Climate change and the intricacies of statistical analysis
TP
13/3/020
The problem
There is a theory that we (humanity) are heading towards disaster due to Global Warming caused by excessive Greenhouse Gases (GHG) emission. If this theory is true, the consequences are of uttermost importance. So who is the most notorious GHG polluter in EU? Look at the numbers.
The method
Select biggest EU-member states and visually show their level and change in GHG emission.
Selecting big members
Which country is a big one? We will use total GDP as an indicator. We start by quering Eurostat database to figure out which table contains GDP data:
## [1] "GDP and main components (output, expenditure and income)=>nama_10_gdp"
## [2] "GDP and main components (output, expenditure and income)=>namq_10_gdp"
## [3] "Gross domestic product (GDP) at current market prices by NUTS 2 regions=>nama_10r_2gdp"
## [4] "Average annual population to calculate regional GDP data (thousand persons) by NUTS 3 regions=>nama_10r_3popgdp"
## [5] "Gross domestic product (GDP) at current market prices by NUTS 3 regions=>nama_10r_3gdp"
## [6] "European Union trade mark (EUTM) applications per billion GDP by NUTS 3 regions=>ipr_ta_gdpr"
## [7] "Community design (CD) applications per billion GDP by NUTS 3 regions=>ipr_da_gdpr"
## [8] "Gross domestic product (GDP) at current market prices by metropolitan regions=>met_10r_3gdp"
## [9] "Average annual population to calculate regional GDP data by metropolitan regions=>met_10r_3pgdp"
## [10] "European Union trade mark (EUTM) applications per billion GDP by metropolitan regions=>met_ipr_tagdp"
## [11] "Community design (CD) applications per billion GDP by metropolitan regions=>met_ipr_dagdp"
## [12] "Community designs (CD) per billion GDP by metropolitan regions=>met_ipr_dfagdp"
## [13] "Gross domestic product (GDP) at current market prices by other typologies=>urt_10r_3gdp"
## [14] "Average annual population to calculate regional GDP data by other typologies=>urt_10r_3pgdp"
## [15] "European Union trade mark (EUTM) applications per billion GDP by other typologies=>urt_ipr_tagdp"
## [16] "Community design (CD) applications per billion GDP by other typologies=>urt_ipr_dagdp"
## [17] "Community designs (CD) per billion GDP by other typologies=>urt_ipr_dfagdp"
## [18] "Candidate countries and potential candidates: GDP and main aggregates=>cpc_ecnagdp"
## [19] "Labour productivity in GDP (constant prices) per person employed=>enpe_nama_10_lp"
## [20] "Social protection expenditure excluding administrative costs (including health expenditure) as a share of GDP=>enpe_spr_exp"
## [21] "Health care expenditure as a share of GDP=>enpe_hlth_exp"
## [22] "International trade as a share of GDP=>med_ec4"
## [23] "ENP countries: GDP and main aggregates=>enpr_ecnagdp"
## [24] "GDP and main components (output, expenditure and income)=>nama_10_gdp"
## [25] "Main GDP aggregates per capita=>nama_10_pc"
## [26] "Gross domestic product (GDP) at current market prices by NUTS 2 regions=>nama_10r_2gdp"
## [27] "Gross domestic product (GDP) at current market prices by NUTS 3 regions=>nama_10r_3gdp"
## [28] "Average annual population to calculate regional GDP data (thousand persons) by NUTS 3 regions=>nama_10r_3popgdp"
## [29] "GDP and main components (output, expenditure and income)=>namq_10_gdp"
## [30] "Main GDP aggregates per capita=>namq_10_pc"
## [31] "GDP and main aggregates - selected international annual data=>naida_10_gdp"
## [32] "GDP and main aggregates - selected international quarterly data=>naidq_10_gdp"
## [33] "Main Balance of Payments and International Investment Position items as share of GDP (BPM6)=>bop_gdp6_q"
## [34] "EU direct investments indicators in % of GDP, impact indicators and rate of return on direct investment (BPM6)=>bop_fdi6_ind"
## [35] "Public expenditure on education by education level and programme orientation - as % of GDP=>educ_uoe_fine06"
## [36] "Expenditure on education as % of GDP or public expenditure=>educ_figdp"
## [37] "Tables by functions, aggregated benefits and grouped schemes - in % of the GDP=>spr_exp_gdp"
## [38] "Receipts - Tables by sector of origin and type, in % of the GDP=>spr_rec_gdp"
## [39] "Volume of freight transport relative to GDP=>tran_hv_frtra"
## [40] "Volume of passenger transport relative to GDP=>tran_hv_pstra"
## [41] "Environmental protection expenditure - euro per inhabitant and % of GDP=>env_ac_exp2"
## [42] "European Union trade mark (EUTM) applications per billion GDP=>ipr_ta_gdp"
## [43] "European Union trade mark (EUTM) applications per billion GDP by NUTS 3 regions=>ipr_ta_gdpr"
## [44] "Community design (CD) applications per billion GDP=>ipr_da_gdp"
## [45] "Community design (CD) applications per billion GDP by NUTS 3 regions=>ipr_da_gdpr"
## [46] "Percentage of the ICT sector in GDP=>isoc_bde15ag"
## [47] "Main GDP aggregates per capita=>nama_10_pc"
## [48] "Volume of freight transport relative to GDP=>tran_hv_frtra"
## [49] "Volume of passenger transport relative to GDP=>tran_hv_pstra"Dataset with id nama_10_gdp contains GDP and main components:
## tibble [852,535 × 5] (S3: tbl_df/tbl/data.frame)
## $ unit : chr [1:852535] "CLV05_MEUR" "CLV05_MEUR" "CLV05_MEUR" "CLV05_MEUR" ...
## $ na_item: chr [1:852535] "B1G" "B1G" "B1G" "B1G" ...
## $ geo : chr [1:852535] "AT" "BA" "BE" "BG" ...
## $ time : num [1:852535] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ values : num [1:852535] 261330 10368 320710 29408 421408 ...The nama_10_gdp dataset is a multidimensional cube with na_item (variable), unit (measure), geo and time dimensions. Display all possible values of na_item dimension:
## [38;5;246m# A tibble: 627 x 2[39m
## code_name full_name
## [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m
## [38;5;250m 1[39m B1GQ Gross domestic product at market prices
## [38;5;250m 2[39m B1G Value added, gross
## [38;5;250m 3[39m P1 Output
## [38;5;250m 4[39m P2 Intermediate consumption
## [38;5;250m 5[39m P3 Final consumption expenditure
## [38;5;250m 6[39m P31 Individual consumption expenditure
## [38;5;250m 7[39m P32 Collective consumption expenditure
## [38;5;250m 8[39m P4 Actual final consumption
## [38;5;250m 9[39m P3_S13 Final consumption expenditure of general government
## [38;5;250m10[39m P31_S13 Individual consumption expenditure of general government
## [38;5;246m# … with 617 more rows[39mWe will use Gross domestic product at market prices B1GQ. Display all possible values of unit dimension (units of measure):
## NULL## NULL## spec_tbl_df [4,016 × 2] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ code_name: chr [1:4016] "EUR" "EU" "EU_V" "EU27_2020_EFTA" ...
## $ full_name: chr [1:4016] "Europe" "European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020)" "European Union (aggregate changing according to the context)" "European Union - 27 countries (from 2020) and European Free Trade Association (EFTA) countries" ...
## - attr(*, "spec")=
## .. cols(
## .. .default = col_character(),
## .. code_name = col_character(),
## .. full_name = col_character()
## .. )## [38;5;246m# A tibble: 0 x 2[39m
## [38;5;246m# … with 2 variables: code_name <chr>, full_name <chr>[39mWe will use Current prices, million euro CP_MEUR. Now we get rid-off all values for na_item except B1GQ and CP_MEUR for unit:
## tibble [41 × 5] (S3: tbl_df/tbl/data.frame)
## $ unit : chr [1:41] "CP_MEUR" "CP_MEUR" "CP_MEUR" "CP_MEUR" ...
## $ na_item: chr [1:41] "B1GQ" "B1GQ" "B1GQ" "B1GQ" ...
## $ geo : chr [1:41] "AL" "AT" "BA" "BE" ...
## $ time : num [1:41] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ values : num [1:41] 12992 377297 17383 451177 60643 ...## NULLReduce geo to EU-member states only:
## tibble [27 × 5] (S3: tbl_df/tbl/data.frame)
## $ unit : chr [1:27] "CP_MEUR" "CP_MEUR" "CP_MEUR" "CP_MEUR" ...
## $ na_item: chr [1:27] "B1GQ" "B1GQ" "B1GQ" "B1GQ" ...
## $ geo : chr [1:27] "AT" "BE" "BG" "CY" ...
## $ time : num [1:27] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ values : num [1:27] 377297 451177 60643 20841 215257 ...Plot
We decide to use top six member states in further analysis (ie up to Poland.)
Total UE-27 GDP is 13348749.1, while total GDP for top six economies is 9735466.2 (72.9%)
Now query Eurostat database for Greenhouse Gases emissions (somehow we know that the dataset title contains Air emissions):
## [1] "Air emissions per sector and per gas=>med_en2"
## [2] "Air emissions accounts by NACE Rev. 2 activity=>env_ac_ainah_r2"
## [3] "Air emissions accounts totals bridging to emission inventory totals=>env_ac_aibrid_r2"
## [4] "Air emissions intensities by NACE Rev. 2 activity=>env_ac_aeint_r2"
## [5] "Air emissions accounts by NACE Rev. 2 activity=>env_ac_ainah_r2"
## [6] "Air emissions accounts totals bridging to emission inventory totals=>env_ac_aibrid_r2"
## [7] "Air emissions intensities by NACE Rev. 2 activity=>env_ac_aeint_r2"We will use env_ac_ainah_r2 dataset (Air emissions accounts by NACE)
## tibble [4,126,284 × 6] (S3: tbl_df/tbl/data.frame)
## $ airpol : chr [1:4126284] "ACG" "ACG" "ACG" "ACG" ...
## $ nace_r2: chr [1:4126284] "A" "A" "A" "A" ...
## $ unit : chr [1:4126284] "G_HAB" "G_HAB" "G_HAB" "G_HAB" ...
## $ geo : chr [1:4126284] "IS" "NL" "SE" "UK" ...
## $ time : num [1:4126284] 2019 2019 2019 2019 2019 ...
## $ values : num [1:4126284] 38850 14017.1 10017.3 7667.5 38.9 ...The env_ac_ainah_r2 dataset is a multidimensional cube with airpol (variable), nace_r2, unit (measure), geo and time dimensions. Display all possible values for airpol dimension:
## [1] "GHG= Greenhouse gases (CO2, N2O in CO2 equivalent, CH4 in CO2 equivalent, HFC in CO2 equivalent, PFC in CO2 equivalent, SF6 in CO2 equivalent, NF3 in CO2 equivalent)"
## [2] "CO2_N2O_CH4_CO2E= Greenhouse gases (CO2, N2O in CO2 equivalent, CH4 in CO2 equivalent)"
## [3] "CO2= Carbon dioxide"
## [4] "CH4= Methane"
## [5] "CH4_CO2E= Methane (CO2 equivalent)"
## [6] "N2O= Nitrous oxide"
## [7] "N2O_CO2E= Nitrous oxide (CO2 equivalent)"
## [8] "HFC_CO2E= Hydrofluorocarbones (CO2 equivalent)"
## [9] "PFC_CO2E= Perfluorocarbones (CO2 equivalent)"
## [10] "HFC_PFC_NSP_CO2E= Hydrofluorocarbones and perfluorocarbones - not specified mix (CO2 equivalent)"
## [11] "SF6_CO2E= Sulphur hexafluoride (CO2 equivalent)"
## [12] "NF3_CO2E= Nitrogen trifluoride (CO2 equivalent)"
## [13] "NF3_SF6= Nitrogen trifluoride and sulphur hexafluoride"
## [14] "NF3_SF6_CO2E= Nitrogen trifluoride and sulphur hexafluoride (CO2 equivalent)"
## [15] "CO2_BIO= Carbon dioxide from biomass used as a fuel"
## [16] "ACG= Acidifying gases (SOX in SO2 equivalent, NOX in SO2 equivalent, NH3 in SO2 equivalent)"
## [17] "SOX_SO2E= Sulphur oxides (SO2 equivalent)"
## [18] "NOX_NO2E= Nitrogen oxides (NO2 equivalent)"
## [19] "NOX_SO2E= Nitrogen oxides (SO2 equivalent)"
## [20] "NH3_SO2E= Ammonia (SO2 equivalent)"
## [21] "O3PR= Ozone precursors (NMVOC, NOX in NMVOC equivalent, CO in NMVOC equivalent, CH4 in NMVOC equivalent)"
## [22] "NOX_NMVOCE= Nitrogen oxides (NMVOC equivalent)"
## [23] "CO= Carbon monoxide"
## [24] "CO_NMVOCE= Carbon monoxide (NMVOC equivalent)"
## [25] "CH4_NMVOCE= Methane (NMVOC equivalent)"
## [26] "PM= Particulates"
## [27] "O3= Ozone"
## [28] "PB= Lead (Pb)"
## [29] "SO2= Sulphur dioxide"
## [30] "NOX= Nitrogen oxides"
## [31] "SOX= Sulphur oxides"
## [32] "NH3= Ammonia"
## [33] "PM2_5= Particulates < 2.5µm"
## [34] "PM10= Particulates < 10µm"
## [35] "NMVOC= Non-methane volatile organic compounds"
## [36] "AS= Arsenic (As)"
## [37] "CD= Cadmium (Cd)"
## [38] "HG= Mercury (Hg)"
## [39] "CR= Chromium (Cr)"
## [40] "CU= Copper (Cu)"
## [41] "NI= Nickel (Ni)"
## [42] "SE= Selenium (Se)"
## [43] "ZN= Zinc (Zn)"
## [44] "VOC= Volatile organic compounds"
## [45] "HFC= Hydrofluorocarbons"
## [46] "PFC= Perfluorocarbons"
## [47] "SF6= Sulphur hexafluoride"## tibble [4,126,284 × 6] (S3: tbl_df/tbl/data.frame)
## $ airpol : chr [1:4126284] "ACG" "ACG" "ACG" "ACG" ...
## $ nace_r2: chr [1:4126284] "A" "A" "A" "A" ...
## $ unit : chr [1:4126284] "G_HAB" "G_HAB" "G_HAB" "G_HAB" ...
## $ geo : chr [1:4126284] "IS" "NL" "SE" "UK" ...
## $ time : num [1:4126284] 2019 2019 2019 2019 2019 ...
## $ values : num [1:4126284] 38850 14017.1 10017.3 7667.5 38.9 ...## NULL## tibble [5,925 × 6] (S3: tbl_df/tbl/data.frame)
## $ airpol : chr [1:5925] "GHG" "GHG" "GHG" "GHG" ...
## $ nace_r2: chr [1:5925] "A" "A" "A" "A" ...
## $ unit : chr [1:5925] "THS_T" "THS_T" "THS_T" "THS_T" ...
## $ geo : chr [1:5925] "DE" "ES" "FR" "IT" ...
## $ time : num [1:5925] 2019 2019 2019 2019 2019 ...
## $ values : num [1:5925] 68496 50971 87367 39161 28233 ...GHG is an aggregated value of all Greenhouse gases.
Display all possible values for unit dimension:
## spec_tbl_df [0 × 2] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ code_name: chr(0)
## $ full_name: chr(0)
## - attr(*, "spec")=
## .. cols(
## .. .default = col_character(),
## .. code_name = col_character(),
## .. full_name = col_character()
## .. )## character(0)We will use THS_T (thousand tonnes). Filter out all values except GHG and THS_T. Limit the data set for 2008 onwards.
## grouped_df [72 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ geo : chr [1:72] "DE" "DE" "DE" "DE" ...
## $ time : num [1:72] 2008 2009 2010 2011 2012 ...
## $ geo_v: num [1:72] 3584477 3321679 3432438 3344088 3372081 ...
## - attr(*, "groups")= tibble [6 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ geo : chr [1:6] "DE" "ES" "FR" "IT" ...
## ..$ .rows: list<int> [1:6]
## .. ..$ : int [1:12] 1 2 3 4 5 6 7 8 9 10 ...
## .. ..$ : int [1:12] 13 14 15 16 17 18 19 20 21 22 ...
## .. ..$ : int [1:12] 25 26 27 28 29 30 31 32 33 34 ...
## .. ..$ : int [1:12] 37 38 39 40 41 42 43 44 45 46 ...
## .. ..$ : int [1:12] 49 50 51 52 53 54 55 56 57 58 ...
## .. ..$ : int [1:12] 61 62 63 64 65 66 67 68 69 70 ...
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE## [1] 2008## [1] 2019
## [38;5;246m# A tibble: 6 x 2[39m
## geo pp
## [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<dbl>[39m[23m
## [38;5;250m1[39m DE -[31m21[39m[31m.[39m[31m9[39m
## [38;5;250m2[39m ES -[31m28[39m[31m.[39m[31m1[39m
## [38;5;250m3[39m FR -[31m19[39m[31m.[39m[31m3[39m
## [38;5;250m4[39m IT -[31m34[39m[31m.[39m[31m4[39m
## [38;5;250m5[39m NL -[31m11[39m[31m.[39m[31m1[39m
## [38;5;250m6[39m PL -[31m5[39m[31m.[39m[31m45[39m
Conclusion: Poland is the only country with GHG increase.
But let’s check the GDP dynamics in analysed period:
## [1] "GDP and main components (output, expenditure and income)=>nama_10_gdp"
## [2] "GDP and main components (output, expenditure and income)=>namq_10_gdp"
## [3] "Gross domestic product (GDP) at current market prices by NUTS 2 regions=>nama_10r_2gdp"
## [4] "Average annual population to calculate regional GDP data (thousand persons) by NUTS 3 regions=>nama_10r_3popgdp"
## [5] "Gross domestic product (GDP) at current market prices by NUTS 3 regions=>nama_10r_3gdp"
## [6] "European Union trade mark (EUTM) applications per billion GDP by NUTS 3 regions=>ipr_ta_gdpr"
## [7] "Community design (CD) applications per billion GDP by NUTS 3 regions=>ipr_da_gdpr"
## [8] "Gross domestic product (GDP) at current market prices by metropolitan regions=>met_10r_3gdp"
## [9] "Average annual population to calculate regional GDP data by metropolitan regions=>met_10r_3pgdp"
## [10] "European Union trade mark (EUTM) applications per billion GDP by metropolitan regions=>met_ipr_tagdp"
## [11] "Community design (CD) applications per billion GDP by metropolitan regions=>met_ipr_dagdp"
## [12] "Community designs (CD) per billion GDP by metropolitan regions=>met_ipr_dfagdp"
## [13] "Gross domestic product (GDP) at current market prices by other typologies=>urt_10r_3gdp"
## [14] "Average annual population to calculate regional GDP data by other typologies=>urt_10r_3pgdp"
## [15] "European Union trade mark (EUTM) applications per billion GDP by other typologies=>urt_ipr_tagdp"
## [16] "Community design (CD) applications per billion GDP by other typologies=>urt_ipr_dagdp"
## [17] "Community designs (CD) per billion GDP by other typologies=>urt_ipr_dfagdp"
## [18] "Candidate countries and potential candidates: GDP and main aggregates=>cpc_ecnagdp"
## [19] "Labour productivity in GDP (constant prices) per person employed=>enpe_nama_10_lp"
## [20] "Social protection expenditure excluding administrative costs (including health expenditure) as a share of GDP=>enpe_spr_exp"
## [21] "Health care expenditure as a share of GDP=>enpe_hlth_exp"
## [22] "International trade as a share of GDP=>med_ec4"
## [23] "ENP countries: GDP and main aggregates=>enpr_ecnagdp"
## [24] "GDP and main components (output, expenditure and income)=>nama_10_gdp"
## [25] "Main GDP aggregates per capita=>nama_10_pc"
## [26] "Gross domestic product (GDP) at current market prices by NUTS 2 regions=>nama_10r_2gdp"
## [27] "Gross domestic product (GDP) at current market prices by NUTS 3 regions=>nama_10r_3gdp"
## [28] "Average annual population to calculate regional GDP data (thousand persons) by NUTS 3 regions=>nama_10r_3popgdp"
## [29] "GDP and main components (output, expenditure and income)=>namq_10_gdp"
## [30] "Main GDP aggregates per capita=>namq_10_pc"
## [31] "GDP and main aggregates - selected international annual data=>naida_10_gdp"
## [32] "GDP and main aggregates - selected international quarterly data=>naidq_10_gdp"
## [33] "Main Balance of Payments and International Investment Position items as share of GDP (BPM6)=>bop_gdp6_q"
## [34] "EU direct investments indicators in % of GDP, impact indicators and rate of return on direct investment (BPM6)=>bop_fdi6_ind"
## [35] "Public expenditure on education by education level and programme orientation - as % of GDP=>educ_uoe_fine06"
## [36] "Expenditure on education as % of GDP or public expenditure=>educ_figdp"
## [37] "Tables by functions, aggregated benefits and grouped schemes - in % of the GDP=>spr_exp_gdp"
## [38] "Receipts - Tables by sector of origin and type, in % of the GDP=>spr_rec_gdp"
## [39] "Volume of freight transport relative to GDP=>tran_hv_frtra"
## [40] "Volume of passenger transport relative to GDP=>tran_hv_pstra"
## [41] "Environmental protection expenditure - euro per inhabitant and % of GDP=>env_ac_exp2"
## [42] "European Union trade mark (EUTM) applications per billion GDP=>ipr_ta_gdp"
## [43] "European Union trade mark (EUTM) applications per billion GDP by NUTS 3 regions=>ipr_ta_gdpr"
## [44] "Community design (CD) applications per billion GDP=>ipr_da_gdp"
## [45] "Community design (CD) applications per billion GDP by NUTS 3 regions=>ipr_da_gdpr"
## [46] "Percentage of the ICT sector in GDP=>isoc_bde15ag"
## [47] "Main GDP aggregates per capita=>nama_10_pc"
## [48] "Volume of freight transport relative to GDP=>tran_hv_frtra"
## [49] "Volume of passenger transport relative to GDP=>tran_hv_pstra"## [1] "GDP per capita in PPS=>tec00114"
## [2] "Real GDP growth rate - volume=>tec00115"
## [3] "Exports of goods and services in % of GDP=>tet00003"
## [4] "Imports of goods and services in % of GDP=>tet00004"
## [5] "Real GDP per capita=>sdg_08_10"
## [6] "GDP deflator=>teina110"
## [7] "GDP per capita in PPS=>tec00114"
## [8] "Volume of passenger transport relative to GDP=>ttr00001"
## [9] "Percentage of the ICT sector on GDP=>tin00074"
## [10] "Private sector credit flow, consolidated - % GDP=>tipspc20"
## [11] "Private sector debt, consolidated - % of GDP=>tipspd20"
## [12] "Current account, main components, net balance - annual data, % of GDP=>tipsbp11"
## [13] "Current account, main component, credit - annual data, % of GDP=>tipsbp12"
## [14] "Current account, main components, debit - annual data, % of GDP=>tipsbp13"
## [15] "Direct investment in the reporting economy (flows) - annual data, % of GDP=>tipsbp90"
## [16] "Direct investment in the reporting economy (stocks) - annual data, % of GDP=>tipsbp100"
## [17] "Current account, main components, net balance - quarterly data, % of GDP=>tipsbp41"
## [18] "Current account, main components, credit - quarterly data, % of GDP=>tipsbp42"
## [19] "Current account, main components, debit - quarterly data, % of GDP=>tipsbp43"
## [20] "Direct investment in the reporting economy - quarterly data, % of GDP=>tipsbp51"
## [21] "Direct investment abroad - quarterly data, % of GDP=>tipsbp53"
## [22] "Net international investment position - quarterly data, % of GDP=>tipsii40"
## [23] "Net external debt - annual data, % of GDP=>tipsii20"
## [24] "Net external debt - quarterly data, % of GDP=>tipsii30"
## [25] "Net international investment position excluding non-defaultable instruments - % of GDP=>tipsii50"
## [26] "Net trade balance of energy products - % of GDP=>tipsen10"
## [27] "Total financial sector liabilities, by sub-sectors, non-consolidated - % of GDP=>tipsfs11"
## [28] "Total financial sector liabilities, by instruments, non-consolidated - % of GDP=>tipsfs13"
## [29] "Total financial sector liabilities, by sub-sectors, consolidated - % of GDP=>tipsfs31"
## [30] "Total financial sector liabilities, by instruments, consolidated - % of GDP=>tipsfs33"
## [31] "Private sector debt: debt securities by sectors, non-consolidated - % of GDP=>tipspd13"
## [32] "Private sector debt: loans, by sectors, non-consolidated - % of GDP=>tipspd15"
## [33] "Household debt, consolidated including Non-profit institutions serving households - % of GDP=>tipspd22"
## [34] "Private sector debt: debt securities, by sectors, consolidated - % of GDP=>tipspd23"
## [35] "Private sector debt: loans, by sectors, consolidated - % of GDP=>tipspd25"
## [36] "Private sector credit flow: debt securities by sectors, non-consolidated - % of GDP=>tipspc13"
## [37] "Private sector credit flow: loans by sectors, non-consolidated - % of GDP=>tipspc15"
## [38] "Private sector credit flow: debt securities by sectors, consolidated - % of GDP=>tipspc23"
## [39] "Private sector credit flow: loans by sectors, consolidated - % of GDP=>tipspc25"
## [40] "Residential construction - annual data, % of GDP=>tipsna50"
## [41] "Gross domestic product (GDP) at market prices - annual data=>tipsau10"
## [42] "Gross domestic product (GDP) at market prices - quarterly data=>tipsau20"
## [43] "Balance of payments, current account, quarterly data - % of GDP=>teibp051"
## [44] "GDP deflator=>teina110"
## [45] "Generation of waste excluding major mineral wastes per GDP unit=>cei_pc032"
## [46] "Real GDP per capita=>sdg_08_10"
## [47] "Investment share of GDP by institutional sectors=>sdg_08_11"
## [48] "Purchasing power adjusted GDP per capita=>sdg_10_10"
## [49] "Exports of goods and services in % of GDP=>tet00003"
## [50] "Imports of goods and services in % of GDP=>tet00004"
## [51] "Inward FDI stocks in % of GDP=>tec00105"
## [52] "Outward FDI stocks in % of GDP=>tec00106"We will use tec00114 table (GDP per capita in PPS):
## tibble [527 × 5] (S3: tbl_df/tbl/data.frame)
## $ na_item: chr [1:527] "VI_PPS_EU27_2020_HAB" "VI_PPS_EU27_2020_HAB" "VI_PPS_EU27_2020_HAB" "VI_PPS_EU27_2020_HAB" ...
## $ ppp_cat: chr [1:527] "GDP" "GDP" "GDP" "GDP" ...
## $ geo : chr [1:527] "AL" "AT" "BA" "BE" ...
## $ time : num [1:527] 2009 2009 2009 2009 2009 ...
## $ values : num [1:527] 28 128 30 118 44 167 106 87 118 127 ...## [1] "GDP= Gross domestic product"
## [2] "A01= Actual individual consumption"
## [3] "A0101= Food and non-alcoholic beverages"
## [4] "A010101= Food"
## [5] "A01010101= Bread and cereals"
## [6] "A01010102= Meat"
## [7] "A01010103= Fish"
## [8] "A01010104= Milk, cheese and eggs"
## [9] "A01010105= Oils and fats"
## [10] "A01010106= Fruits, vegetables, potatoes"
## [11] "A01010199= Other food"
## [12] "A010102= Non-alcoholic beverages"
## [13] "A0102= Alcoholic beverages, tobacco and narcotics"
## [14] "A010201= Alcoholic beverages"
## [15] "A010202= Tobacco"
## [16] "A0103= Clothing and footwear"
## [17] "A010301= Clothing"
## [18] "A010302= Footwear"
## [19] "A0104= Housing, water, electricity, gas and other fuels"
## [20] "A010405= Electricity, gas and other fuels"
## [21] "A0105= Household furnishings, equipment and maintenance"
## [22] "A010501= Furniture and furnishings, carpets and other floor coverings"
## [23] "A010503= Households appliances"
## [24] "A0106= Health"
## [25] "A010603= Hospital Services"
## [26] "A0107= Transport"
## [27] "A010701= Personal transport equipment"
## [28] "A010703= Transport services"
## [29] "A0108= Communication"
## [30] "A0109= Recreation and culture"
## [31] "A010901= Audio-visual, photographic and information processing equipment"
## [32] "A0110= Education"
## [33] "A0111= Restaurants and hotels"
## [34] "A0112= Miscellaneous goods and services"
## [35] "A04= Actual collective consumption"
## [36] "A05= Gross fixed capital formation"
## [37] "A0501= Machinery and equipment"
## [38] "A050101= Fabricated metal products and equipment (except electrical and optical equipment)"
## [39] "A050102= Electrical and optical equipment"
## [40] "A050103= Transport equipment"
## [41] "A0502= Construction"
## [42] "A050201= Residential buildings"
## [43] "A050202= Non-residential buildings"
## [44] "A050203= Civil engineering works"
## [45] "A0503= Software"
## [46] "E01= Final consumption expenditure"
## [47] "E011= Household final consumption expenditure"
## [48] "E012= Government final consumption expenditure"
## [49] "E0121= Collective consumption expenditure"
## [50] "E0122= Individual consumption expenditure"
## [51] "P01= Total goods"
## [52] "P0101= Consumer goods"
## [53] "P010101= Non-durable goods"
## [54] "P010102= Semi-durable goods"
## [55] "P010103= Durable goods"
## [56] "P0102= Capital goods"
## [57] "P02= Total services"
## [58] "P0201= Consumer services"
## [59] "P0202= Government services"
## [60] "P020201= Collective services"
## [61] "P020202= Individual services"## tibble [72 × 5] (S3: tbl_df/tbl/data.frame)
## $ na_item: chr [1:72] "VI_PPS_EU27_2020_HAB" "VI_PPS_EU27_2020_HAB" "VI_PPS_EU27_2020_HAB" "VI_PPS_EU27_2020_HAB" ...
## $ ppp_cat: chr [1:72] "GDP" "GDP" "GDP" "GDP" ...
## $ geo : chr [1:72] "DE" "ES" "FR" "IT" ...
## $ time : num [1:72] 2009 2009 2009 2009 2009 ...
## $ values : num [1:72] 118 101 109 108 140 60 121 96 109 106 ...## [1] 2009## [1] 2020## [1] 72## [38;5;246m# A tibble: 72 x 5[39m
## na_item ppp_cat geo time values
## [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<dbl>[39m[23m [3m[38;5;246m<dbl>[39m[23m
## [38;5;250m 1[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m009 118
## [38;5;250m 2[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m009 101
## [38;5;250m 3[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m009 109
## [38;5;250m 4[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m009 108
## [38;5;250m 5[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m009 140
## [38;5;250m 6[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m009 60
## [38;5;250m 7[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m010 121
## [38;5;250m 8[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m010 96
## [38;5;250m 9[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m010 109
## [38;5;250m10[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m010 106
## [38;5;250m11[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m010 137
## [38;5;250m12[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m010 63
## [38;5;250m13[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m011 124
## [38;5;250m14[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m011 93
## [38;5;250m15[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m011 109
## [38;5;250m16[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m011 105
## [38;5;250m17[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m011 135
## [38;5;250m18[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m011 66
## [38;5;250m19[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m012 124
## [38;5;250m20[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m012 91
## [38;5;250m21[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m012 108
## [38;5;250m22[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m012 103
## [38;5;250m23[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m012 136
## [38;5;250m24[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m012 67
## [38;5;250m25[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m013 125
## [38;5;250m26[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m013 90
## [38;5;250m27[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m013 110
## [38;5;250m28[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m013 100
## [38;5;250m29[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m013 137
## [38;5;250m30[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m013 67
## [38;5;250m31[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m014 127
## [38;5;250m32[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m014 91
## [38;5;250m33[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m014 108
## [38;5;250m34[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m014 98
## [38;5;250m35[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m014 133
## [38;5;250m36[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m014 68
## [38;5;250m37[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m015 125
## [38;5;250m38[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m015 91
## [38;5;250m39[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m015 107
## [38;5;250m40[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m015 97
## [38;5;250m41[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m015 132
## [38;5;250m42[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m015 69
## [38;5;250m43[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m016 125
## [38;5;250m44[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m016 92
## [38;5;250m45[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m016 106
## [38;5;250m46[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m016 98
## [38;5;250m47[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m016 129
## [38;5;250m48[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m016 69
## [38;5;250m49[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m017 124
## [38;5;250m50[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m017 93
## [38;5;250m51[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m017 104
## [38;5;250m52[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m017 98
## [38;5;250m53[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m017 129
## [38;5;250m54[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m017 70
## [38;5;250m55[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m018 123
## [38;5;250m56[39m VI_PPS_EU27_2020_HAB GDP ES [4m2[24m018 91
## [38;5;250m57[39m VI_PPS_EU27_2020_HAB GDP FR [4m2[24m018 104
## [38;5;250m58[39m VI_PPS_EU27_2020_HAB GDP IT [4m2[24m018 97
## [38;5;250m59[39m VI_PPS_EU27_2020_HAB GDP NL [4m2[24m018 130
## [38;5;250m60[39m VI_PPS_EU27_2020_HAB GDP PL [4m2[24m018 71
## [38;5;246m# … with 12 more rows[39m
## [38;5;246m# A tibble: 72 x 5[39m
## na_item ppp_cat geo time values
## [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<dbl>[39m[23m [3m[38;5;246m<dbl>[39m[23m
## [38;5;250m 1[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m009 118
## [38;5;250m 2[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m010 121
## [38;5;250m 3[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m011 124
## [38;5;250m 4[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m012 124
## [38;5;250m 5[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m013 125
## [38;5;250m 6[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m014 127
## [38;5;250m 7[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m015 125
## [38;5;250m 8[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m016 125
## [38;5;250m 9[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m017 124
## [38;5;250m10[39m VI_PPS_EU27_2020_HAB GDP DE [4m2[24m018 123
## [38;5;246m# … with 62 more rows[39m## [38;5;246m# A tibble: 6 x 2[39m
## geo pp
## [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<dbl>[39m[23m
## [38;5;250m1[39m DE 2.48
## [38;5;250m2[39m ES -[31m17[39m[31m.[39m[31m4[39m
## [38;5;250m3[39m FR -[31m5[39m[31m.[39m[31m83[39m
## [38;5;250m4[39m IT -[31m14[39m[31m.[39m[31m9[39m
## [38;5;250m5[39m NL -[31m5[39m[31m.[39m[31m26[39m
## [38;5;250m6[39m PL 21.1Plot
Conlusion: Poland GDP growth in analysed period was 20% while most countries have negative growth, except Germany with 4% GDP increase within 2008–2018 period.
We can now ‘adjust’ GHG growth by GDP growth (dividing GHG by GDP)
## [38;5;246m# A tibble: 6 x 2[39m
## geo pp
## [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<dbl>[39m[23m
## [38;5;250m1[39m DE -[31m25[39m[31m.[39m[31m0[39m
## [38;5;250m2[39m ES -[31m9[39m[31m.[39m[31m0[39m[31m6[39m
## [38;5;250m3[39m FR -[31m12[39m[31m.[39m[31m7[39m
## [38;5;250m4[39m IT -[31m16[39m[31m.[39m[31m9[39m
## [38;5;250m5[39m NL -[31m5[39m[31m.[39m[31m55[39m
## [38;5;250m6[39m PL -[31m33[39m[31m.[39m[31m6[39m
Conclusion: Poland now is the best.
All in one chart
Which country performance is the worst one?