vignette_parameters.Rmd

---
title: "Parameters"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Parameters}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
resource_files:
  - vignetteFigs
---

<!-------------------------->
<!-------------------------->
# Available Paramaters
<!-------------------------->
<!-------------------------->
<p align="center"> <img src="vignetteFigs/divider.png" width="1400"></p>

Users can get a list of all available params and queries as shown below. The parameters page lists details on each parameter, how it was calculated and cites any sources when available. `gcamextractor::map_param_query` gives a list of all parameters and the corresponding queries used to extract that data from GCAM. Additionally, `gcamextractor::map_param_query` also groups the various parameters into higher level groups which can then be called in the `paramSelect` argument of `gcamextractor` to extractor all the params in those groups. The groups also include curated parameters for particular models such as `CERF` and `GO`.

```{r, eval=F}
library(gcamextractor)

gcamextractor::params # Get all params
gcamextractor::queries # Get all queries used
gcamextractor::map_param_query # Get a table of params and the relevants queries used to extract and calculate them.

```

<!-- Add params list figure -->
<p align="center"> <img src="vignetteFigs/ParamList.png" width="1200"></p>

<!-------------------------->
## Socioeconomics
<!-------------------------->

<a name="table1"></a>
**Table 1:** Summary of parameters for socioeconomics.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("socio",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```

<br />

<!-- ### gdpPerCapita -->

<!-- <br /> -->

<!-- ### gdp -->

<!-- <br /> -->

<!-- ### gdpGrowthRate -->

<!-- <br /> -->

<!-- ### pop -->

<!-- <br /> -->


<!-------------------------->
## Transport
<!-------------------------->
<a name="table2"></a>
**Table 2:** Summary of parameters for transport.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("transport",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```

<sub>\*Passenger VMT modes: 2W and 3W, bus, car, large car and truck, LDV, plane, and rail.</sub> \
<sub>\*Freight VMT modes: heavy truck, light truck, medium truck, rail, and ship.</sub> \
<sub>\*Passenger VMT fuel: biofuel, electricity, fossil fuel, gas, hydrogen, and LA-electricity.</sub> \
<sub>\*Freight VMT fuel: biofuel, coal, electricity, fossil fuel, gas.</sub>

<br />

<!-- ### transportPassengerVMTByMode -->

<!-- <br /> -->

<!-- ### transportFreightVMTByMode -->

<!-- <br /> -->

<!-- ### transportPassengerVMTByFuel -->

<!-- <br /> -->

<!-- ### transportFreightVMTByFuel -->

<!-- <br /> -->

<!-------------------------->
## Water
<!-------------------------->

<a name="table3"></a>
**Table 3:** Summary of parameters for water.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("water",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```

<sub>\*Water consumption sectors: animal, domestic, electric, industry, irrigation, and primary.</sub> \
<sub>\*Water withdrawal sectors: agriculture, electricity, industry, livestock, mining, municipal, and desalination.</sub> \
<sub>\*Crop: biomass, corn, fibercrop, fodderherb, miscellaneous crop, oil crop, other grain, palm fruit, rice, root tuber, sugar crop, wheat, and fodder grass.</sub>


<br />

<!-- ### watConsumBySec -->

<!-- <br /> -->

<!-- ### watWithdrawBySec -->

<!-- <br /> -->

<!-- ### watWithdrawByCrop -->

<!-- <br /> -->


<!-- ### watBioPhysCons -->

<!-- <br /> -->

<!-- ### watIrrWithdrawBasin -->

<!-- <br /> -->

<!-- ### watIrrConsBasin -->

<!-- <br /> -->

<!-- ### watSupRunoffBasin -->

<!-- <br /> -->

<!-- ### waterWithdrawROGW -->

<!-- <br /> -->

<!-------------------------->
## Agriculture
<!-------------------------->

<a name="table4"></a>
**Table 4:** Summary of parameters for agriculture.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("ag",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```
<br />

<!-- ### agProdbyIrrRfd -->

<!-- <br /> -->

<!-- ### agProdBiomass -->

<!-- <br /> -->

<!-- ### agProdForest -->

<!-- <br /> -->

<!-- ### agProdByCrop -->

<!-- <br /> -->

<!-------------------------->
## Livestock
<!-------------------------->

<a name="table5"></a>
**Table 5:** Summary of parameters for livestock.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("livestock",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```

<br />

<!-- ### livestock_MeatDairybyTechMixed -->

<!-- <br /> -->

<!-- ### livestock_MeatDairybyTechPastoral -->

<!-- <br /> -->

<!-- ### livestock_MeatDairybyTechImports -->

<!-- <br /> -->

<!-- ### livestock_MeatDairybySubsector -->

<!-- <br /> -->

<!-------------------------->
## Land Use
<!-------------------------->

<a name="table6"></a>
**Table 6:** Summary of  parameters for land use.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("land",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```

<sub>\*Aggregated land type: crops, forest, natural other, natural other grass, natural other tree, pasture, and crop</sub> \
<sub>\*Detailed land type: biomassGrass, biomassTree, Corn, FiberCrop, Forest, Grassland, MiscCrop, OilCrop, OtherArableLand, OtherGrain, PalmFruit, Pasture, ProtectedGrassland, ProtectedShrubland, ProtectedUnmanagedForest, ProtectedUnmanagedPasture, Rice, RootTuber, Shrubland, SugarCrop, UnmanagedForest, UnmanagedPasture, UrbanLand, Wheat, FodderGrass, FodderHerb, RockIceDesert, and Tundra</sub>

<br />

<!-- ### landIrrRfd -->

<!-- <br /> -->

<!-- ### landIrrCrop -->

<!-- <br /> -->

<!-- ### landRfdCrop -->

<!-- <br /> -->

<!-- ### landAlloc -->

<!-- <br /> -->

<!-- ### landAllocByCrop -->

<!-- <br /> -->

<!-- ### landAllocDetail -->

<!-- <br /> -->

<!-------------------------->
## Emissions
<!-------------------------->

<a name="table7"></a>
**Table 7:** Summary of parameters for emissions.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("emiss",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```



<br />

<!-- ### emissNonCO2BySector -->

<!-- <br /> -->

<!-- ### emissNonCO2BySectorGWPAR5 -->

<!-- <br /> -->

<!-- ### emissNonCO2BySectorGTPAR5 -->

<!-- <br /> -->

<!-- ### emissNonCO2BySectorOrigUnits -->

<!-- <br /> -->

<!-- ### emissNonCO2ByResProdGWPAR5 -->

<!-- <br /> -->

<!-- ### emissNonCO2ByResProdGTPAR5 -->

<!-- <br /> -->

<!-- ### emissCO2BySector -->

<!-- <br /> -->

<!-- ### emissCO2BySectorNoBio -->

<!-- <br /> -->

<!-- ### emissBySectorGTPAR5FFI -->

<!-- <br /> -->

<!-- ### emissBySectorGTPAR5LUC -->

<!-- <br /> -->

<!-- ### emissMethaneBySourceGWPAR5 -->

<!-- <br /> -->

<!-- ### emissMethaneBySourceGTPAR5 -->

<!-- <br /> -->



<!-------------------------->
## Electricity
<!-------------------------->
<a name="table8"></a>
**Table 8:** Summary of parameters for electricity.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr); library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("elec",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```

<sub>\*Fuel: biomass, coal, gas, geothermal, hydro, nuclear, refined liquids, solar, and wind.</sub> \
<sub>\*Final electricity sectors: building, industry, and transport.</sub>

<br />


### elecByTechTWh

`elecByTechTWh` uses "elec gen by gen tech and cooling tech" query and converts electricity generation of GCAM raw output from EJ to TWh:

$$elecByTechTWh = elecByTechEJ_{GCAM} \times convEJ2TWh$$
where `convEJ2TWh` is the conversion constant from EJ to TWh, 1 EJ = 277.7778 TWh.
`gcamextractor::readgcam` also aggregates "rooftop_pv" into "solar" category in the technology subsector (shown in "class1" colomn).

```{r eval=F}
library(gcamextractor)

dataGCAM <- gcamextractor::readgcam(dataProjFile = gcamextractor::example_GCAMv52_2050_proj,
                                    paramsSelect = 'elecByTechTWh',
                                    regionsSelect = 'USA',
                                    folder = "my_output_folder")
```


<br />


### elecCapByFuel

`elecCapByFuel` calculates electricity capacity (GW) based on the output from `elecByTechTWh`:

$$elecCapByFuel = \frac{elecByTechTWh \times 1000}{8760 \times gcamCapacityFactor}$$

`gcamCapacityFactor` is calculated as an average of the capacity factors of the technologies within each subsector (See [Table GCAM Capacity Factors](#table_gcamCapacityFactor)) based on the data from:

  - Global technology capacity factor from "GCAM-FOLDER\\input\\gcamdata\\inst\\extdata\\energy\\A23.globaltech_capacity_factor.csv"
  - USA renewable technology capacity factors by States from "GCAM-FOLDER\\input\\gcamdata\\inst\\extdata\\gcam-usa\\NREL_us_re_capacity_factors.csv"
  - Hydrogen fuel cell capacity factor from https://www.eia.gov/todayinenergy/detail.php?id=35872 and https://www.nrel.gov/analysis/tech-cap-factor.html

Exceptions in calculating `gcamCapacityFactor` include:

  - For "solar" capacity factor the CSP_storage capacity factor value of 0.65 was excluded from the average.
  - The "hydro" capacity factor was sourced from Global CCS Institute "Renewable power generation costs in 2012: an overview" - 5.2 Capacity factors for hydropower, since hydropower is calculated endogenously within GCAM.
  - No cogeneration capacity factor "n CHP" was given.
  - rooftop_pv was assumed the same as solar average (5) grid_storage assumed to have a high cap factor equal to that of nuclear

<a name="table_gcamCapacityFactor"></a>
**Table GCAM Capacity Factors:** Averaged capacity factor for electricity generation by generation technology.

| Subsector (class1) | Average capacity factor from 1971 to 2100 |
|---|---|
| coal | 0.8125 |
| gas | 0.816667 |
| oil | 0.816667 |
| biomass | 0.825 |
| nuclear | 0.9 |
| geothermal | 0.9 |
| hydro | 0.5 |
| wind | 0.37 |
| solar | 0.216667 |
| rooftop_pv | 0.216667 |
| hydrogen | 0.8 |
| grid_storage | 0.9 |

```{r eval=F}
library(gcamextractor)

dataGCAM <- gcamextractor::readgcam(dataProjFile = gcamextractor::example_GCAMv52_2050_proj,
                                    paramsSelect = 'elecCapByFuel',
                                    regionsSelect = 'USA',
                                    folder = "my_output_folder")
```

<br />


<!-- ### elecFinalBySecTWh -->

<!-- <br /> -->

<!-- ### elecFinalByFuelTWh -->

<!-- <br /> -->


<!-------------------------->
## Energy
<!-------------------------->

<a name="table9"></a>
**Table 9:** Summary of parameters for energy.

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(kableExtra); library(dplyr);library(gcamextractor)
  
  table_raw <-   map_param_query%>%filter(grepl("energy",group)) 
  
  if(any(!grepl("^no$",unlist(table_raw$gcamdata%>%unique())))){
    table_kable <- table_raw %>% dplyr::select(param,query,gcamdata)} else {
    table_kable <- table_raw %>% dplyr::select(param,query)
  }
  
  table_kable %>%
  kbl() %>%
  kable_styling(bootstrap_options = c("bordered","striped", "hover", "condensed", "responsive")) %>%
  row_spec(0, background = "#2A2A2A", color = "white")
```

<sub>\*Final energy sectors: building, industry, transport, transport international aviation, and transport international ship.</sub> \
<sub>\*Building final energy subsectors: commercial cooling and heating, commercial others, residential cooling and heating, and residential others.</sub> \
<sub>\*Final energy fuel: biomass, coal, electricity, gas, hydrogen, liquids, liquids av, liquids shp, and other.</sub>

<br />

<!-- ### energyPrimaryByFuelEJ -->

<!-- <br /> -->

<!-- ### energyPrimaryRefLiqProdEJ -->

<!-- <br /> -->

<!-- ### energyFinalConsumBySecEJ -->

<!-- <br /> -->

<!-- ### energyFinalByFuelBySectorEJ -->

<!-- <br /> -->

<!-- ### energyFinalSubsecByFuelTranspEJ -->

<!-- <br /> -->

<!-- ### energyFinalSubsecByFuelBuildEJ -->

<!-- <br /> -->

<!-- ### energyFinalSubsecByFuelBuildEJ -->

<!-- <br /> -->

<!-- ### energyFinalSubsecByFuelIndusEJ -->

<!-- <br /> -->

<!-- ### energyFinalSubsecBySectorBuildEJ -->

<!-- <br /> -->

<!-- ### energyFinalConsumByIntlShpAvEJ -->

<!-- <br /> -->


<!-------------------------->
<!-------------------------->
# Data for Specific Models
<!-------------------------->
<!-------------------------->
<p align="center"> <img src="vignetteFigs/divider.png" width="1400"></p>

<!-------------------------->
## CERF
<!-------------------------->

Use `cerf` for the `paramSelect` argument in `gcamextractor` to extract the relevant variables needed for the `CERF` model (https://github.com/IMMM-SFA/cerf).

```{r eval=F}
library(gcamextractor)

path_to_gcam_database <- "E:/gcamfolder/output/database_ref" # Change to your path.
path_to_gcam_projfile <- "E:/my_proj_file.proj" # Change to your .proj file path.
path_to_gcamdata_folder <- "E:/gcamfolder/input/gcamdata"
gcamextractor::params # view available parameters
dataGCAM <- gcamextractor::readgcam(gcamdatabase = path_to_gcam_database,
                                    dataProjFile = path_to_gcam_projfile # Optional. Or cerf_proj_file.proj
                                    gcamdata_folder = path_to_gcamdata_folder,
                                    paramsSelect = "cerf",
                                    folder = "cerf")  # Set to "All" to read in all available params

df <- dataGCAM$data; df
dfParam <- dataGCAM$dataAggParam; dfParam
dfClass1 <- dataGCAM$dataAggClass1; dfClass1
dfClass2 <- dataGCAM$dataAggClass1; dfClass2

```

The parameters extracted for CERF are as follows with links to the relevant parameters explanation:

<a name="table_cerf"></a>
**Table: CERF**

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(pander)
mytable = data.frame(
    Parameter     = c("elec_lifetime_yr",
                      "elec_lifetime_scurve_yr",
                      "elec_capacity_factor_usa_in",
                      "elec_variable_om_2015USDperMWh",
                      "elec_variable_om_escl_rate_2015USDperMWh",
                      "elec_fuel_price_2015USDperMBTU",
                      "elec_fuel_price_escl_rate_2015USDperMBTU",
                      "elec_cap_usa_GW",
                      "energy_fuel_co2_content_tonsperMBTU",
                      "elec_carbon_capture_rate_fraction",
                      "elec_carbon_capture_escl_rate_fraction",
                      "elec_heat_rate_BTUperkWh"),
    Description = c("Technology Lifetime (Yr)",
                    "Base year S-Curve Lifetime, Steepness, half life",
                    "Capacity Factor assumptions for US States by investment segments (baseline, intermediate, subpeak, peak",
                    "Variable OnM cost (2015 USD/MWh)",
                    "Variable OnM cost escalation rate (fraction)",
                    "Fuel Price (2015 USD/MBTU)",
                    "Fuel Price Escalation Rate (fraction)",
                    "Installed Capacity in GW",
                    "Fuel CO2 Content (Tons per MBTU)",
                    "Carbon Capture Rate (fraction)",
                    "Carbon Capture Escalation Rate (fraction)",
                    "Heat Rate (BTU per kWh)"
                    ),
    Unit     = c("year",
                 "* lifetime (yr)\
                 \n* steepness (no units)\
                 \n* half life (yr)",
                 "None",
                 "2015 USD/MWh",
                 "2015 USD/MWh",
                 "2015 USD/MBTU",
                 "2015 USD/MBTU",
                 "GW",
                 "Tons per MBTU",
                 "Fraction",
                 "Fraction",
                 "BTU per kWh"),
    Queries = c("NA",
                "NA",
                "elec investment capacity factor",
                "elec operating costs by tech and vintage",
                "elec operating costs by tech and vintage",
                "prices by sector",
                "prices by sector",
                "elec capacity by tech and vintage",
                "NA",
                "NA",
                "NA",
                "elec coeff"),
    `GCAM version tested` = c("v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3",
                              "v5.3"),
    `gcamdata files` = c("* L223.TechLifetime_Dispatch\
                         \n* L2242.TechLifetime_hydro\
                         \n* L2233.GlobalTechLifetime_elec_cool\
                         \n* L2233.GlobalIntTechLifetime_elec_cool",
                         "* L2244.TechSCurve_nuc_gen2_USA\
                         \n* L223.TechSCurve_Dispatch\
                         \n* L2241.TechSCurve_coalret_vintage_dispatch_gcamusa\
                         \n* L2233.GlobalTechSCurve_elec_cool",
                         "None",
                         "None",
                         "None",
                         "None",
                         "None",
                         "None",
                         "* L2261.CarbonCoef_bio_USA\
                         \n* L202.CarbonCoef\
                         \n* L222.CarbonCoef_en_USA",
                         "* L223.TechCarbonCapture_Dispatch\
                         \n* L2233.GlobalTechCapture_elec_cool",
                         "* L223.TechCarbonCapture_Dispatch\
                         \n* L2233.GlobalTechCapture_elec_cool",
                         "None")
    )

pander::pander(mytable, keep.line.breaks = TRUE, style = 'grid', justify = 'left', 
               split.tables=Inf, split.cells=100)
```


<!-------------------------->
## GO
<!-------------------------->

Use `cerf` for the `paramSelect` argument in `gcamextractor` to extract the relevant variables needed for the `CERF` model (https://github.com/IMMM-SFA/cerf).

```{r eval=F}
library(gcamextractor)

path_to_gcam_database <- "E:/gcamfolder/output/database_ref" # Change to your path.
path_to_gcam_projfile <- "E:/my_proj_file.proj" # (OPTIONAL) Change to your .proj file path. 
gcamextractor::params # view available parameters
dataGCAM <- gcamextractor::readgcam(gcamdatabase = path_to_gcam_database,
                                    dataProjFile = path_to_gcam_projfile # Optional. Or go_proj_file.proj
                                    paramsSelect = "go",
                                    folder = "go")  # Set to "All" to read in all available params

df <- dataGCAM$data; df
dfParam <- dataGCAM$dataAggParam; dfParam
dfClass1 <- dataGCAM$dataAggClass1; dfClass1
dfClass2 <- dataGCAM$dataAggClass1; dfClass2

```

The parameters extracted for GO are as follows with links to the relevant parameters explanation:

<a name="table_cerf"></a>
**Table: GO**

```{r, results = 'show', eval=TRUE, echo=FALSE, warning=FALSE, error = FALSE, message = FALSE}
library(pander)
mytable = data.frame(
    Parameter     = c("elec_fuel_price_2015USDperMBTU",
                      "elec_heat_rate_MBTUperMWh"),
    Description = c("Fuel Price (2015 USD/MBTU)",
                    "Heat Rate (MBTU per MWh)"
                    ),
    Unit     = c("2015 USD/MBTU",
                 "MBTU per MWh"),
    Queries = c("prices by sector",
                "elec coeff"),
    `GCAM version tested` = c("v5.3",
                              "v5.3"),
    `gcamdata files` = c("None",
                         "None")
    )

pander::pander(mytable, keep.line.breaks = TRUE, style = 'grid', justify = 'left', 
               split.tables=Inf, split.cells=100)
```