.py