This data and code repository contains the data and code necessary to reproduce the findings, tables and figures from the publication:

"Observed carbon decoupling of subnational production insufficient for net-zero goal by 2050"

Code was written by Maria Zioga. In case of questions please contact: maria.zioga@pik-potsdam.de


Primary and secondary data included:

-"Data/DOSE_V2.csv" DOSE dataset including subnational economic, population and sector specific economic data from: https://www.nature.com/articles/s41597-023-02323-8

-"Secondary_Data/reg_co2_in_tons.csv" subnational CO2 emissions time series, output from the Python script "calc_reg_co2.py" which aggregates the emissions data from grid cells to the respective subnational level using masks and region areas produced from "calc_region_mask_and_region_area.py"

-"Secondary_Data/annual_co2_sum.xlsx" globally aggregated CO2 emissions time series, output from the Python script "calc_annual_co2_sum.py" which aggregates the emissions data from grid cells to global level

-"Secondary_Data/hist_DI.csv" subnational decoupling indicator calculated for all available years, output from the Python script "TableS2.py"

-"Secondary_Data/co2_cumul_sum.xlsx" regional co2 for the years before 1970, based on national emissions (Global Carbon Project data) and regional shares (from 1970-1980), output from script "calc_cumul_sum_co2.py"

-"Data/OECD_EXPENDITURE.csv" Data on national spending for subnational climate actions in OECD countries from: https://data-explorer.oecd.org/vis?tenant=archive&df%5Bds%5D=DisseminateArchiveDMZ&df%5Bid%5D=DF_SGCFD&df%5Bag%5D=OECD&dq=..S1312_1313.EXPENDITURE.GDP_SH

-"Data/wgidataset.xlsx" Data on the government effectiveness ranking from the World Bank from: https://www.worldbank.org/en/publication/worldwide-governance-indicators

-"Data/CCPI_2010.csv" Climate Change Performance Index (CCPI) data from the 2010 report: https://ccpi.org/downloads/

-"Secondary_Data/city_mitigation_plans_GID_1.csv" Data on existence and type of climate mitigation plans for EU cities allocated in the respective subnational regions, output from the script "city_to_subnational_level.py"



Primary data not included:

-Gridded carbon emissions data is publicly available from: https://edgar.jrc.ec.europa.eu/dataset_ghg70

-Shapefiles of DOSE subnational regions from: https://www.nature.com/articles/s41597-023-02323-8

-Gridded carbon emissions data is publicly available from: https://edgar.jrc.ec.europa.eu/dataset_ghg70

-Data on existence and type of climate mitigation plans for EU cities is available from: https://data.mendeley.com/datasets/65h7t7sdd7/1

-Gridded global population scenarios data is publicly available from: https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/62/

-Shapefiles for subnational regions from GADM, used for map plots, from: https://gadm.org/



Code included:

-"calc_region_mask_and_region_area.py" produces mask for each subnational region, according to DOSE shapefiles, as well as region areas

-"calc_reg_co2.py" aggregates the CO2 data from grid cells to the respective subnational level in tons

-"calc_annual_co2_sum.py" produces global CO2 emissions timeseries

-"city_to_subnational_level.py" allocates city- level climate policies data to the respective subnational regions used in the analysis

-"Climate_actions_effect.py" produces Figure 3 and related results for Tables S3-5

-"calc_cumul_sum_co2.py" calculates regional co2 for the years before 1970, based on national emissions (Global Carbon Project data) and regional shares (from 1970-1980)

-"net_zero_X_s_t.py" script submitted for 1000 iterations with Nboot and seeds ranging until 20 and 50 respectively, to conduct Monte Carlo simulations which sample from the sources of uncertainty for the net zero projections under the assumption X namely constant emission intensity change rates (a) or accelerated rates driven by socioeconomic factors (b)

-"net_zero_X_s_t_process.py" processes results from the script net_zero_X_s_t.py

-"FigureX.py" scripts to plot subplots for Figures 1,2,4 and S1-3

-"TableX.py" scripts for tables S1-2

-"Regressions.py" produces all regressions results for Tables S6-7