These datasets shows a state of the data prior to becoming an input for this package.
Usage
example_emission_product_co2_des_analysis()
example_transition_risk_product_trs_des_analysis()
example_transition_risk_emission_ep_product_des_analysis()
example_transition_risk_sector_ep_product_des_analysis()
example_transition_risk_ep_product_des_analysis()
example_transition_risk_product_emission_cov()
example_transition_risk_product_sector_cov()
example_transition_risk_product_transition_risk_cov()
example_emission_product_best_case_worst_case()
example_sector_product_best_case_worst_case()
example_transition_risk_company_emission_avg_best_case_worst_case()
example_transition_risk_company_sector_avg_best_case_worst_case()
example_transition_risk_product_best_case_worst_case()
example_transition_risk_company_transition_risk_avg_best_case_worst_case()Examples
example_emission_product_co2_des_analysis()
#> # A tibble: 8 × 8
#> grouping_emission tilt_sector tilt_subsector unit isic_4digit co2_footprint
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 tilt_subsector group chemicals kg '5555' 1
#> 2 tilt_subsector group chemicals kg '5555' 4
#> 3 tilt_subsector group chemicals kg '5555' 9
#> 4 tilt_subsector group chemicals kg '5555' 8
#> 5 tilt_subsector group chemicals kg '5555' NA
#> 6 tilt_subsector group iron & steel m3 '6666' 1
#> 7 tilt_subsector group iron & steel m3 '6666' 2
#> 8 tilt_subsector group iron & steel m3 '6666' 4
#> # ℹ 2 more variables: product <chr>, activity_uuid_product_uuid <chr>
example_transition_risk_product_trs_des_analysis()
#> # A tibble: 16 × 3
#> tilt_subsector grouping_transition_risk transition_risk_score
#> <chr> <chr> <dbl>
#> 1 chemicals 1.5C RPS_2030_tilt_subsector 1
#> 2 chemicals 1.5C RPS_2030_tilt_subsector 2
#> 3 chemicals 1.5C RPS_2050_tilt_subsector 3
#> 4 chemicals 1.5C RPS_2050_tilt_subsector 4
#> 5 chemicals NZ 2050_2030_tilt_subsector 5
#> 6 chemicals NZ 2050_2030_tilt_subsector 6
#> 7 chemicals NZ 2050_2050_tilt_subsector 7
#> 8 chemicals NZ 2050_2050_tilt_subsector 8
#> 9 iron & steel 1.5C RPS_2030_tilt_subsector 9
#> 10 iron & steel 1.5C RPS_2030_tilt_subsector 10
#> 11 iron & steel 1.5C RPS_2050_tilt_subsector 11
#> 12 iron & steel 1.5C RPS_2050_tilt_subsector 12
#> 13 iron & steel NZ 2050_2030_tilt_subsector 13
#> 14 iron & steel NZ 2050_2030_tilt_subsector 14
#> 15 iron & steel NZ 2050_2050_tilt_subsector 15
#> 16 iron & steel NZ 2050_2050_tilt_subsector NA
example_transition_risk_product_emission_cov()
#> # A tibble: 12 × 3
#> companies_id grouping_emission cov_emission_rank
#> <chr> <chr> <dbl>
#> 1 comp_1 all 10
#> 2 comp_1 isic_4digit 15
#> 3 comp_1 tilt_subsector 20
#> 4 comp_1 unit 25
#> 5 comp_1 unit_isic_4digit 30
#> 6 comp_1 unit_tilt_subsector 35
#> 7 comp_2 all 45
#> 8 comp_2 isic_4digit 50
#> 9 comp_2 tilt_subsector 55
#> 10 comp_2 unit 60
#> 11 comp_2 unit_isic_4digit 65
#> 12 comp_2 unit_tilt_subsector 70
example_transition_risk_product_sector_cov()
#> # A tibble: 8 × 4
#> companies_id scenario year cov_sector_target
#> <chr> <chr> <dbl> <dbl>
#> 1 comp_1 1.5C RPS 2030 10
#> 2 comp_1 1.5C RPS 2050 15
#> 3 comp_1 NZ 2050 2030 20
#> 4 comp_1 NZ 2050 2050 25
#> 5 comp_2 1.5C RPS 2030 45
#> 6 comp_2 1.5C RPS 2050 50
#> 7 comp_2 NZ 2050 2030 55
#> 8 comp_2 NZ 2050 2050 60
example_transition_risk_product_transition_risk_cov()
#> # A tibble: 8 × 3
#> companies_id grouping_transition_risk cov_transition_risk
#> <chr> <chr> <dbl>
#> 1 comp_1 1.5C RPS_2030_tilt_subsector 10
#> 2 comp_1 1.5C RPS_2050_tilt_subsector 15
#> 3 comp_1 NZ 2050_2030_tilt_subsector 20
#> 4 comp_1 NZ 2050_2050_tilt_subsector 25
#> 5 comp_2 1.5C RPS_2030_tilt_subsector 45
#> 6 comp_2 1.5C RPS_2050_tilt_subsector 50
#> 7 comp_2 NZ 2050_2030_tilt_subsector 55
#> 8 comp_2 NZ 2050_2050_tilt_subsector 60
example_emission_product_best_case_worst_case()
#> # A tibble: 30 × 7
#> companies_id grouping_emission country emission_category
#> <chr> <chr> <chr> <chr>
#> 1 comp_1 all france high
#> 2 comp_2 all france high
#> 3 comp_1 all france high
#> 4 comp_1 all france medium
#> 5 comp_1 all france low
#> 6 comp_1 all france low
#> 7 comp_1 all france NA
#> 8 comp_1 tilt_subsector france high
#> 9 comp_1 tilt_subsector france medium
#> 10 comp_1 tilt_subsector france medium
#> # ℹ 20 more rows
#> # ℹ 3 more variables: emissions_profile_equal_weight <dbl>,
#> # emissions_profile_best_case <dbl>, emissions_profile_worst_case <dbl>
example_transition_risk_company_emission_avg_best_case_worst_case()
#> # A tibble: 14 × 6
#> companies_id country grouping_emission emission_rank_avg_equal_weight
#> <chr> <chr> <chr> <dbl>
#> 1 comp_1 france all 0.25
#> 2 comp_1 france tilt_subsector 0.85
#> 3 comp_2 france all 0.1
#> 4 comp_2 france tilt_subsector 0.3
#> 5 comp_3 austria all 0.35
#> 6 comp_3 austria tilt_subsector 0.55
#> 7 comp_4 austria all 0.7
#> 8 comp_4 austria tilt_subsector 0.2
#> 9 comp_5 germany all 0.7
#> 10 comp_5 germany tilt_subsector 0.2
#> 11 comp_6 netherlands all 0.7
#> 12 comp_6 netherlands tilt_subsector 0.2
#> 13 comp_7 spain all 0.7
#> 14 comp_7 spain tilt_subsector 0.2
#> # ℹ 2 more variables: emission_rank_avg_best_case <dbl>,
#> # emission_rank_avg_worst_case <dbl>
example_transition_risk_company_sector_avg_best_case_worst_case()
#> # A tibble: 14 × 7
#> companies_id country scenario year sector_target_avg_equal_weight
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 comp_1 france 1.5C RPS 2030 0.25
#> 2 comp_1 france NZ 2050 2030 0.85
#> 3 comp_2 france 1.5C RPS 2030 0.1
#> 4 comp_2 france NZ 2050 2030 0.3
#> 5 comp_3 austria 1.5C RPS 2030 0.35
#> 6 comp_3 austria NZ 2050 2030 0.55
#> 7 comp_4 austria 1.5C RPS 2030 0.7
#> 8 comp_4 austria NZ 2050 2030 0.2
#> 9 comp_5 germany 1.5C RPS 2030 0.7
#> 10 comp_5 germany NZ 2050 2030 0.2
#> 11 comp_6 netherlands 1.5C RPS 2030 0.7
#> 12 comp_6 netherlands NZ 2050 2030 0.2
#> 13 comp_7 spain 1.5C RPS 2030 0.7
#> 14 comp_7 spain NZ 2050 2030 0.2
#> # ℹ 2 more variables: sector_target_avg_best_case <dbl>,
#> # sector_target_avg_worst_case <dbl>
example_transition_risk_product_best_case_worst_case()
#> # A tibble: 30 × 7
#> companies_id grouping_transition_risk country transition_risk_category
#> <chr> <chr> <chr> <chr>
#> 1 comp_1 1.5C RPS_2030_all france high
#> 2 comp_2 1.5C RPS_2030_all france high
#> 3 comp_1 1.5C RPS_2030_all france high
#> 4 comp_1 1.5C RPS_2030_all france medium
#> 5 comp_1 1.5C RPS_2030_all france low
#> 6 comp_1 1.5C RPS_2030_all france low
#> 7 comp_1 1.5C RPS_2030_all france NA
#> 8 comp_1 NZ 2050_2030_all france high
#> 9 comp_1 NZ 2050_2030_all france medium
#> 10 comp_1 NZ 2050_2030_all france medium
#> # ℹ 20 more rows
#> # ℹ 3 more variables: transition_risk_profile_equal_weight <dbl>,
#> # transition_risk_profile_best_case <dbl>,
#> # transition_risk_profile_worst_case <dbl>
example_transition_risk_company_transition_risk_avg_best_case_worst_case()
#> # A tibble: 14 × 6
#> companies_id country grouping_transition_risk avg_transition_risk_equal…¹
#> <chr> <chr> <chr> <dbl>
#> 1 comp_1 france 1.5C RPS_2030_all 0.25
#> 2 comp_1 france NZ 2050_2030_all 0.85
#> 3 comp_2 france 1.5C RPS_2030_all 0.1
#> 4 comp_2 france NZ 2050_2030_all 0.3
#> 5 comp_3 austria 1.5C RPS_2030_all 0.35
#> 6 comp_3 austria NZ 2050_2030_all 0.55
#> 7 comp_4 austria 1.5C RPS_2030_all 0.7
#> 8 comp_4 austria NZ 2050_2030_all 0.2
#> 9 comp_5 germany 1.5C RPS_2030_all 0.7
#> 10 comp_5 germany NZ 2050_2030_all 0.2
#> 11 comp_6 netherlands 1.5C RPS_2030_all 0.7
#> 12 comp_6 netherlands NZ 2050_2030_all 0.2
#> 13 comp_7 spain 1.5C RPS_2030_all 0.7
#> 14 comp_7 spain NZ 2050_2030_all 0.2
#> # ℹ abbreviated name: ¹avg_transition_risk_equal_weight
#> # ℹ 2 more variables: avg_transition_risk_best_case <dbl>,
#> # avg_transition_risk_worst_case <dbl>
example_transition_risk_emission_ep_product_des_analysis()
#> # A tibble: 10 × 4
#> companies_id product grouping_emission emission_category
#> <chr> <chr> <chr> <chr>
#> 1 comp_1 a all high
#> 2 comp_1 b all high
#> 3 comp_1 c all high
#> 4 comp_1 d NA NA
#> 5 comp_1 e NA NA
#> 6 comp_2 a all high
#> 7 comp_2 b all high
#> 8 comp_2 c all high
#> 9 comp_3 d NA NA
#> 10 comp_3 e NA NA
example_transition_risk_sector_ep_product_des_analysis()
#> # A tibble: 10 × 4
#> companies_id product sector_target sector_category
#> <chr> <chr> <dbl> <chr>
#> 1 comp_1 a 0.12 high
#> 2 comp_1 b 0.96 high
#> 3 comp_1 c 0.64 high
#> 4 comp_1 d NA NA
#> 5 comp_1 e NA NA
#> 6 comp_2 a 0.12 high
#> 7 comp_2 b 0.96 high
#> 8 comp_2 c 0.64 high
#> 9 comp_3 d NA NA
#> 10 comp_3 e NA NA
example_transition_risk_ep_product_des_analysis()
#> # A tibble: 10 × 4
#> companies_id product grouping_transition_risk transition_risk_category
#> <chr> <chr> <chr> <chr>
#> 1 comp_1 a 1.5C RPS_2030_all high
#> 2 comp_1 b 1.5C RPS_2030_all high
#> 3 comp_1 c 1.5C RPS_2030_all high
#> 4 comp_1 d NA NA
#> 5 comp_1 e NA NA
#> 6 comp_2 a 1.5C RPS_2030_all high
#> 7 comp_2 b 1.5C RPS_2030_all high
#> 8 comp_2 c 1.5C RPS_2030_all high
#> 9 comp_3 d NA NA
#> 10 comp_3 e NA NA
example_sector_product_best_case_worst_case()
#> # A tibble: 30 × 8
#> companies_id scenario year country sector_category sector_profile_equal_we…¹
#> <chr> <chr> <dbl> <chr> <chr> <dbl>
#> 1 comp_1 1.5C RPS 2030 france high 0.25
#> 2 comp_2 1.5C RPS 2030 france high 0.25
#> 3 comp_1 1.5C RPS 2030 france high 0.75
#> 4 comp_1 1.5C RPS 2030 france medium 0.1
#> 5 comp_1 1.5C RPS 2030 france low 0.15
#> 6 comp_1 1.5C RPS 2030 france low 0.55
#> 7 comp_1 1.5C RPS 2030 france NA 0.3
#> 8 comp_1 NZ 2050 2030 france high 0.45
#> 9 comp_1 NZ 2050 2030 france medium 0.6
#> 10 comp_1 NZ 2050 2030 france medium 0.4
#> # ℹ 20 more rows
#> # ℹ abbreviated name: ¹sector_profile_equal_weight
#> # ℹ 2 more variables: sector_profile_best_case <dbl>,
#> # sector_profile_worst_case <dbl>