Where Are People More Likely To Treat Climate Change as the Most Serious Global Problem?
Joining Climate Awareness & Air Pollution Data On Individual Level
A better map after harmonization of geographical metadata.library(regions)
library(lubridate)
library(dplyr)
if ( dir.exists('data-raw') ) {
data_raw_dir <- "data-raw"
} else {
data_raw_dir <- file.path("..", "..", "data-raw")
}
The first results of our longitudinal table were difficult to map, because the surveys used an obsolete regional coding. We will adjust the wrong coding, when possible, and join the data with the European Environment Agency’s (EEA) Air Quality e-Reporting (AQ e-Reporting) data on environmental pollution. We recoded the annual level for every available reporting stations [not shown here] and all values are in μg/m3. The period under observation is 2014-2016. Data file: https://www.eea.europa.eu/data-and-maps/data/aqereporting-8 (European Environment Agency 2021).
Recoding the Regions
Recoding means that the boundaries are unchanged, but the country changed the names and codes of regions because there were other boundary changes which did not affect our observation unit. We explain the problem and the solution in greater detail in our tutorial that aggregates the data on regional levels.
panel <- readRDS((file.path(data_raw_dir, "climate-panel.rds")))
climate_data_geocode <- panel %>%
mutate ( year = lubridate::year(date_of_interview)) %>%
recode_nuts()
Let’s join the air pollution data and join it by corrected geocodes:
load(file.path("data", "air_pollutants.rda")) ## good practice to use system-independent file.path
climate_awareness_air <- climate_data_geocode %>%
rename ( region_nuts_codes = .data$code_2016) %>%
left_join ( air_pollutants, by = "region_nuts_codes" ) %>%
select ( -all_of(c("w1", "wex", "date_of_interview",
"typology", "typology_change", "geo", "region"))) %>%
mutate (
# remove special labels and create NA_numeric_
age_education = retroharmonize::as_numeric(age_education)) %>%
mutate_if ( is.character, as.factor) %>%
mutate (
# we only have responses from 4 years, and this should be treated as a categorical variable
year = as.factor(year)
) %>%
filter ( complete.cases(.) )
The climate_awareness_air data frame contains the answers of 75086
individual respondents. 17.07% thought that climate change was the most
serious world problem and 33.6% mentioned climate change as one of the
three most important global problems.
summary ( climate_awareness_air )
## rowid serious_world_problems_first
## ZA5877_v2-0-0_1 : 1 Min. :0.0000
## ZA5877_v2-0-0_10 : 1 1st Qu.:0.0000
## ZA5877_v2-0-0_100 : 1 Median :0.0000
## ZA5877_v2-0-0_1000 : 1 Mean :0.1707
## ZA5877_v2-0-0_10000: 1 3rd Qu.:0.0000
## ZA5877_v2-0-0_10001: 1 Max. :1.0000
## (Other) :75080
## serious_world_problems_climate_change isocntry
## Min. :0.000 BE : 3028
## 1st Qu.:0.000 CZ : 3023
## Median :0.000 NL : 3019
## Mean :0.336 SK : 3000
## 3rd Qu.:1.000 SE : 2980
## Max. :1.000 DE-W : 2978
## (Other):57058
## marital_status age_education
## (Re-)Married: without children :13242 18 :15485
## (Re-)Married: children this marriage :12696 19 : 7728
## Single: without children : 7650 16 : 5840
## (Re-)Married: w children of this marriage: 6520 still studying: 5098
## (Re-)Married: living without children : 6225 17 : 5092
## Single: living without children : 4102 15 : 4528
## (Other) :24651 (Other) :31315
## age_exact occupation_of_respondent
## Min. :15.0 Retired, unable to work :22911
## 1st Qu.:36.0 Skilled manual worker : 6774
## Median :51.0 Employed position, at desk : 6716
## Mean :50.1 Employed position, service job: 5624
## 3rd Qu.:65.0 Middle management, etc. : 5252
## Max. :99.0 Student : 5098
## (Other) :22711
## occupation_of_respondent_recoded
## Employed (10-18 in d15a) :32763
## Not working (1-4 in d15a) :37125
## Self-employed (5-9 in d15a): 5198
##
##
##
##
## respondent_occupation_scale_c_14
## Retired (4 in d15a) :22911
## Manual workers (15 to 18 in d15a) :15269
## Other white collars (13 or 14 in d15a): 9203
## Managers (10 to 12 in d15a) : 8291
## Self-employed (5 to 9 in d15a) : 5198
## Students (2 in d15a) : 5098
## (Other) : 9116
## type_of_community is_student no_education
## DK : 34 Min. :0.0000 Min. :0.000000
## Large town :20939 1st Qu.:0.0000 1st Qu.:0.000000
## Rural area or village :24686 Median :0.0000 Median :0.000000
## Small or middle sized town: 9850 Mean :0.0679 Mean :0.008151
## Small/middle town :19577 3rd Qu.:0.0000 3rd Qu.:0.000000
## Max. :1.0000 Max. :1.000000
##
## education year region_nuts_codes country_code
## Min. :14.00 2013:25103 LU : 1432 DE : 4531
## 1st Qu.:17.00 2015: 0 MT : 1398 GB : 3538
## Median :18.00 2017:25053 CY : 1192 BE : 3028
## Mean :19.61 2019:24930 SK02 : 1053 CZ : 3023
## 3rd Qu.:22.00 EL30 : 974 NL : 3019
## Max. :30.00 EE : 973 SK : 3000
## (Other):68064 (Other):54947
## pm2_5 pm10 o3 BaP
## Min. : 2.109 Min. : 5.883 Min. : 66.37 Min. :0.0102
## 1st Qu.: 9.374 1st Qu.: 28.326 1st Qu.: 90.89 1st Qu.:0.1779
## Median :11.866 Median : 33.673 Median :102.81 Median :0.4105
## Mean :12.954 Mean : 38.637 Mean :101.49 Mean :0.8759
## 3rd Qu.:15.890 3rd Qu.: 49.488 3rd Qu.:110.73 3rd Qu.:1.0692
## Max. :41.293 Max. :123.239 Max. :141.04 Max. :7.8050
##
## so2 ap_pc1 ap_pc2 ap_pc3
## Min. : 0.0000 Min. :-4.6669 Min. :-2.21851 Min. :-2.1007
## 1st Qu.: 0.0000 1st Qu.:-0.4624 1st Qu.:-0.49130 1st Qu.:-0.5695
## Median : 0.0000 Median : 0.4263 Median : 0.02902 Median :-0.1113
## Mean : 0.1032 Mean : 0.1031 Mean : 0.04166 Mean :-0.1746
## 3rd Qu.: 0.0000 3rd Qu.: 0.9748 3rd Qu.: 0.57416 3rd Qu.: 0.3309
## Max. :42.5325 Max. : 2.0344 Max. : 3.25841 Max. : 4.1615
##
## ap_pc4 ap_pc5
## Min. :-1.7387 Min. :-2.75079
## 1st Qu.:-0.1669 1st Qu.:-0.18748
## Median : 0.0371 Median : 0.01811
## Mean : 0.1154 Mean : 0.06797
## 3rd Qu.: 0.3050 3rd Qu.: 0.34937
## Max. : 3.2476 Max. : 1.42816
##
Let’s see a simple CART tree! We remove the regional codes, because there are very serious differences among regional climate awareness. These differences, together with education level, and the year we are talking about, are the most important predictors of thinking about climate change as the most important global problem in Europe.
# Classification Tree with rpart
library(rpart)
# grow tree
fit <- rpart(as.factor(serious_world_problems_first) ~ .,
method="class", data=climate_awareness_air %>%
select ( - all_of(c("rowid", "region_nuts_codes"))),
control = rpart.control(cp = 0.005))
printcp(fit) # display the results
##
## Classification tree:
## rpart(formula = as.factor(serious_world_problems_first) ~ .,
## data = climate_awareness_air %>% select(-all_of(c("rowid",
## "region_nuts_codes"))), method = "class", control = rpart.control(cp = 0.005))
##
## Variables actually used in tree construction:
## [1] age_education isocntry
## [3] serious_world_problems_climate_change year
##
## Root node error: 12817/75086 = 0.1707
##
## n= 75086
##
## CP nsplit rel error xerror xstd
## 1 0.0240566 0 1.00000 1.00000 0.0080438
## 2 0.0082703 3 0.92783 0.92783 0.0078055
## 3 0.0050000 5 0.91129 0.91425 0.0077588
plotcp(fit) # visualize cross-validation results
summary(fit) # detailed summary of splits
## Call:
## rpart(formula = as.factor(serious_world_problems_first) ~ .,
## data = climate_awareness_air %>% select(-all_of(c("rowid",
## "region_nuts_codes"))), method = "class", control = rpart.control(cp = 0.005))
## n= 75086
##
## CP nsplit rel error xerror xstd
## 1 0.024056592 0 1.0000000 1.0000000 0.008043837
## 2 0.008270266 3 0.9278302 0.9278302 0.007805478
## 3 0.005000000 5 0.9112897 0.9142545 0.007758824
##
## Variable importance
## serious_world_problems_climate_change isocntry
## 31 26
## country_code BaP
## 20 8
## pm2_5 ap_pc1
## 4 3
## age_education pm10
## 2 2
## education ap_pc2
## 2 1
## year
## 1
##
## Node number 1: 75086 observations, complexity param=0.02405659
## predicted class=0 expected loss=0.1706976 P(node) =1
## class counts: 62269 12817
## probabilities: 0.829 0.171
## left son=2 (25229 obs) right son=3 (49857 obs)
## Primary splits:
## serious_world_problems_climate_change < 0.5 to the right, improve=2214.2040, (0 missing)
## isocntry splits as RRLLLRRRLLRLRLLLLLLLLLLRRLLLRLL, improve= 728.0160, (0 missing)
## country_code splits as RRLLLRRLLRLLLLLLLLLLRRLLLRLL, improve= 673.3656, (0 missing)
## BaP < 0.4300347 to the right, improve= 310.6229, (0 missing)
## pm2_5 < 13.38264 to the right, improve= 296.4013, (0 missing)
## Surrogate splits:
## age_education splits as ----RRRRRR-RRRRRRRRRR-RRRRRRRRRR-RRRRRRRRRR-RRRRRRRRRR-RRRRRL-RRR-RRRRRRRRR--RRRLLR--R-R, agree=0.664, adj=0, (0 split)
## pm10 < 7.491315 to the left, agree=0.664, adj=0, (0 split)
##
## Node number 2: 25229 observations
## predicted class=0 expected loss=0 P(node) =0.3360014
## class counts: 25229 0
## probabilities: 1.000 0.000
##
## Node number 3: 49857 observations, complexity param=0.02405659
## predicted class=0 expected loss=0.2570752 P(node) =0.6639986
## class counts: 37040 12817
## probabilities: 0.743 0.257
## left son=6 (34631 obs) right son=7 (15226 obs)
## Primary splits:
## isocntry splits as RRLLLRRRLLRLRLLLLLLLLLLRRLLLRLL, improve=1454.9460, (0 missing)
## country_code splits as RRLLLRRLLRLLLLLLLLLLRRLLLRLL, improve=1359.7210, (0 missing)
## BaP < 0.4300347 to the right, improve= 629.8844, (0 missing)
## pm2_5 < 13.38264 to the right, improve= 555.7484, (0 missing)
## ap_pc1 < -0.005459537 to the left, improve= 533.3579, (0 missing)
## Surrogate splits:
## country_code splits as RRLLLRRLLRLLLLLLLLLLRRLLLRLL, agree=0.987, adj=0.957, (0 split)
## BaP < 0.1749425 to the right, agree=0.775, adj=0.264, (0 split)
## pm2_5 < 5.206993 to the right, agree=0.737, adj=0.140, (0 split)
## ap_pc1 < 1.405527 to the left, agree=0.733, adj=0.126, (0 split)
## pm10 < 25.31211 to the right, agree=0.718, adj=0.076, (0 split)
##
## Node number 6: 34631 observations
## predicted class=0 expected loss=0.1769802 P(node) =0.4612178
## class counts: 28502 6129
## probabilities: 0.823 0.177
##
## Node number 7: 15226 observations, complexity param=0.02405659
## predicted class=0 expected loss=0.4392487 P(node) =0.2027808
## class counts: 8538 6688
## probabilities: 0.561 0.439
## left son=14 (11607 obs) right son=15 (3619 obs)
## Primary splits:
## isocntry splits as LL---LLR--L-L----------LL---R--, improve=337.5462, (0 missing)
## country_code splits as LL---LR--L-L--------LL---R--, improve=337.5462, (0 missing)
## age_education splits as ----LLLLLL-LLLRRRRRRR-RRRRRRRRRL-RRRRRRLLRR-RRRRLLRLRL-RRLRRR-RRR-LLLLRRR-----LR-----L-R, improve=294.0807, (0 missing)
## education < 22.5 to the left, improve=262.3747, (0 missing)
## BaP < 0.053328 to the right, improve=232.7043, (0 missing)
## Surrogate splits:
## BaP < 0.053328 to the right, agree=0.878, adj=0.485, (0 split)
## pm2_5 < 4.810361 to the right, agree=0.827, adj=0.271, (0 split)
## ap_pc2 < 0.8746175 to the left, agree=0.792, adj=0.124, (0 split)
## so2 < 0.3302972 to the left, agree=0.781, adj=0.078, (0 split)
## age_education splits as ----LLLLLL-LLLLLLLRLR-LRRLRRRRRR-RRRRLLLLLR-LRLRLLRRLL-LLRLLR-LLR-RRLLLLL-----RR-----R-L, agree=0.779, adj=0.071, (0 split)
##
## Node number 14: 11607 observations, complexity param=0.008270266
## predicted class=0 expected loss=0.3804601 P(node) =0.1545827
## class counts: 7191 4416
## probabilities: 0.620 0.380
## left son=28 (7462 obs) right son=29 (4145 obs)
## Primary splits:
## age_education splits as ----LLLLLL-LRRRRRRRRR-RRLRRLRRLL-RRRRLRLLRR-RLRLLLRLRL-RR-RR--RRL-L-LLRRR------------L-R, improve=123.71070, (0 missing)
## year splits as R-LR, improve=107.79460, (0 missing)
## education < 20.5 to the left, improve= 90.28724, (0 missing)
## occupation_of_respondent splits as LRRLRRRRRLRLLLRLLL, improve= 84.62865, (0 missing)
## respondent_occupation_scale_c_14 splits as LRLLLRRL, improve= 68.88653, (0 missing)
## Surrogate splits:
## education < 20.5 to the left, agree=0.950, adj=0.861, (0 split)
## occupation_of_respondent splits as LLLLRLLRRLRLLLRLLL, agree=0.738, adj=0.267, (0 split)
## respondent_occupation_scale_c_14 splits as LRLLLLRL, agree=0.733, adj=0.251, (0 split)
## is_student < 0.5 to the left, agree=0.709, adj=0.186, (0 split)
## age_exact < 23.5 to the right, agree=0.676, adj=0.094, (0 split)
##
## Node number 15: 3619 observations
## predicted class=1 expected loss=0.3722023 P(node) =0.04819807
## class counts: 1347 2272
## probabilities: 0.372 0.628
##
## Node number 28: 7462 observations
## predicted class=0 expected loss=0.326052 P(node) =0.09937938
## class counts: 5029 2433
## probabilities: 0.674 0.326
##
## Node number 29: 4145 observations, complexity param=0.008270266
## predicted class=0 expected loss=0.4784077 P(node) =0.05520337
## class counts: 2162 1983
## probabilities: 0.522 0.478
## left son=58 (2573 obs) right son=59 (1572 obs)
## Primary splits:
## year splits as L-LR, improve=40.13885, (0 missing)
## occupation_of_respondent splits as LRLLRRRRRLRLLLRLLL, improve=18.33254, (0 missing)
## marital_status splits as LRRRLRRRLRRLRLLRRRRRRLRLRLLRR, improve=17.86888, (0 missing)
## type_of_community splits as LRLRL, improve=17.55254, (0 missing)
## age_education splits as ------------LLRRRRRRR-RR-RL-RR---LRRR-R--LR-R-R---R-R--RR-RR--RR------RRR--------------R, improve=14.66121, (0 missing)
## Surrogate splits:
## type_of_community splits as LLLRL, agree=0.777, adj=0.412, (0 split)
## marital_status splits as RRLLLLLRLLLLLLLRRRLLLLLLRLRLL, agree=0.680, adj=0.155, (0 split)
## isocntry splits as LL---LL---L-R----------LL------, agree=0.669, adj=0.127, (0 split)
## country_code splits as LL---L---L-R--------LL------, agree=0.669, adj=0.127, (0 split)
## o3 < 83.06345 to the right, agree=0.650, adj=0.076, (0 split)
##
## Node number 58: 2573 observations
## predicted class=0 expected loss=0.4240187 P(node) =0.03426737
## class counts: 1482 1091
## probabilities: 0.576 0.424
##
## Node number 59: 1572 observations
## predicted class=1 expected loss=0.43257 P(node) =0.02093599
## class counts: 680 892
## probabilities: 0.433 0.567
# plot tree
plot(fit, uniform=TRUE,
main="Classification Tree: Climate Change Is The Most Serious Threat")
text(fit, use.n=TRUE, all=TRUE, cex=.8)
## Warning in labels.rpart(x, minlength = minlength): more than 52 levels in a
## predicting factor, truncated for printout
saveRDS ( climate_awareness_air , file.path(tempdir(), "climate_panel_recoded.rds"), version = 2)
# not evaluated
saveRDS( climate_awareness_air, file = file.path("data-raw", "climate-panel_recoded.rds"))
Daniel Antal
Developer of open-source statistical software
My research interests include reproducible social science, economics and finance.