Greenhouse gas emissions for Northern Ireland by source sector for the years 1990 to 2022. Further information available from
https://www.daera-ni.gov.uk/articles/northern-ireland-greenhouse-gas-inventory
Reader Information
These statistics are based on the NI bulletin of the greenhouse gas inventory. The greenhouse gas inventory data are produced annually by Ricardo Energy and Environment, on behalf of the Department for Business, Energy & Industrial Strategy, the Scottish Government, the Welsh Assembly Government and the Northern Ireland Department of Agriculture, Environment and Rural Affairs. Each year the greenhouse gas inventory is extended and updated. The entire historical data series, from base year to 2022, is revised to incorporate methodological improvements and new data. This takes into account revisions to the datasets used in its compilation.
Emissions are reported for seven greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). Depending on their molecular weights, radiative properties and residence times in the atmosphere, each greenhouse gas has a different capacity to cause global warming. The base year is 1990 for carbon dioxide, methane and nitrous oxide, and 1995 for the fluorinated gases. The fluorinated gases are hydrofluorocarbons, perfluorocarbons, sulphur hexafluoride and nitrogen trifluoride. Emissions are reported according to the National Communication sectors in accordance with international reporting guidelines from the United Nations Framework Convention on Climate Change (UNFCCC).
The greenhouse gas emission estimates are based on a wide range of data sources and sources of uncertainty include statistical differences, assumptions, proxy datasets and expert judgement. In addition, the natural variability in the processes that are being modelled introduce uncertainty. For example, carbon content of fuels and farming practices under different climatic conditions and soil types. The uncertainties presented are a close approximation of the 95% confidence interval. The width of the interval provides a measure of the accuracy of the estimates. Uncertainty estimates for Northern Ireland emissions are available for the base year, the latest year (2022) and for the percentage change between the two years. For the base year, uncertainty is ±8%, and for 2022 it is ±6%. For the percentage reduction between the base year and 2022, the uncertainty interval ranges from 20% to 37%. There remains greater uncertainty around emissions in Northern Ireland compared to other parts of the United Kingdom due to the relative importance of methane and nitrous oxide emissions in the agriculture sector. Emissions of this gas are more difficult to estimate than carbon dioxide, and the agriculture sector makes up a larger share of Northern Ireland’s emissions than in other parts of the UK.
The UK greenhouse gas inventory National Statistics user guide provides a simple guide to the origins and use of data in the compilation of the UK Greenhouse Gas Inventory. This guide can be accessed through the Department for Business, Energy & Industrial Strategy website https://www.gov.uk/government/publications/uk-greenhouse-gas-emissions-statistics-user-guidance.
Contact Details
DAERA Statistics & Analytical Services Branch is keen to hear your feedback.
Please e-mail comments to env.stats@daera-ni.gov.uk
Further information can be found on the DAERA Statistics website https://www.daera-ni.gov.uk/landing-pages/statistics
```
---
title: "Northern Ireland greenhouse gas inventory 1990 - 2022"
output:
flexdashboard::flex_dashboard:
orientation: rows
theme: cerulean
source_code: embed
navbar:
- { title: "Cookies", href: "https://www.nisra.gov.uk/cookies", align: right }
---
<!--html_preserve-->
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```{r setup, message=F}
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
#LOAD PACKAGES
library(flexdashboard)
library(knitr)
library(crosstalk)
library(DT)
library(tidyverse)
library(plotly)
library(stringr)
library(reshape2)
# READ IN THE DATA
GHG <- read.csv("ghg emissions 2022.csv", header = T, stringsAsFactors = F)
# TIDY THE DATA UP
GHG<-GHG %>% mutate(Total=ifelse(Year%in%c("1991","1992","1993","1994"), NA_character_, Total))
GHG$Year[GHG$Year == 'BaseYear'] <- 1989
kpis <- names(GHG)[c(3)]
GHGall <- melt(GHG, id=1:2)
GHGall$Sector[GHGall$Sector == "Agriculture"] <- "Agriculture"
GHGall$Sector[GHGall$Sector == "Buildings and product uses"] <- "Buildings and product uses"
GHGall$Sector[GHGall$Sector == "Domestic transport"] <- "Domestic transport"
GHGall$Sector[GHGall$Sector == "Electricity supply"] <- "Electricity supply"
GHGall$Sector[GHGall$Sector == "Fuel supply"] <- "Fuel supply"
GHGall$Sector[GHGall$Sector == "Industry"] <- "Industry"
GHGall$Sector[GHGall$Sector == "LULUCF"] <- "Land Use Change"
GHGall$Sector[GHGall$Sector == "Waste"] <- "Waste"
GHGall$Sector[GHGall$Sector == "Northern Ireland"] <- "Northern Ireland"
GHGall$Sector[GHGall$Sector == "Agricultural combustion"] <- "Agricultural combustion"
GHGall$Sector[GHGall$Sector == "Agricultural soils"] <- "Agricultural soils"
GHGall$Sector[GHGall$Sector == "Livestock"] <- "Livestock"
GHGall$Sector[GHGall$Sector == "Other agriculture"] <- "Other Agriculture"
GHGall$Sector[GHGall$Sector == "Commercial buildings"] <- "Commercial buildings"
GHGall$Sector[GHGall$Sector == "Other buildings and product uses"] <- "Other buildings and product uses"
GHGall$Sector[GHGall$Sector == "Public sector buildings"] <- "Public sector buildings"
GHGall$Sector[GHGall$Sector == "Residential buildings"] <- "Residential buildings"
GHGall$Sector[GHGall$Sector == "Civil aviation"] <- "Civil aviation"
GHGall$Sector[GHGall$Sector == "Other domestic transport"] <- "Other domestic transport"
GHGall$Sector[GHGall$Sector == "Railways"] <- "Railways"
GHGall$Sector[GHGall$Sector == "Road"] <- "Road"
GHGall$Sector[GHGall$Sector == "Waterborne"] <- "Waterborne"
GHGall$Sector[GHGall$Sector == "Power stations - coal"] <- "Coal"
GHGall$Sector[GHGall$Sector == "Power stations - gas"] <- "Gas"
GHGall$Sector[GHGall$Sector == "Power stations - municipal solid waste"] <- "Municipal solid waste"
GHGall$Sector[GHGall$Sector == "Power stations - other fuel"] <- "Other fuel"
GHGall$Sector[GHGall$Sector == "Power stations - petroleum"] <- "Petroleum"
GHGall$Sector[GHGall$Sector == "Oil and gas supply"] <- "Oil and gas supply"
GHGall$Sector[GHGall$Sector == "Solid fuel supply"] <- "Solid fuel supply"
GHGall$Sector[GHGall$Sector == "Industrial fuel combustion"] <- "Industrial fuel combustion"
GHGall$Sector[GHGall$Sector == "Industrial processes"] <- "Industrial processes"
GHGall$Sector[GHGall$Sector == "Other industry"] <- "Other industry"
GHGall$Sector[GHGall$Sector == "Forestry"] <- "Forestry"
GHGall$Sector[GHGall$Sector == "Grassland mineral soils under LUC"] <- "Grassland mineral soils under LUC"
GHGall$Sector[GHGall$Sector == "Other LULUCF"] <- "Other LULUCF"
GHGall$Sector[GHGall$Sector == "Peatland"] <- "Peatland"
GHGall$Sector[GHGall$Sector == "Settlement"] <- "Settlement"
GHGall$Sector[GHGall$Sector == "Accidental fires"] <- "Accidental fires"
GHGall$Sector[GHGall$Sector == "Anaerobic digestion"] <- "Anaerobic digestion"
GHGall$Sector[GHGall$Sector == "Composting"] <- "Composting"
GHGall$Sector[GHGall$Sector == "Incineration without energy recovery"] <- "Incineration without energy recovery"
GHGall$Sector[GHGall$Sector == "Landfill"] <- "Landfill"
GHGall$Sector[GHGall$Sector == "Wastewater"] <- "Wastewater"
# format variables
GHGall$Sector <- factor(GHGall$Sector)
GHGall$variable <- factor(GHGall$variable)
GHGall$Year <- factor(GHGall$Year)
GHGall$value <- as.numeric(GHGall$value)
# CREATE FINAL DATAFRAME FOR SHARING
dat <-
GHGall %>%
arrange(desc(Year))
# CREATE A COPY OF dat FOR GRAPHING THAT DOESN'T USE big.mark ","
dat_graph <- dat
dat$value <- sapply(dat$value, format, big.mark = ",")
# CROSSTALK
GHGkpis <-
GHGall %>%
filter(variable %in% kpis) %>%
select(Year, Sector, variable, value)
shared_dat <- SharedData$new(GHGkpis)
sd_df <- SharedData$new(GHGkpis, group = shared_dat$groupName())
# CREATE DATAFRAMES TO USE WITH PLOTLY
tidy_dat <-
dat_graph
###############################################
################# Total ####################
###############################################
Total <-
GHGall %>%
filter(variable == "Total") %>%
select(Year, Sector, value) %>% spread(Sector, value)
pre1997 <-
Total[as.numeric(as.character(Total$Year)) < 1997, ]
post1997 <-
Total[as.numeric(as.character(Total$Year)) > 1997, ]
ticklabs <- list()
for(i in 1:length(Total$Year)){
ticklabs[[i]] <- 1988+i
}
ticklabs[[1]] <- "Base year"
ticklabs[[3]] <- " "
ticklabs[[4]] <- " "
ticklabs[[5]] <- " "
ticklabs[[6]] <- " "
ticklabs[[8]] <- " "
ticklabs[[9]] <- " "
ticklabs[[31]]<-"2019"
ticklabs[[32]]<-"2020"
ticklabs[[33]]<-"2021"
ticklabs[[34]]<-"2022"
tickval <- list()
for(i in 1:length(Total$Year)){
tickval[[i]] <- 1988+i
}
tickval[[31]]<-2019
tickval[[32]]<-2020
tickval[[33]]<-2021
tickval[[34]]<-2022
pre1997<-pre1997 %>% mutate_if(is.numeric, round, digits=1)
post1997<-post1997%>% mutate_if(is.numeric, round, digits=1)
```
```{r logo, out.width = "175px", echo=FALSE, fig.align='centre'}
```
Charts {data-icon="fa-chart-line"}
===========================================================
**Greenhouse gas emissions for Northern Ireland by source sector for the years 1990 to 2022. Further information available from **
**https://www.daera-ni.gov.uk/articles/northern-ireland-greenhouse-gas-inventory**
Row {.tabset .tabset-fade}
-------------------------------------
### **Northern Ireland**
```{r Northern Ireland greenhouse gas inventory 1990 - 2022}
plot_ly(pre1997, x = ~Year, y = ~Agriculture, legendgroup = "agri", name = 'Agriculture', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Buildings and product uses`, name = 'Buildings and product uses', legendgroup = "build", mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Domestic transport`, name = 'Domestic transport', legendgroup = "tran", mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Electricity supply`, name = 'Electricity supply', legendgroup = "elec", visible = T, showlegend = F, color = I("firebrick")) %>%
add_trace(y = ~pre1997$`Fuel supply`, name = 'Fuel supply', legendgroup = "fuel", visible = T, showlegend = F, color = I("purple")) %>%
add_trace(y = ~pre1997$`Industry`, name = 'Industry', legendgroup = "ind", visible = T, showlegend = F, color = I("blue4")) %>%
add_trace(y = ~pre1997$'Land Use Change', name = 'Land Use Change', legendgroup = "land", visible = T, showlegend = F, color = I("darkgreen")) %>%
add_trace(y = ~pre1997$`Waste`, name = 'Waste', legendgroup = "waste", visible = T, showlegend = F, color = I("darkcyan")) %>%
add_trace(y = ~pre1997$`Northern Ireland`, name = 'Northern Ireland', legendgroup = "NI", visible = 'legendonly', showlegend = F, color = I("darkgrey")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$Agriculture, legendgroup = "agri", name = 'Agriculture', mode = 'lines', visible = T, showlegend = T, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Buildings and product uses`, name = 'Buildings and product uses', legendgroup = "build", mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Domestic transport`, name = 'Domestic transport', legendgroup = "tran", mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Electricity supply`, name = 'Electricity supply', legendgroup = "elec", mode= 'lines', visible = T, showlegend = T, color = I("firebrick")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Fuel supply`, name = 'Fuel supply', legendgroup = "fuel", mode= 'lines',visible = T, showlegend = T, color = I("purple")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Industry`, name = 'Industry', legendgroup = "ind", mode= 'lines',visible = T, showlegend = T, color = I("blue4")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$'Land Use Change', name = 'Land Use Change', legendgroup = "land", mode= 'lines', visible = T, showlegend = T, color = I("darkgreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Waste`, name = 'Waste', legendgroup = "waste", mode= 'lines', visible = T, showlegend = T, color = I("darkcyan")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Northern Ireland`, name = 'Northern Ireland', legendgroup = "NI", mode= 'lines', visible = 'legendonly', showlegend = T, color = I("darkgrey")) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare", hoverinfo="text",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
tickvals = tickval,
ticktext = ticklabs,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kilotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
range = c(0, 8,000),
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
hoverformat='.0f',
separator = ".,",
zeroline = FALSE))
```
### **Agriculture**
```{r Agri}
plot_ly(pre1997, x = ~Year, y = ~`Agricultural combustion`, legendgroup = "combustion", name = 'Agricultural combustion', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Agricultural soils`, legendgroup = "soils", name = 'Agricultural soils', mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Livestock`, legendgroup = "livestock", name = 'Livestock', mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Other agriculture`, legendgroup = "other", name = 'Other agriculture', mode = 'markers', visible = T, showlegend = F, color = I("firebrick")) %>%
add_trace(y = ~pre1997$`Agriculture`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace (x = ~post1997$Year, y = ~post1997$`Agricultural combustion`, legendgroup = "combustion", name = 'Agricultural combustion', mode = 'lines',
visible = T, showlegend = T, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Agricultural soils`, legendgroup = "soils", name = 'Agricultural soils', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Livestock`, legendgroup = "livestock", name = 'Livestock', mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Other agriculture`, legendgroup = "other", name = 'Other Agriculture', mode = 'lines', visible = T, showlegend = T, color = I("firebrick")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Agriculture`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
rangemode = "tozero",
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
### **Buildings and Product Uses**
```{r Build}
plot_ly(pre1997, x = ~Year, y = ~`Commercial buildings`, legendgroup = "comm", name = 'Commercial buildings', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Other buildings and product uses`, legendgroup = "other", name = 'Other buildings and product uses', mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Public sector buildings`, legendgroup = "public", name = 'Public sector buildings', mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Residential buildings`, legendgroup = "resi", name = 'Residential buildings', mode = 'markers', visible = T, showlegend = F, color = I("firebrick")) %>%
add_trace(y = ~pre1997$`Buildings and product uses`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace (x = ~post1997$Year, y = ~post1997$`Commercial buildings`, legendgroup = "comm", name = 'Commercial buildings', mode = 'lines',visible = T, showlegend = T, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Other buildings and product uses`, legendgroup = "other", name = 'Other buildings and product uses', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Public sector buildings`, legendgroup = "public", name = 'Public sector buildings', mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Residential buildings`, legendgroup = "resi", name = 'Residential buildings', mode = 'lines', visible = T, showlegend = T, color = I("firebrick")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Buildings and product uses`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
rangemode = "tozero",
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
### **Domestic Transport**
```{r transport}
plot_ly(pre1997, x = ~Year, y = ~`Civil aviation`, legendgroup = "air", name = 'Civil aviation', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Other domestic transport`, legendgroup = "other", name = 'Other domestic transport', mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Railways`, legendgroup = "rail", name = 'Railways', mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Road`, legendgroup = "road", name = 'Road', mode = 'markers', visible = T, showlegend = F, color = I("firebrick")) %>%
add_trace(y = ~pre1997$`Waterborne`, legendgroup = "water", name = 'Waterborne', mode = 'markers', visible = T, showlegend = F, color = I("purple")) %>%
add_trace(y = ~pre1997$`Domestic transport`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Civil aviation`, legendgroup = "air", name = 'Civil aviation', mode = 'lines', visible = T, showlegend = TRUE, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Other domestic transport`, legendgroup = "other", name = 'Other domestic transport', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Railways`, legendgroup = "rail", name = 'Railways', mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Road`, legendgroup = "road", name = 'Road', mode = 'lines', visible = T, showlegend = T, color = I("firebrick")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Waterborne`, legendgroup = "water", name = 'Waterborne', mode = 'lines', visible = T, showlegend = T, color = I("purple")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Domestic transport`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
autorange = TRUE,
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
### **Electricity supply**
```{r electric}
plot_ly(pre1997, x = ~Year, y = ~`Coal`, legendgroup = "coal", name = 'Coal', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Gas`, legendgroup = "gas", name = 'Gas', mode = 'markers', visible = T, showlegend = F, color = I("orange"))%>%
add_trace(y = ~pre1997$`Municipal solid waste`, legendgroup = "waste", name = 'Municipal solid waste', mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Other fuel`, legendgroup = "other", name = 'Other fuel', mode = 'markers', visible = T, showlegend = F, color = I("purple")) %>%
add_trace(y = ~pre1997$`Petroleum`, legendgroup = "petrol", name = 'Petroleum', mode = 'markers', visible = T, showlegend = F, color = I("firebrick")) %>%
add_trace(y = ~pre1997$`Electricity supply`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Coal`, legendgroup = "coal", name = 'Coal', mode = 'lines', visible = T, showlegend = TRUE, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Gas`, legendgroup = "gas", name = 'Gas', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Municipal solid waste`, legendgroup = "waste", name = 'Municipal solid waste', mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Other fuel`, legendgroup = "other", name = 'Other fuel', mode = 'lines', visible = T, showlegend = T, color = I("purple")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Petroleum`, legendgroup = "petrol", name = 'Petroleum', mode = 'lines', visible = T, showlegend = T, color = I("firebrick")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Electricity supply`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
autorange = TRUE,
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
### **Fuel supply**
```{r fuel}
plot_ly(pre1997, x = ~Year, y = ~`Oil and gas supply`, legendgroup = "oilgas", name = 'Oil and gas supply', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Solid fuel supply`, legendgroup = "solid", name = 'Solid fuel supply', mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Fuel supply`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Oil and gas supply`, legendgroup = "oilgas", name = 'Oil and gas supply', mode = 'lines', visible = T, showlegend = TRUE, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Solid fuel supply`, legendgroup = "solid", name = 'Solid fuel supply', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Fuel supply`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
autorange = TRUE,
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
### **Industry**
```{r industry}
plot_ly(pre1997, x = ~Year, y = ~`Industrial fuel combustion`, legendgroup = "fuel", name = 'Industrial fuel combustion', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Industrial processes`, legendgroup = "process", name = 'Industrial processes', mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Other industry`, legendgroup = "other", name = 'Other industry', mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Industry`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Industrial fuel combustion`, legendgroup = "fuel", name = 'Industrial fuel combustion', mode = 'lines', visible = T, showlegend = TRUE, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Industrial processes`, legendgroup = "process", name = 'Industrial processes', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Other industry`, legendgroup = "other", name = 'Other industry', mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Industry`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
autorange = TRUE,
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
### **Land Use Change**
```{r LULUCF}
####LULUCF
plot_ly(pre1997, x = ~Year, y = ~`Forestry`, legendgroup = "forest", name = 'Forestry', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Grassland mineral soils under LUC`, legendgroup = "grass", name = 'Grassland mineral soils under LUC', mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Other LULUCF`, legendgroup = "other", name = 'Other LULUCF', mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Peatland`, legendgroup = "peat", name = 'Peatland', mode = 'markers', visible = T, showlegend = F, color = I("firebrick")) %>%
add_trace(y = ~pre1997$`Settlement`, legendgroup = "set", name = 'Settlement', mode = 'markers', visible = T, showlegend = F, color = I("purple")) %>%
add_trace(y = ~pre1997$`Land Use Change`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace (x = ~post1997$Year, y = ~post1997$`Forestry`, legendgroup = "forest", name = 'Forestry', mode = 'lines',
visible = T, showlegend = TRUE, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Grassland mineral soils under LUC`, legendgroup = "grass", name = 'Grassland mineral soils under LUC', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Other LULUCF`, legendgroup = "other", name = 'Other LULUCF', mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Peatland`, legendgroup = "peat", name = 'Peatland', mode = 'lines', visible = T, showlegend = T, color = I("firebrick")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Settlement`, legendgroup = "set", name = 'Settlement', mode = 'lines', visible = T, showlegend = T, color = I("purple")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Land Use Change`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
autorange = TRUE,
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
### **Waste**
```{r waste}
plot_ly(pre1997, x = ~Year, y = ~`Accidental fires`, legendgroup = "fire", name = 'Accidental fires', type = 'scatter', mode = 'markers', visible = T, showlegend = F, color = I("blue")) %>%
add_trace(y = ~pre1997$`Anaerobic digestion`, legendgroup = "anaer", name = 'Anaerobic digestion', mode = 'markers', visible = T, showlegend = F, color = I("orange")) %>%
add_trace(y = ~pre1997$`Composting`, legendgroup = "compost", name = 'Composting', mode = 'markers', visible = T, showlegend = F, color = I("limegreen")) %>%
add_trace(y = ~pre1997$`Incineration without energy recovery`, legendgroup = "incin", name = 'Incineration without energy recovery', mode = 'markers', visible = T, showlegend = F, color = I("firebrick")) %>%
add_trace(y = ~pre1997$`Landfill`, legendgroup = "dump", name = 'Landfill', mode = 'markers', visible = T, showlegend = F, color = I("purple")) %>%
add_trace(y = ~pre1997$`Wastewater`, legendgroup = "water", name = 'Wastewater', mode = 'markers', visible = T, showlegend = F, color = I("blue4")) %>%
add_trace(y = ~pre1997$`Waste`, legendgroup = "total", name = 'Total', visible = 'legendonly', showlegend = F, marker = list(color = 'rgb(0,0,0)')) %>%
add_trace (x = ~post1997$Year, y = ~post1997$`Accidental fires`, legendgroup = "fire", name = 'Accidental fires', mode = 'lines',
visible = T, showlegend = TRUE, color = I("blue")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Anaerobic digestion`, legendgroup = "anaer", name = 'Anaerobic digestion', mode = 'lines', visible = T, showlegend = T, color = I("orange")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Composting`, legendgroup = "compost", name = 'Composting', mode = 'lines', visible = T, showlegend = T, color = I("limegreen")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Incineration without energy recovery`, legendgroup = "incin", name = 'Incineration without energy recovery', mode = 'lines', visible = T, showlegend = T, color = I("firebrick")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Landfill`, legendgroup = "dump", name = 'Landfill', mode = 'lines', visible = T, showlegend = T, color = I("purple")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Wastewater`, legendgroup = "water", name = 'Wastewater', mode = 'lines', visible = T, showlegend = T, color = I("blue4")) %>%
add_trace(x = ~post1997$Year, y = ~post1997$`Waste`, legendgroup = "total", name = 'Total', mode = 'lines', visible = 'legendonly', showlegend = T, line = list(color = 'rgb(0,0,0)')) %>%
layout(title = "",
paper_bgcolor = 'rgb(255,255,255)', plot_bgcolor = 'rgb(255,255,255)', hovermode = "compare",
xaxis = list(title = "",
gridcolor = 'rgb(255,255,255)',
showgrid = FALSE,
range = c(1988, 2022),
ticktext = ticklabs,
tickvals = tickval,
showline = TRUE,
dtick = 1,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
zeroline = FALSE),
yaxis = list(title = "Kiltotonnes CO2e",
titlefont = list(size = 11),
gridcolor = 'rgb(229,229,299)',
autorange = TRUE,
showgrid = TRUE,
showline = TRUE,
showticklabels = TRUE,
tickcolor = 'rgb(127,127,127)',
ticks = 'outside',
tickformat = ",",
zeroline = FALSE))
```
Reader Information & Contact Details {data-icon="fa-chart-line"}
===========================================================
**Reader Information**
*These statistics are based on the NI bulletin of the greenhouse gas inventory. The greenhouse gas inventory data are produced annually by Ricardo Energy and Environment, on behalf of the Department for Business, Energy & Industrial Strategy, the Scottish Government, the Welsh Assembly Government and the Northern Ireland Department of Agriculture, Environment and Rural Affairs. Each year the greenhouse gas inventory is extended and updated. The entire historical data series, from base year to 2022, is revised to incorporate methodological improvements and new data. This takes into account revisions to the datasets used in its compilation. *
*Emissions are reported for seven greenhouse gases: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). Depending on their molecular weights, radiative properties and residence times in the atmosphere, each greenhouse gas has a different capacity to cause global warming. The base year is 1990 for carbon dioxide, methane and nitrous oxide, and 1995 for the fluorinated gases. The fluorinated gases are hydrofluorocarbons, perfluorocarbons, sulphur hexafluoride and nitrogen trifluoride. Emissions are reported according to the National Communication sectors in accordance with international reporting guidelines from the United Nations Framework Convention on Climate Change (UNFCCC).*
*The greenhouse gas emission estimates are based on a wide range of data sources and sources of uncertainty include statistical differences, assumptions, proxy datasets and expert judgement. In addition, the natural variability in the processes that are being modelled introduce uncertainty. For example, carbon content of fuels and farming practices under different climatic conditions and soil types. The uncertainties presented are a close approximation of the 95% confidence interval. The width of the interval provides a measure of the accuracy of the estimates. Uncertainty estimates for Northern Ireland emissions are available for the base year, the latest year (2022) and for the percentage change between the two years. For the base year, uncertainty is ±8%, and for 2022 it is ±6%. For the percentage reduction between the base year and 2022, the uncertainty interval ranges from 20% to 37%. There remains greater uncertainty around emissions in Northern Ireland compared to other parts of the United Kingdom due to the relative importance of methane and nitrous oxide emissions in the agriculture sector. Emissions of this gas are more difficult to estimate than carbon dioxide, and the agriculture sector makes up a larger share of Northern Ireland’s emissions than in other parts of the UK.*
*The UK greenhouse gas inventory National Statistics user guide provides a simple guide to the origins and use of data in the compilation of the UK Greenhouse Gas Inventory. This guide can be accessed through the Department for Business, Energy & Industrial Strategy website https://www.gov.uk/government/publications/uk-greenhouse-gas-emissions-statistics-user-guidance.*
**Contact Details**
*DAERA Statistics & Analytical Services Branch is keen to hear your feedback.*
*Please e-mail comments to env.stats@daera-ni.gov.uk*
*Further information can be found on the DAERA Statistics website https://www.daera-ni.gov.uk/landing-pages/statistics*
Row {.tabset .tabset-fade}
-------------------------------------
```