Load the internal datasets
assets_testdata <- read.csv(system.file("testdata", "assets_testdata.csv", package = "trisk.model"))
scenarios_testdata <- read.csv(system.file("testdata", "scenarios_testdata.csv", package = "trisk.model"))
financial_features_testdata <- read.csv(system.file("testdata", "financial_features_testdata.csv", package = "trisk.model"))
ngfs_carbon_price_testdata <- read.csv(system.file("testdata", "ngfs_carbon_price_testdata.csv", package = "trisk.model"))Datasets
Assets Test Data
This dataset contains data about company assets, including production, technology, and geographical details.
Data Description
The assets_testdata dataset includes the following
columns:
-
company_id: Unique identifier for the company. -
company_name: Name of the company. -
plan_sec_prod: Secondary production plan. -
country_name: Country where the asset is located. -
plant_age_years: Age of the plant in years. -
workforce_size: Size of the workforce. -
asset_id: Unique identifier for the asset. -
country_iso2: ISO 3166-1 alpha-2 code for the country. -
asset_name: Name of the asset. -
production_year: Year of production data. -
emission_factor: Emissions from production. -
technology: Type of technology used. -
sector: Production sector. -
capacity: Asset capacity. -
capacity_factor: Asset utilization percentage. -
production_unit: Unit for production.
Data Structure
str(assets_testdata)
#> 'data.frame': 42 obs. of 12 variables:
#> $ company_id : int 101 101 101 101 101 101 102 102 102 102 ...
#> $ company_name : chr "Company 1" "Company 1" "Company 1" "Company 1" ...
#> $ asset_id : int 101 101 101 101 101 101 102 102 102 102 ...
#> $ country_iso2 : chr "DE" "DE" "DE" "DE" ...
#> $ asset_name : chr "Company 1" "Company 1" "Company 1" "Company 1" ...
#> $ production_year: int 2022 2023 2024 2025 2026 2027 2022 2023 2024 2025 ...
#> $ emission_factor: num 0.062 0.062 0.062 0.062 0.062 ...
#> $ technology : chr "Gas" "Gas" "Gas" "Gas" ...
#> $ sector : chr "Oil&Gas" "Oil&Gas" "Oil&Gas" "Oil&Gas" ...
#> $ capacity : num 8600 8600 8600 8600 8600 ...
#> $ capacity_factor: num 0.581 0.631 0.721 0.86 0.907 ...
#> $ production_unit: chr "GJ" "GJ" "GJ" "GJ" ...Sample Data
knitr::kable(head(assets_testdata)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
kableExtra::scroll_box(width = "100%", height = "400px") %>%
kableExtra::column_spec(1:ncol(assets_testdata), width = "150px")| company_id | company_name | asset_id | country_iso2 | asset_name | production_year | emission_factor | technology | sector | capacity | capacity_factor | production_unit |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 101 | Company 1 | 101 | DE | Company 1 | 2022 | 0.0620259 | Gas | Oil&Gas | 8600 | 0.5813953 | GJ |
| 101 | Company 1 | 101 | DE | Company 1 | 2023 | 0.0620259 | Gas | Oil&Gas | 8600 | 0.6305814 | GJ |
| 101 | Company 1 | 101 | DE | Company 1 | 2024 | 0.0620259 | Gas | Oil&Gas | 8600 | 0.7209302 | GJ |
| 101 | Company 1 | 101 | DE | Company 1 | 2025 | 0.0620259 | Gas | Oil&Gas | 8600 | 0.8604651 | GJ |
| 101 | Company 1 | 101 | DE | Company 1 | 2026 | 0.0620259 | Gas | Oil&Gas | 8600 | 0.9069767 | GJ |
| 101 | Company 1 | 101 | DE | Company 1 | 2027 | 0.0620259 | Gas | Oil&Gas | 8600 | 1.0000000 | GJ |
Financial Features Test Data
This dataset contains financial metrics necessary for calculating stress test outputs.
Data Description
The financial_features_testdata dataset includes the
following columns:
-
company_id: Unique identifier for the company. -
pd: Probability of default for the company. -
net_profit_margin: Net profit margin for the company. -
debt_equity_ratio: Debt to equity ratio. -
volatility: Volatility of the company’s asset values.
Data Structure
str(financial_features_testdata)
#> 'data.frame': 5 obs. of 5 variables:
#> $ company_id : int 101 103 105 104 102
#> $ pd : num 0.00562 0.00398 0.00246 0.00298 0.00365
#> $ net_profit_margin: num 0.0764 0.0717 0.0539 0.0539 0.1058
#> $ debt_equity_ratio: num 0.13 0.128 0.119 0.11 0.104
#> $ volatility : num 0.259 0.251 0.236 0.251 0.317Sample Data
knitr::kable(head(financial_features_testdata)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
kableExtra::scroll_box(width = "100%", height = "400px")| company_id | pd | net_profit_margin | debt_equity_ratio | volatility |
|---|---|---|---|---|
| 101 | 0.0056224 | 0.0763542 | 0.1297317 | 0.2593230 |
| 103 | 0.0039782 | 0.0716949 | 0.1277164 | 0.2513500 |
| 105 | 0.0024568 | 0.0539341 | 0.1194000 | 0.2360043 |
| 104 | 0.0029792 | 0.0539341 | 0.1097633 | 0.2513500 |
| 102 | 0.0036483 | 0.1057878 | 0.1044025 | 0.3167116 |
NGFS Carbon Price Test Data
This dataset provides carbon pricing data used in the stress test scenarios.
Data Description
The ngfs_carbon_price_testdata dataset includes the
following columns:
-
year: Year of the carbon price. -
model: Model used to generate the carbon price. -
scenario: Scenario name. -
scenario_geography: Geographic region for the scenario. -
variable: The variable measured (e.g., carbon price). -
unit: Unit of the variable. -
carbon_tax: The amount of carbon tax applied in the scenario.
Data Structure
str(ngfs_carbon_price_testdata)
#> 'data.frame': 1376 obs. of 7 variables:
#> $ year : int 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 ...
#> $ model : chr "GCAM 5.3+ NGFS" "GCAM 5.3+ NGFS" "GCAM 5.3+ NGFS" "GCAM 5.3+ NGFS" ...
#> $ scenario : chr "B2DS" "B2DS" "B2DS" "B2DS" ...
#> $ scenario_geography: chr "Global" "Global" "Global" "Global" ...
#> $ variable : chr "Price|Carbon" "Price|Carbon" "Price|Carbon" "Price|Carbon" ...
#> $ unit : chr "US$2010/t CO2" "US$2010/t CO2" "US$2010/t CO2" "US$2010/t CO2" ...
#> $ carbon_tax : num 0 0 0 0 0 0 0 0 0 0 ...Sample Data
knitr::kable(head(ngfs_carbon_price_testdata)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
kableExtra::scroll_box(width = "100%", height = "400px")| year | model | scenario | scenario_geography | variable | unit | carbon_tax |
|---|---|---|---|---|---|---|
| 2015 | GCAM 5.3+ NGFS | B2DS | Global | Price|Carbon | US$2010/t CO2 | 0 |
| 2016 | GCAM 5.3+ NGFS | B2DS | Global | Price|Carbon | US$2010/t CO2 | 0 |
| 2017 | GCAM 5.3+ NGFS | B2DS | Global | Price|Carbon | US$2010/t CO2 | 0 |
| 2018 | GCAM 5.3+ NGFS | B2DS | Global | Price|Carbon | US$2010/t CO2 | 0 |
| 2019 | GCAM 5.3+ NGFS | B2DS | Global | Price|Carbon | US$2010/t CO2 | 0 |
| 2020 | GCAM 5.3+ NGFS | B2DS | Global | Price|Carbon | US$2010/t CO2 | 0 |
Scenarios Test Data
This dataset contains scenario-specific data including price paths, capacity factors, and other relevant information.
Data Description
The scenarios_testdata dataset includes the following
columns:
-
scenario_geography: Region relevant to the scenario. -
scenario: Scenario name. -
scenario_pathway: Specific pathway for the scenario. -
scenario_type: Type of scenario (e.g., baseline, shock). -
sector: Sector of production. -
technology: Type of technology. -
scenario_year: Year of the scenario data. -
scenario_price: Price in the scenario. -
price_unit: Unit for the price. -
pathway_unit: Unit of the pathway. -
technology_type: Type of technology involved. -
capacity_factor_unit: Unit for the capacity factor. -
price_indicator: Indicator for the price path.
Data Structure
str(scenarios_testdata)
#> 'data.frame': 1422 obs. of 14 variables:
#> $ scenario : chr "NGFS2023GCAM_CP" "NGFS2023GCAM_CP" "NGFS2023GCAM_CP" "NGFS2023GCAM_CP" ...
#> $ scenario_type : chr "baseline" "baseline" "baseline" "baseline" ...
#> $ scenario_geography : chr "Global" "Global" "Global" "Global" ...
#> $ sector : chr "Coal" "Coal" "Coal" "Coal" ...
#> $ technology : chr "Coal" "Coal" "Coal" "Coal" ...
#> $ scenario_year : int 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 ...
#> $ price_unit : chr "$/tonnes" "$/tonnes" "$/tonnes" "$/tonnes" ...
#> $ scenario_price : num 57 57.4 57.7 58 58.4 ...
#> $ pathway_unit : chr "EJ/yr" "EJ/yr" "EJ/yr" "EJ/yr" ...
#> $ scenario_pathway : num 159 160 161 162 162 ...
#> $ technology_type : chr "carbontech" "carbontech" "carbontech" "carbontech" ...
#> $ scenario_capacity_factor: num 1 1 1 1 1 1 1 1 1 1 ...
#> $ country_iso2_list : logi NA NA NA NA NA NA ...
#> $ scenario_provider : chr "NGFS2023GCAM" "NGFS2023GCAM" "NGFS2023GCAM" "NGFS2023GCAM" ...Sample Data
knitr::kable(head(scenarios_testdata, 50)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
kableExtra::scroll_box(width = "200%", height = "400px")| scenario | scenario_type | scenario_geography | sector | technology | scenario_year | price_unit | scenario_price | pathway_unit | scenario_pathway | technology_type | scenario_capacity_factor | country_iso2_list | scenario_provider |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2022 | $/tonnes | 57.03917 | EJ/yr | 159.4468 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2023 | $/tonnes | 57.35451 | EJ/yr | 160.4324 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2024 | $/tonnes | 57.66985 | EJ/yr | 161.4180 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2025 | $/tonnes | 57.98520 | EJ/yr | 162.4035 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2026 | $/tonnes | 58.41776 | EJ/yr | 162.4545 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2027 | $/tonnes | 58.85032 | EJ/yr | 162.5055 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2028 | $/tonnes | 59.28289 | EJ/yr | 162.5565 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2029 | $/tonnes | 59.71545 | EJ/yr | 162.6075 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2030 | $/tonnes | 60.14802 | EJ/yr | 162.6585 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2031 | $/tonnes | 60.53991 | EJ/yr | 163.5647 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2032 | $/tonnes | 60.93181 | EJ/yr | 164.4709 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2033 | $/tonnes | 61.32370 | EJ/yr | 165.3771 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2034 | $/tonnes | 61.71560 | EJ/yr | 166.2833 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2035 | $/tonnes | 62.10749 | EJ/yr | 167.1895 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2036 | $/tonnes | 62.28684 | EJ/yr | 167.6793 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2037 | $/tonnes | 62.46619 | EJ/yr | 168.1691 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2038 | $/tonnes | 62.64553 | EJ/yr | 168.6589 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2039 | $/tonnes | 62.82488 | EJ/yr | 169.1487 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2040 | $/tonnes | 63.00422 | EJ/yr | 169.6385 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2041 | $/tonnes | 63.11835 | EJ/yr | 169.6492 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2042 | $/tonnes | 63.23248 | EJ/yr | 169.6599 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2043 | $/tonnes | 63.34661 | EJ/yr | 169.6706 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2044 | $/tonnes | 63.46074 | EJ/yr | 169.6813 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2045 | $/tonnes | 63.57487 | EJ/yr | 169.6920 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2046 | $/tonnes | 63.60577 | EJ/yr | 169.3689 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2047 | $/tonnes | 63.63667 | EJ/yr | 169.0457 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2048 | $/tonnes | 63.66757 | EJ/yr | 168.7226 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2049 | $/tonnes | 63.69847 | EJ/yr | 168.3994 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2050 | $/tonnes | 63.72937 | EJ/yr | 168.0763 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2051 | $/tonnes | 63.73330 | EJ/yr | 167.9941 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2052 | $/tonnes | 63.73723 | EJ/yr | 167.9119 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2053 | $/tonnes | 63.74115 | EJ/yr | 167.8298 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2054 | $/tonnes | 63.74508 | EJ/yr | 167.7476 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2055 | $/tonnes | 63.74900 | EJ/yr | 167.6655 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2056 | $/tonnes | 63.66008 | EJ/yr | 167.3440 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2057 | $/tonnes | 63.57115 | EJ/yr | 167.0226 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2058 | $/tonnes | 63.48223 | EJ/yr | 166.7011 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2059 | $/tonnes | 63.39331 | EJ/yr | 166.3796 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2060 | $/tonnes | 63.30438 | EJ/yr | 166.0582 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2061 | $/tonnes | 63.18854 | EJ/yr | 165.4385 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2062 | $/tonnes | 63.07270 | EJ/yr | 164.8189 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2063 | $/tonnes | 62.95686 | EJ/yr | 164.1992 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2064 | $/tonnes | 62.84101 | EJ/yr | 163.5796 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2065 | $/tonnes | 62.72517 | EJ/yr | 162.9599 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2066 | $/tonnes | 62.58408 | EJ/yr | 161.8002 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2067 | $/tonnes | 62.44300 | EJ/yr | 160.6404 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2068 | $/tonnes | 62.30191 | EJ/yr | 159.4807 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2069 | $/tonnes | 62.16082 | EJ/yr | 158.3210 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2070 | $/tonnes | 62.01973 | EJ/yr | 157.1612 | carbontech | 1 | NA | NGFS2023GCAM |
| NGFS2023GCAM_CP | baseline | Global | Coal | Coal | 2071 | $/tonnes | 61.87448 | EJ/yr | 156.1628 | carbontech | 1 | NA | NGFS2023GCAM |
Data Preparation
Before running the model, ensure that your datasets are correctly formatted and contain the necessary information. Use the provided test datasets as templates for your own data.
For the scenarios, make sure that the baseline_scenario
and target_scenario you plan to use are present in your
scenarios dataframe, and that they correspond to the
scenario_geography you are analyzing.