Health impacts of climate change and mitigation policies in Ireland

Du y, Ka ie e al.
Resea ch Repo
Heal h impac s o clima e change and mi iga ion policies
in I eland
Resea ch Se ies, No. 188
P o ided in Coope a ion wi h:
The Economic and Social Resea ch Ins i u e (ESRI), Dublin
Sugges ed Ci a ion: Du y, Ka ie e al. (2024) : Heal h impac s o clima e change and mi iga ion
policies in I eland, Resea ch Se ies, No. 188, The Economic and Social Resea ch Ins i u e (ESRI),
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HEALTH IMPACTS OF CLIMATE CHANGE
AND MITIGATION POLICIES IN IRELAND
KATIE DUFFY, KELLY DE BRUIN, LOÏC HENRY, CLEMENT KWEKU
KYEI, ANNE NOLAN AND BRENDAN WALSH
RESEARCH
SERIES
NUMBER 188
JULY 2024
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HEALTH IMPACTS OF CLIMATE CHANGE AND
MITIGATION POLICIES IN IRELAND
Ka ie Du y
Kelly de B uin
Loïc Hen y
Clemen Kweku Kyei
Anne Nolan
B endan Walsh
July 2024
RESEARCH SERIES
NUMBER 188
A ailable o download om www.es i.ie
© The Economic and Social Resea ch Ins i u e
Whi ake Squa e, Si John Roge son’s Quay, Dublin 2
h ps://doi.o g/10.26504/ s188
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ep oduc ion in any medium, p o ided he o iginal wo k is p ope ly c edi ed.
ABOUT THE ESRI
The Economic and Social Resea ch Ins i u e (ESRI) ad ances e idence-based
policymaking ha suppo s economic sus ainabili y and social p og ess in I eland.
ESRI esea che s apply he highes s anda ds o academic excellence o challenges
acing policymake s, ocusing on en a eas o c i ical impo ance o 21s Cen u y
I eland.
The Ins i u e was ounded in 1960 by a g oup o senio ci il se an s led by
D T.K. Whi ake , who iden i ied he need o independen and in-dep h esea ch
analysis. Since hen, he Ins i u e has emained commi ed o independen
esea ch and i s wo k is ee o any exp essed ideology o poli ical posi ion.
The Ins i u e publishes all esea ch eaching he app op ia e academic s anda d,
i espec i e o i s indings o who unds he esea ch.
The ESRI is a company limi ed by gua an ee, answe able o i s membe s and
go e ned by a Council, comp ising up o 14 ep esen a i es d awn om a c oss-
sec ion o ESRI membe s om academia, ci il se ices, s a e agencies, businesses
and ci il socie y. Funding o he ESRI comes om esea ch p og ammes suppo ed
by go e nmen depa men s and agencies, public bodies, compe i i e esea ch
p og ammes, membe ship ees, and an annual g an -in-aid om he Depa men
o Public Expendi u e NDP Deli e y and Re o m.
Fu he in o ma ion is a ailable a www.es i.ie.
THE AUTHORS
Kelly de B uin and B endan Walsh a e Senio Resea ch O ice s a he Economic
and Social Resea ch Ins i u e (ESRI) and Adjunc Associa e P o esso s a T ini y
College Dublin (TCD). Clemen Kweku Kyei is a Pos doc o al Resea ch Fellow a
he ESRI. Loïc Hen y is an Assis an P o esso a LEDa, Uni e si é Pa is-Dauphine,
F ance. Anne Nolan is a Resea ch P o esso a he ESRI and Adjunc P o esso
a TCD. Ka ie Du y was a Resea ch Assis an a he ESRI when he esea ch was
conduc ed.
ACKNOWLEDGEMENTS
The esea ch was unded by he I ish Hea Founda ion and I ish Cance Socie y
on behal o he Clima e and Heal h Alliance unde a p og amme o esea ch
on he ‘Heal h E ec s o Clima e Change and Mi iga ion Ac ions in I eland’ ca ied
ou a he ESRI. This wo k was also suppo ed by unding om he En i onmen al
P o ec ion Agency unde he Clima e Change Ad iso y Council Fellowship on
Adap a ion. The au ho s hank he membe s o he S ee ing G oup (Emma Ha e,
I ish Cance Socie y; Ma k Mu phy, I ish Hea Founda ion; Tom McDe mo ,
Uni e si y o Galway; and Seamus McGuinness, ESRI) o help ul guidance and
discussions h oughou he p ojec . We a e e y g a e ul o he Heal hca e
P icing O ice (HPO) and Me Éi eann o p o iding he da a used in he analyses
in his epo .
This epo has been accep ed o publica ion by he Ins i u e, which does no i sel ake ins i u ional
policy posi ions. All ESRI Resea ch Se ies epo s a e pee e iewed p io o publica ion. The au ho (s)
a e solely esponsible o he con en and he iews exp essed.

Table o con en s | iii
TABLE OF CONTENTS
EXECUTIVE SUMMARY ........................................................................................................................ VIII
0.1 In oduc ion .................................................................................................................... iii
0.2 Me hods ........................................................................................................................... ix
0.2.1 Clima e change modelling ix
0.2.2 Heal h impac s ix
0.2.3 Heal h bene i s and co-bene i s x
0.3 Key indings ...................................................................................................................... xi
0.3.1 Clima e change modelling xi
0.3.2 Heal h e ec s o empe a u e change xi
0.3.3 Heal h bene i s and co-bene i s o clima e change mi iga ion measu es xi
0.4 Discussion .........................................................................................................................xii
CHAPTER 1: BACKGROUND .................................................................................................................... 1
1.1 In oduc ion ...................................................................................................................... 1
1.2 Concep ual amewo k (clima e change and heal h) ....................................................... 2
1.3 Li e a u e e iew ( empe a u e change and heal h) ........................................................ 6
1.3.1 In e na ional li e a u e on empe a u e change and heal h 7
1.3.2 I ish li e a u e on empe a u e change and heal h 9
1.4 Concep ual amewo k (heal h bene i s and co-bene i s o clima e change
mi iga ion) ....................................................................................................................... 10
1.4.1 Ai pollu ion 11
1.4.2 Sus ainable anspo and die 12
1.5 Li e a u e e iew (heal h bene i s and co-bene i s o clima e change mi iga ion) ........ 13
1.5.1 Scena io-based analyses 13
1.5.2 Analyses o co-bene i s o speci ic mi iga ion ac ions 15
1.6 Repo s uc u e .............................................................................................................. 17
CHAPTER 2: CLIMATE CHANGE MODELLING ....................................................................................... 18
2.1 In oduc ion .................................................................................................................... 18
2.2 Global and egional clima e models ............................................................................... 18
2.3 G eenhouse gas emissions scena ios .............................................................................. 19
2.4 Fu u e clima e p ojec ions o I eland ............................................................................ 21
2.5 Summa y ......................................................................................................................... 25
CHAPTER 3: HEALTH EFFECTS OF TEMPERATURE CHANGE ................................................................ 26
3.1 In oduc ion .................................................................................................................... 26
Table o con en s | i
3.2 Da a ................................................................................................................................. 27
3.2.1 Hospi al In-Pa ien Enqui y (HIPE) 27
3.2.2 Popula ion da a 28
3.2.3 Me eo ological da a 28
3.2.4 Analy ical sample 29
3.3 Me hodology ................................................................................................................... 30
3.3.1 Ou come a iables 30
3.3.2 Model speci ica ion 1: Tempe a u e bins 30
3.3.3 Model speci ica ion 2: Mean de ia ion model 32
3.4 Resul s ............................................................................................................................. 34
3.4.1 Tempe a u e bin analysis 35
3.4.2 Mean de ia ion model 41
3.5 Discussion ........................................................................................................................ 42
CHAPTER 4: HEALTH BENEFITS AND CO-BENEFITS OF CLIMATE CHANGE MITIGATION
MEASURES ........................................................................................................................................... 44
4.1 In oduc ion .................................................................................................................... 44
4.2 Analyses o heal h bene i s and co-bene i s o I eland ................................................. 44
4.3 Heal h e ec s o ac ions o mi iga e empe a u e change in I eland: Case s udy ......... 48
4.4 Summa y ......................................................................................................................... 53
CHAPTER 5: SUMMARY, DISCUSSION AND POLICY IMPLICATIONS ..................................................... 55
5.1 Summa y o main indings .............................................................................................. 55
5.2 Discussion and policy implica ions .................................................................................. 56
REFERENCES ......................................................................................................................................... 60
APPENDIX A – DATA, VARIABLES AND SUMMARY STATISTICS............................................................ 69
APPENDIX B – LAGGED EFFECTS OF TEMPERATURE BIN MODEL ........................................................ 72
APPENDIX C – MEAN DEVIATION MODEL: GROUP/DIAGNOSIS ANALYSIS AND FULL MODEL
RESULTS ............................................................................................................................................... 73
APPENDIX D – PROJECTION ANALYSIS ................................................................................................. 76
Scena io RCP4.5 – mos likely ................................................................................................. 76
Lis o ables |
LIST OF TABLES
Table 2.1 The inc ease in global mean empe a u e compa ed o p eindus ial le el 21
Table 3.1 Tempe a u e bins used in he analysis 31
Table 3.2 Illus a ion o mean de ia ion admissions a iable 34
Table 3.3 Eme gency in-pa ien hospi al admissions (pe 100,000 popula ion) 2015–2019 35
Table 3.4 Tempe a u e bin analysis 36
Table 3.5 Tempe a u e bin analysis – age g oup analysis 38
Table 3.6 Tempe a u e bin analysis – diagnos ic g oup analysis 40
Table 3.7 Mean de ia ion model, qua e s 2 and 3 41
Table 3.8 Nume ical illus a ion 42
Table 4.1 Hea -a ibu able mo ali y in I eland (addi ional dea hs) 46
Table 4.2 Heal h and economic bene i s o ai pollu ion educ ions (pe annum) 48
Table 4.3 Illus a ion o empe a u e scena io da a 49
Table 4.4 Numbe o days pe qua e abo e he 90 h h eshold o he RCP4.5 (“mos likely”)
scena io 50
Table 4.5 A e age qua e ly h eshold alues o he 75 h, 90 h and 95 h h esholds 50
Table 4.6 HIPE model esul s 51
Table 4.7 P ojec ed inc ease in eme gency hospi alisa ions by 20-yea pe iod (RCP4.5, 90 h
pe cen ile) 52
Table 4.8 Lowe bound es ima es (RCP4.5, 95 h pe cen ile) 52
Table 4.9 Compa ison o annual inc eases ac oss RCP scena ios 53
Table A.1 Summa y o a iables used om HIPE 69
Table A.2 ICD-10-AM codes o diagnosis a iables 70
Table B.1 Coe icien es ima es o empe a u e bin analysis 72
Table C.1 Mean de ia ion model by age g oup 73
Table C.2 DI e ences model by diagnosis g oup 74
Table C.3 Coe icien es ima es om he mean de ia ion model 75
Table D.1 Numbe o days pe qua e abo e he 75 h h eshold (RCP4.5) 76
Table D.2 P ojec ed inc ease in eme gency hospi alisa ions (75 h h eshold and RCP4.5) 76
Table D.3 Numbe o days pe qua e abo e he 95 h h eshold (RCP4.5) 76
Lis o igu es | i
LIST OF FIGURES
Figu e 0.1 In og aphic o e ec s o clima e change on heal h x
Figu e 1.1 In og aphic o e ec s o clima e change on heal h 4
Figu e 1.2 In og aphic o e ec s o inc easing empe a u es on heal h 5
Figu e 1.3 In og aphic o heal h bene i s and co-bene i s o mi iga ion ac ions 11
Figu e 2.1 Dublin Coun y his o ical and u u e annual maximum empe a u e 22
Figu e 2.2 P ojec ions o empe a u e change o RCP4.5 (A) and RCP8.5 (B) scena ios 23
Figu e 2.3 Mid-cen u y seasonal p ojec ions o empe a u e change o RCP4.5 (a) and RCP8.5 (b)
scena ios 24
Figu e 3.1 A e age daily maximum empe a u e (°C) and ain all (mm) by coun y in 2019 29
Figu e 3.2 Coe icien plo o empe a u e bins 37
Figu e 4.2 T end in hea -a ibu able dea hs 46
Figu e A.1 Eme gency in-pa ien hospi al admissions a e by coun y (2019) 71
Execu i e summa y | xiii
Uni ed S a es’ Gul Coas .
Finally, he epo unde lines he impo ance o app op ia e da a collec ion and
a ailabili y in clima e change esea ch. Global Bu den o Disease s udies a e only
now s a ing o include empe a u e change as a isk ac o o global disease and
mo ali y, and a e unde pinned by nume ous assump ions, many o which will no
be ans e able o an I ish (o mode a e clima e) con ex . The esea ch in his
epo adop ed a mo e di ec app oach, quan i ying empe a u e change impac s
on heal h (i.e., mo bidi y) ia hospi al admissions om he HIPE da ase . This
app oach allowed o he con ol o addi ional ac o s in luencing hospi al
admissions and emphasises he alue o making adminis a i e heal h da a like
HIPE accessible o esea che s and policymake s, and he linking o such da a wi h
o he da ase s (e.g., wea he da a), o ully ha ness i s ichness o e idence-based
policy o mula ion.

Backg ound | 1
CHAPTER 1
Backg ound
1.1 INTRODUCTION
Recogni ion o he need o limi clima e change has d i en global nego ia ions
conce ning combined e o s o dec ease g eenhouse gas (GHG) emissions o e he
pas decades wi hin he Uni ed Na ions F amewo k Con en ion on Clima e Change
(UNFCCC). In 2015, he Pa is Ag eemen was adop ed and o da e has been a i ied
by 197 s a es and he Eu opean Union (EU); he global commi men o his
ag eemen was ein o ced in he Con e ence o Pa ies (COP) held in No embe
2021 and he esul ing Glasgow Clima e Pac . Unde he Pa is Ag eemen , he EU
has submi ed i s EU-wide emissions a ge s ( h ough Na ionally De e mined
Con ibu ions (NDCs)), which commi o a GHG emissions educ ion goal o a leas
55 pe cen compa ed o 1990 le els by 2030 and ne -ze o emissions by 2050.
These a ge s ha e been legisla ed h ough he EU Clima e Law, making hem
legally binding.
I eland has shown i s commi men o educing emissions, whe e he P og amme
o Go e nmen 2020 included an annual emissions educ ion a ge o 7 pe cen ,
esul ing in a 51 pe cen educ ion o emissions by 2030 (Go e nmen o I eland,
2020). This a ge was made legally binding by he Clima e Ac ion and Low Ca bon
De elopmen (Amendmen ) Ac o 2021, and u he commi s o ne -ze o
emissions by 2050. The go e nmen has also made signi ican s ides o e he pas
yea s o in oduce and o mula e policies needed o ensu e hese a ge s a e me .
The Clima e Ac ion Plans 2019 and 2021 p o ide a de ailed plan o measu es
needed o his ansi ion, wi h he hi d na ional upda e, he Clima e Ac ion Plan
2023, se ing ou he addi ional measu es equi ed o align wi h economy-wide
ca bon budge s and sec o al emission ceilings (Go e nmen o I eland, 2021,
2022). Howe e , he En i onmen al P o ec ion Agency (EPA) has no ed ha I eland
is only on a ge o achie e a educ ion o 29 pe cen in GHG emissions by 2030
compa ed o a a ge o 51 pe cen (EPA, 2023).
When analysing he e ec s o clima e change, he ocus is o en on he economic
cos s o socie y. Fo example, he Eu opean En i onmen Agency (EEA) es ima ed
ha he economic losses om wea he - and clima e- ela ed ex emes in Eu ope
eached app oxima ely hal a illion eu o o e he las 40 yea s (Eu opean
En i onmen Agency, 2021), and €48bn in 2021 alone ( an Daalen e al., 2022). Fo
Ge many, Ka lsson and Zieba h (2018) es ima e ha one addi ional ho day wi h
empe a u es abo e 30°C would c ea e mone ised heal h losses o be ween
€750,000 o €5 million pe 10 million popula ion. In F ance, Adélaïde e al. (2022)
es ima e ha he economic impac o heal h e ec s om hea wa es o e he
pe iod 2015–2019 amoun s o app oxima ely €25.5 billion including mo ali y,
mino es ic ed ac i i y days and mo bidi y. Henshe (2023) highligh s he ac
Backg ound | 2
ha clima e change can also a ec he sus ainabili y o he heal hca e sec o . He
no es ha i will be necessa y o p epa e he heal hca e sys em o eme ging
heal h needs as well as he physical, economic and social impac s o con inued
clima e change.
Heal h impac s o clima e change a e impo an and emain ela i ely unexplo ed
o I eland. As clima e change is p edic ed o wo sen o e ime, clima e change-
ela ed heal h impac s a e likely o become mo e p onounced e en in empe a e
clima es (Gibney e al., 2022). In addi ion, he heal h isks associa ed wi h clima e
change a e mo e p onounced o ulne able popula ion g oups, such as he olde
popula ion and child en, and hose wi h p e-exis ing ch onic diseases (C immins e
al., 2016; EASAC, 2019; Flood e al., 2020; Romanello e al., 2022). Mo e socio-
economically disad an aged popula ions a e also mo e ulne able o he e ec s o
clima e change (Kaźmie czak e al., 2022).
The e is also li le discussion o he a oided clima e impac s in I eland as a esul
o clima e change mi iga ion measu es. Mi iga ion measu es limi he ex en o
clima e change and hence limi he clima e- ela ed heal h impac s in I eland.
Ce ain mi iga ion measu es also ha e concomi an heal h co-bene i s, e.g., he
shi o mo e bicycle-based commu ing h ough inc eased cycle lanes o he swi ch
o lowe mea consump ion can help educe emissions and imp o e heal h
ou comes. The esea ch de ailed in his epo , ca ied ou as pa o a esea ch
p og amme unded by he I ish Hea Founda ion and I ish Cance Socie y on
behal o he Clima e and Heal h Alliance, aims o help ill his gap. I ocuses on
he heal h impac s o clima e change (and speci ically empe a u e change) and
how global and na ional commi men s o limi ing empe a u e change may educe
hese impac s in I eland. The e o e, i also aims o be e unde s and some o he
heal h bene i s and co-bene i s o clima e change mi iga ion e o s.
The ollowing sec ion (Sec ion 1.2) se s ou a concep ual amewo k ha desc ibes
he a ious ways in which clima e change may a ec heal h. I p o ides a
amewo k ha unde pins he analysis o he heal h e ec s o clima e change in
he I ish con ex (ca ied ou in Chap e 3). Focusing on he heal h impac s o
empe a u e change, Sec ion 1.3 hen discusses he na ional and in e na ional
li e a u e on he heal h e ec s o empe a u e change. Sec ion 1.4 in oduces he
concep ual amewo k ha unde pins he analysis o heal h bene i s and
co-bene i s o clima e change mi iga ion ac ions (ca ied ou in Chap e 4), while
Sec ion 1.5 discusses he na ional and in e na ional li e a u e on he heal h
bene i s and co-bene i s o clima e mi iga ion ac ions. Sec ion 1.6 p o ides a b ie
o e iew o he s uc u e o he emainde o he epo .
1.2 CONCEPTUAL FRAMEWORK (CLIMATE CHANGE AND HEALTH)
When examining he e ec s o clima e change on heal h, i is help ul o p o ide a
amewo k in which o concep ualise he ways in which clima e change a ec s
heal h. In e na ional s udies ha e shown ha he pa hways h ough which clima e
Backg ound | 3
change a ec s heal h a e complex (C immins e al., 2016; Romanello e al., 2022).
The e a e h ee easons unde pinning his complexi y:
• he a ious e en s ha could occu (e.g., inc easing empe a u es,
hea wa es, looding);
• whe he pa hways a e di ec o indi ec (i.e., media ed ia o he ac o s);
• he a ious heal h ou comes ha can be measu ed (e.g., speci ic diseases,
mo ali y).
The la es Assessmen Repo o he In e go e nmen al Panel on Clima e Change
(IPCC) se s ou he mos up- o-da e scien i ic in o ma ion in ela ion o global
clima e change. Physical changes a ibu ed o clima e change include inc eases in
ho ex eme empe a u es, uppe ocean acidi ica ion, global sea le el ise, glacie
e ea , inc ease in hea y p ecipi a ion, inc ease in looding, inc ease in i e
wea he and inc ease in ag icul u al and ecological d ough (Cisse and McLeman,
2023). Fo Eu ope, ex eme wea he e en s (e.g., hea wa es), no hwa d
mo emen o diseases (e.g., mala ia), d ough , o es i es and soil mois u e
de ici s ha e been highligh ed as pa icula conce ns (Eu opean En i onmen
Agency, 2021).
In e ms o pa hways, he li e a u e ha aids in he concep ualisa ion o clima e
change e ec s on heal h illus a es he e ec pa hways in he ollowing way
(Smi h, 1999; Depa men o Heal h, 2019; EASAC, 2019; Romanello e al., 2022).
The pa hways a e sepa a ed in o wo dis inc channels: di ec and indi ec .
Indi ec pa hways a e u he sepa a ed in o e ec s media ed by
(1) ecosys em/en i onmen , and (2) ins i u ions/in as uc u e. Di ec e ec s a e
no media ed by o he ac o s and cap u e, o example, he e ec ha inc easing
empe a u es o ex eme wea he e en s ha e on heal h ou comes. This could
include looding di ec ly causing inju y, bu also hea exace ba ing ca dio ascula
and espi a o y diseases, especially among aile indi iduals. Indi ec pa hways,
by con as , a e media ed by o he ac o s. Ho e empe a u es can gi e ise o
bac e ial condi ions in wa e ha can lead o wa e -bo ne disease ou b eaks, o
wild i es ha can be linked wi h espi a o y and ci cula o y disease complica ions
(Na a o e al. 2019). These pa hways a e summa ised in he ollowing in og aphic
de ised by he au ho s o his epo .
Backg ound | 4
FIGURE 1.1 INFOGRAPHIC OF EFFECTS OF CLIMATE CHANGE ON HEALTH
Sou ce: In og aphic de ised by au ho s based on li e a u e.
Figu e 1.1 shows ha he e a e mul iple heal h ou comes om e en jus one
clima e change e en . Fo example, inc eases in empe a u e can cause
ca dio ascula and espi a o y dis ess, while also con ibu ing o longe wa m
seasons, aiding he ansmission o insec -bo ne and wa e -bo ne diseases.
Acco ding o esea ch ca ied ou as pa o he 2019 Global Bu den o Disease
(GBD) s udy, he high empe a u e- ela ed disabili y-adjus ed li e yea (DALY) and
dea h a es we e he highes o lowe espi a o y in ec ions, ollowed by s oke
and diabe es melli us (Song e al., 2021).
The di ec and indi ec e ec s o clima e change occu wi hin a social and economic
con ex . This con ex is impo an o conside because ce ain ulne able g oups in
he popula ion a e mo e a isk. Li e a u e has shown ha hese g oups include,
bu a e no limi ed o, hose who a e aged o e 65 o unde 5, hose wi h ch onic
illnesses o disabili ies and hose who a e socio-economically disad an aged
(C immins e al., 2016; EASAC, 2019; Romanello e al., 2022). Fo example, hose
in mo e disad an aged social posi ions may be mo e likely o li e in a eas ha a e
exposed o clima e change (Eu opean En i onmen Agency, 2018). Vulne able
popula ion g oups may also be mo e ulne able o he heal h-damaging e ec s o
clima e change such as ai pollu ion, due o o he cha ac e is ics such as poo
housing condi ions, ch onic disease, e c. Ce ain occupa ional g oups, such as
ou doo wo ke s, pa amedics, i e igh e s and anspo wo ke s, as well as
wo ke s in ho indoo wo k en i onmen s, will be especially ulne able o ex eme
hea (Flood e al., 2020). These complex in e ac ions a e illus a ed o inc easing
empe a u es in he ollowing in og aphic de ised by he au ho s based on epo s
om he IPCC (Smi h e al., 2014; Cisse and McLeman, 2023), Depa men o Heal h
(2019) and EASAC (2019). I would be possible o concep ualise o he clima e
change e en s (e.g., looding) in a simila amewo k.
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FIGURE 1.2 INFOGRAPHIC OF EFFECTS OF INCREASING TEMPERATURES ON HEALTH
Sou ce: In og aphic de ised by au ho s based on li e a u e.
This amewo k unde pins he app oach aken o s uc u e he analysis in he i s
pa o he epo . The analysis will p ima ily examine he e ec s o inc easing
empe a u es on heal h, which is iden i ied by he EEA o be one o he key clima e-
heal h h ea s acing Eu ope (Kaźmie czak e al., 2022). The EEA also no es ha
despi e high a e age li ing s anda ds, Eu ope’s ageing socie y and p e alence o
ch onic diseases make i s popula ion pa icula ly ulne able o hea (Kaźmie czak
e al., 2022). While clima e-heal h h ea s a e likely o di e be ween no he n and
sou he n Eu ope, he Depa men o Heal h (2019) and Desmond e al. (2017) ha e
iden i ied inc easing empe a u es as one o he p incipal heal h h ea s acing
I eland wi h ega d o clima e change. In I eland, mean ai empe a u es ha e
inc eased by 0.8°C in he 1900–2011 pe iod, wi h p ojec ions ou o he mid-
cen u y es ima ing an inc ease o 1.1–1.6°C (Desmond e al., 2017). Desmond e
al. (2017) also highligh he isk om inc eased p ecipi a ion o looding which can
inc ease he isk o wa e -bo ne diseases such as campylobac e iosis and
c yp ospo idiosis. Fu u e esea ch (discussed in Chap e 5) could examine he
heal h impac s o hese and o he likely clima e change e en s in I eland, as well
as he impac s on o he ou comes such as wo ke p oduc i i y. In he ollowing
sec ion, we p o ide a mo e de ailed o e iew o he na ional and in e na ional
li e a u e ha has examined he e ec o empe a u e change on heal h, be o e
mo ing on o discuss he concep ual amewo k and li e a u e on he heal h
bene i s and co-bene i s o clima e change mi iga ion.

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1.3 LITERATURE REVIEW (TEMPERATURE CHANGE AND HEALTH)
The a ailable li e a u e on he e ec s o empe a u e change on heal h co e s a
wide ange o coun ies, ime pe iods and modelling app oaches. The clima e in
which empe a u e-heal h s udies a e conduc ed is impo an as coun ies o ci ies
ha a e mo e acclima ised o hea may ha e al eady de eloped adap ion
measu es o deal wi h ho e empe a u es. Acclima isa ion can occu h ough
physical adap a ion, housing cha ac e is ics, o beha iou al pa e ns (e.g., s aying
indoo s, changing wo k pa e ns) (Ande son and Bell, 2009).
1
Indeed Ba eca e al.
(2016) ound ha he di usion o ai condi ioning explained nea ly all o he
decline in hea - ela ed mo ali y obse ed in he US since 1960. This means ha
any iden i ied e ec s o empe a u e on heal h om hese s udies may be mu ed
o only exis o ex eme empe a u es, and may no he e o e be gene alisable o
mo e empe a e clima es. Baccini e al. (2008) deal wi h his in hei s udy o
mul iple ci ies by allowing he h eshold empe a u e alue o a y ac oss di e en
ci ies: hey show ha he h eshold empe a u e o London is 23.9°C compa ed o
Rome, which had a h eshold alue o 30.3°C.
How empe a u e is cha ac e ised, and he esul ing modelling app oach, can also
di e conside ably ac oss s udies. Fo example, Ka lsson and Zieba h (2018)
con as he di e ing app oaches implemen ed ac oss epidemiological and
economic s udies. In some cases, empe a u e is cha ac e ised as a con inuous
a iable, in o he s empe a u e is ca ego ised in o ‘ empe a u e bins’ o allow o
mo e lexible unc ional o ms (Deschênes and G eens one, 2011; Whi e, 2017;
Gibney e al., 2022; Liao e al., 2023), while in o he s h eshold o pe cen ile
alues a e cons uc ed (Haja e al., 2006; Baccini e al., 2008; B ei ne e al., 2014).
Many s udies also ake accoun o he po en ial o lagged e ec s in he esponse
o heal h ou comes (e.g., mo ali y) o changes in empe a u e (Goodman e al.,
2004; Baccini e al., 2008; Ande son and Bell, 2009; Zeka e al., 2014; Liao e al.,
2023).
Fu he mo e, p e ious esea ch uses a a ie y o heal h ou come measu es o
assess he heal h impac s o empe a u e change. Indeed, a ecen o e iew o
sys ema ic e iews o he li e a u e on he e ec s o clima e change on heal h
ca ego ised heal h impac s in o en b oad g oups (Rocque e al., 2021). These
g oups co e ed ou comes such as hospi alisa ions, mo ali y, in ec ious diseases
and espi a o y, ca dio ascula and neu ological disease, and men al heal h and
wellbeing (Rocque e al., 2021). Mo ali y is gene ally measu ed by using he
numbe o dea hs (all-cause and in some cases, cause-speci ic) o he age-
s anda dised mo ali y a e. Mo ali y is he mos common ou come examined in
exis ing s udies o he e ec s o empe a u e on heal h (Haja e al., 2006; Baccini
e al., 2008; Deschênes and G eens one, 2011; B ei ne e al., 2014; Gaspa ini e
al., 2022; Liao e al., 2023). Howe e , while mo ali y measu es dea hs in a
popula ion, mo bidi y is also a key ou come. Mo bidi y ocuses on he p e alence
1
I has been sugges ed he same is ue o cold wea he . Deschênes and G eens one (2011), howe e , ind e y weak
e idence in suppo o his hypo hesis o cold wea he .
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and impac o diseases and heal h condi ions, including non- a al condi ions.
Mo bidi y allows o he b oade impac o disease, o he causes o disease (e.g.,
empe a u e inc eases), on quali y o li e and heal hca e sys ems o be examined.
Mo bidi y is commonly p oxied by calcula ing heal hca e u ilisa ion, such as
in-pa ien hospi al admission a es o he a e o a endances o eme gency
depa men s (EDs). In Sec ion 3.2 in Chap e 3, we desc ibe he da a and me hods
we use in his analysis o model he impac o empe a u e change on heal h in
I eland. In he ollowing sec ions (Sec ions 1.3.1 and 1.3.2), we su ey he ele an
na ional and in e na ional li e a u e on empe a u e change and heal h.
1.3.1 In e na ional li e a u e on empe a u e change and heal h
The 2019 GBD s udy p o ides a comp ehensi e pic u e o mo ali y and disabili y
ac oss coun ies, ime, age, and sex. I quan i ies heal h loss om hund eds o
diseases, inju ies and isk ac o s. As a esul o mul iple eques s o begin
cap u ing impo an dimensions o clima e change in o he GBD s udy, he di ec
ela ionship be ween high and low non-op imal empe a u es on all GBD disease
and inju y ou comes was modelled in he 2019 s udy (Mu ay e al., 2020).
2
Globally, low empe a u e o cold was he 12 h-leading le el 3
3
isk ac o o
dea hs in 2019, con ibu ing o 2.9 pe cen o all dea hs, o 1.65 million dea hs.
Globally, 308,000 dea hs in 2019 we e a ibu able o exposu e o high
empe a u es, concen a ed mainly in sou h Asia, no h A ica, he Middle Eas and
Sub-Saha an A ica. A ela ed s udy o he 2019 GBD ocused speci ically on high
empe a u e, and e alua ed he disease bu den a ibu able o high empe a u e.
The esul s show ha in 2019, 589 dea hs (o a a e o 0.06 pe 100,000 popula ion)
in Wes e n Eu ope we e a ibu ed o high ambien empe a u e. The analysis also
showed ha he disease bu den a ibu able o high empe a u e a ied spa ially,
wi h he hea ies bu den in egions wi h low socio-demog aphic index (SDI) and
he ligh es bu den in egions wi h high SDI (Song e al., 2021).
The e a e wo key pan-Eu opean s udies ha examined hea e ec s on mo ali y
in ci ies. While Baccini e al. (2008) examined he e ec s o empe a u e, Haja e
al. (2006) analysed whe he he e is an added hea wa e e ec on mo ali y.
O e all, posi i e associa ions be ween empe a u e and mo ali y we e iden i ied.
Baccini e al. (2008) es ima ed he obse ed mo ali y e ec pas a ci y-speci ic
h eshold a abou 3.1 pe cen o Medi e anean ci ies and 1.8 pe cen o
no he n Eu ope o each 1°C inc ease abo e he h eshold. Haja e al. (2006)
ound ha o London, mo ali y inc eased by 5.1 pe cen o e e y 1°C inc ease
abo e he iden i ied h eshold. The s udies ound ha o e all summe ime
mo ali y bu den is mo e impo an o examine han he acu e e ec s o
2
Howe e , o he clima e- ela ed ela ionships, such as be ween p ecipi a ion o humidi y and heal h ou comes, ha e no
ye been e alua ed.
3
The GBD me hodology has a isk ac o hie a chy. Le el 1 isk ac o s a e beha iou al, en i onmen al and occupa ional,
and me abolic; Le el 2 isk ac o s include 20 isks o clus e s o isks (e.g., non-op imal empe a u e); Le el 3 includes 52
isk ac o s o clus e s o isks (e.g., high empe a u e) (Mu ay e al., 2020).
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hea wa es. Thei indings also showed g ea e associa ions o espi a o y dea hs,
e en when ai quali y was con olled o .
O he s udies ha e examined he empe a u e-hea ela ionship wi hin one
coun y. B ei ne e al. (2014) conduc ed a s udy on h ee Ge man ci ies, using
ime-se ies analysis o in es iga e he associa ion be ween daily ai empe a u e
and cause-speci ic mo ali y. They ound ha an inc ease om he 90 h o he 99 h
pe cen ile o 2-day mean empe a u e led o an inc ease in non-acciden al
mo ali y by 11.4 pe cen . Al e na i ely, a dec ease om he 10 h o he 1s
pe cen ile in a 15-day mean empe a u e led o an inc ease in mo ali y by 6.2 pe
cen . They ound ha he popula ion aged o e 85 we e he mos suscep ible o
excess hea . Adélaïde e al. (2022) analysed he heal h e ec o hea wa es in
F ance using ED isi s and ou -pa ien clinic isi s. They ound a signi ican e ec o
empe a u e on ED isi s o hea - ela ed symp oms. Liao e al. (2023) examined
he ela ionship be ween ex eme hea and mo ali y using coun y-le el da a o
China o e he pe iod 2000–2015. They ound ha an addi ional day wi h a
maximum empe a u e o 38°C o abo e was associa ed wi h a 1.7 pe cen ne
inc ease in he mon hly mo ali y a e ( ela i e o i ha day’s maximum had been
in he 16–21°C ange).
The use o ED da a o es ima e empe a u e- ela ed heal h e ec s is also applied
in a s udy o he UK (Gibney e al., 2022). This s udy d aws on a simila
me hodology as Whi e (2017), who in es iga ed he ela ionship be ween
empe a u e and ED a endances in Cali o nia. The e a e simila indings om he
wo s udies, bu he analysis in he UK s udy is mo e ele an o he analysis in his
epo . Gibney e al. (2022) ound an immedia e empe a u e e ec on hea -
ela ed mo bidi y, as measu ed by ED isi s. In con as , cold- ela ed mo bidi y has
a lagged esponse o up o h ee weeks a e he empe a u e shock, wi h a g ea e
cumula i e e ec .
O he heal h ou comes ha e also been examined; o example, G a Zi in e al.
(2018) exploi ed a ia ion in su ey in e iew da es wi h young people om he
US Na ional Longi udinal Su ey o You h o examine he e ec o empe a u e on
cogni i e pe o mance. They ind ha ma hs pe o mance declines linea ly abo e
21°C, wi h he e ec s a is ically signi ican beyond 26°C ( he e ec s o
empe a u e luc ua ions on assessmen s o eading ecogni ion and
comp ehension we e non-signi ican ). Mullins and Whi e (2019) analysed he
e ec o empe a u e luc ua ions on a a ie y o men al heal h ou comes
(including ED isi s o men al heal h condi ions, and suicides) in he US, and ound
ha cold empe a u es educe men al heal h symp oms while ho empe a u es
inc ease hem. A ecen sys ema ic e iew ound e idence o associa ions be ween
hea and p e e m bi h (Bekka e al., 2020). Yu e al. (2023) no e ha clima e
change will widen inequi ies in cance incidence and ea men h ough i s
complex connec ions wi h modi iable isk ac o s, such as ambien and household
ai pollu ion. While he e idence base o associa ions be ween high empe a u es
and cance is s ill de eloping, inc easing ul a iole adia ion (UV) exposu e is
associa ed wi h inc eased isks o melanoma and o he skin cance s (e.g.,
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squamous cell skin cance ). Recen esea ch om he US has also ound ha
i e igh e s who deal wi h wild i es ha e inc eased isks o lung cance and
ca dio ascula disease mo ali y (Na a o e al. 2019).
A ew s udies examining empe a u e e ec s on heal h look a dis ibu ional
impac s and cause-speci ic impac s. Rizmie e al. (2022) used a simila
me hodology o bo h Whi e (2017) and Gibney e al. (2022) o UK ED a endance
da a, bu subsequen ly s a i ied he analysis by age and socio-economic
dep i a ion. They also s a i ied by ype o diagnosis. They ound ha olde and
dep i ed popula ions we e he mos a isk o ad e se heal h e ec s om
empe a u e- ela ed illness, and in pa icula o admissions due o me abolic
disease and inju ies. Gaspa ini e al. (2022) also examined he dis ibu ion o
ulne abili y o heal h isks om empe a u e o England using small-a ea da a.
While cold- ela ed excess mo ali y o e he pe iod 2000–2019 was subs an ially
highe han hea - ela ed excess mo ali y, hey ound ha he e was inc eased isk
o hea - ela ed mo ali y in mo e socio-economically dep i ed and u ban a eas.
They ound an inc eased isk o cold- ela ed mo ali y in no he n egions o
England. Using da a om New Yo k Ci y, Lin e al. (2009) ound ha ex eme high
empe a u es inc ease hospi al admissions o ca dio ascula and espi a o y
diso de s, wi h olde and Hispanic esiden s pa icula ly ulne able o he
empe a u e e ec s on espi a o y illnesses.
1.3.2 I ish li e a u e on empe a u e change and heal h
I ish s udies examining he link be ween empe a u e change and heal h
4
a e
simila in me hodology o he Eu opean s udies men ioned in he p e ious sec ion,
al hough all o he I ish s udies summa ised in his sec ion use mo ali y as he
ou come o in e es . The I ish li e a u e shows ha a b oade analysis o bo h ho
and cold wea he is necessa y, as bo h a e shown o ha e signi ican associa ions
wi h mo ali y.
The e a e wo pan-Eu opean s udies in which da a om I eland is included. Healy,
2003 used a mul i-coun y analysis using 14 Eu opean coun ies o examine he
e ec o cold wea he on mo ali y. Baccini e al. (2008) used a simila app oach
bu wi h mul iple Eu opean ci ies and examined he e ec o hea on mo ali y.
Healy (2003) ound ha I eland had he hi d highes a es o excess win e
mo ali y a e Po ugal and Spain o he pe iod 1988 o 1997. Baccini e al. (2008)
ound ha he e was no signi ican associa ion be ween hea and mo ali y o
Dublin be ween 1990 and 2000. Ano he s udy, Pascal e al. (2013), examined he
mo ali y e ec s o i e hea wa es in I eland om 1981 o 2006. O e all, hey
ound ha 294 excess dea hs we e a ibu ed ac oss all o he hea wa es. The
au ho s highligh he u ban- u al di ide showing ha du ing he summe mon hs,
4
While no examined in his epo , an eme ging li e a u e is examining he link be ween o he aspec s o clima e change
and heal h in I eland (see o example, Musacchio e al. (2021) who quan i y he capaci y o p i a e well use s in I eland o
cope wi h lood- igge ed con amina ion isks).
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ha e majo heal h bene i s. Compa ed wi h he e e ence scena io, hey p ojec
ha adop ion o global die a y guidelines would esul in 5.1 million a oided
dea hs pe yea and 79 million yea s o li e sa ed. The equi alen igu es o he
ege a ian die a e 7.3 million a oided dea hs and 114 million li e yea s sa ed, and
o he egan die 8.1 million a oided dea hs and 129 million li e yea s sa ed.
Milne e al. (2015) es ima e ha i he a e age UK die a y in ake we e op imised
o comply wi h he WHO ecommenda ions
9
, he e could be an inciden al
educ ion o 17 pe cen in GHG emissions. Adhe ence o such a die could sa e
almos 7 million yea s o li e los p ema u ely in he UK o e a 30-yea pe iod and
inc ease a e age li e expec ancy by o e 8 mon hs.
Focusing on ca bon axa ion, Vandenbe ghe and Alb ech (2018) simula e h ee
ca bon ax scena ios in he ene gy and ood sec o in Belgium and assess he
esul ing heal h- ela ed co-bene i s (i.e., educ ion in PM ai pollu ion, and
consump ion o animal p oduc s). They ind ha he ca bon ax could p e en
42,300–78,800 DALYs in Belgium, o sa e 0.6–1.1 pe cen o o al heal hca e
expendi u e and an addi ional 0.06–0.12 pe cen o Belgian GDP.
Many o he abo e s udies ely on da a and es ima es om he epidemiological
li e a u e ha examine he impac o beha iou s ha a e consis en wi h clima e
change mi iga ion ac ions, such as inc eased ac i e a el and mo e plan -based
die s, on heal h ou comes ( o summa ies, see Saunde s e al., 2013; God ay e
al., 2018). These es ima es a e hen used o simula e he impac o speci ic clima e
change mi iga ion ac ions on heal h ( o example, see Shaw e al., (2011) o ac i e
a el and Fa chi e al. (2017) o die ). Howe e , es ablishing causali y in he
unde lying ela ionships is di icul (e.g., does ac i e a el lead o be e heal h
ou comes, o a e hose in be e heal h mo e likely o use ac i e a el?) (K oesen
and De Vos, 2020). These es ima es end no o ake in o accoun he
en i onmen al and heal h consequences (bo h posi i e and nega i e) o b oade
beha iou al changes (e.g., a swi ch om mea - o plan -based die s is likely o lead
o inc eases in he consump ion o o he ood g oups such as nu s and seeds)
(Sp ingmann e al., 2016). In addi ion, he iming o heal h co-bene i s is likely o
di e , making quan i ica ion o co-bene i s in o he u u e di icul . Fo example,
bene i s om clima e change mi iga ion ac ions include likely immedia e
educ ions in acu e espi a o y in ec ions in child en om dec eases in ai pollu ion
(pa icula ly in low-income coun ies), sho - e m and medium- e m educ ions in
ca dio ascula disease incidence and mo ali y ha migh occu o e a pe iod o
yea s, and educ ions in cance incidence and mo ali y ela ed o obesi y ha
migh ake place o e decades (Haines e al., 2009).
9
In o de o con o m o he WHO nu i ional ecommenda ions, he UK die would need o con ain less ed mea , dai y
p oduc s, eggs and swee and sa ou y snacks, bu mo e ce eals, ui and ege ables.

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1.6 REPORT STRUCTURE
The emainde o he epo is s uc u ed as ollows: in Chap e 2, we p o ide an
o e iew o clima e modelling and he clima e change p ojec ion p ocess and
scena ios used h oughou his epo ; in Chap e 3 we ou line he esul s o ou
analysis o he e ec s o empe a u e changes o e he pe iod 2015–2019 on
eme gency in-pa ien hospi al admissions; in Chap e 4 we examine he heal h
bene i s and co-bene i s o clima e change mi iga ion measu es on mo ali y,
mo bidi y and selec ed economic ou comes. We also illus a e he po en ial e ec s
on mo bidi y using a simula ion ha p edic s he impac s on eme gency in-pa ien
hospi al admissions o di e en empe a u e pa hs o I eland ou o 2100.
Chap e 5 summa ises and discusses he esul s.
Clima e change modelling | 18
CHAPTER 2
Clima e change modelling
2.1 INTRODUCTION
The Ea h’s clima e sys em is highly complex and in ol es a mul i ude o
in e ac ion mechanisms. To unde s and he Ea h’s clima e sys em, clima e models
ha e been de eloped and imp o ed signi ican ly o e he pas decades. Clima e
models also gi e insigh s in o he e olu ion o he clima e and enable he
p edic ion o u u e changes o he clima e. The Ea h’s clima e sys em consis s o
he a mosphe e (i.e., he laye s o gases ha en elop he Ea h), he hyd osphe e
(i.e., wa e ), he c yosphe e (i.e., ice and snow), he land su ace (i.e., soil and
ocks), and he biosphe e (i.e., animals and plan s), all in luenced by a ious
ex e nal o cing mechanisms such as sola and o bi al a ia ions (IPCC, 2013).
Clima e change modelling is an essen ial ool o unde s anding he Ea h’s clima e
sys em and hence how i will impac socie y. This chap e p o ides an o e iew o
clima e change modelling, including global and egional clima e models,
g eenhouse gas emissions (GHG) scena ios, and I ish clima e p ojec ions based on
simula ions pe o med by he I ish Cen e o High-End Compu ing (ICHEC).
2.2 GLOBAL AND REGIONAL CLIMATE MODELS
Global Clima e Models (GCMs) a e used o unde s and pas clima e a ia ions,
ep oduce his o ical clima e pa e ns, and p edic he cha ac e is ics o he u u e
clima e. They a e complex ma hema ical ep esen a ions o he componen s o he
Ea h’s clima e sys em (a mosphe e, c yosphe e, hyd osphe e, land su ace, and
biosphe e) and hei in e ac ions. By ep esen ing he Ea h’s clima e sys em as a
se o ma hema ical equa ions ha a e nume ically sol ed using es ablished
ma hema ical echniques, GCMs cap u e he in e ac ions be ween he a ious
componen s and hei beha iou o e ime, enabling clima e scien is s o s udy he
long- e m impac s on he clima e (IPCC, 2013; Au hamme , 2018).
GCMs equi e a la ge amoun o compu a ional powe o sol e he complex
equa ions ha con ol he clima e sys em. Using supe compu e s, hey di ide he
Ea h in o a h ee-dimensional g id and calcula e in e ac ions be ween g id cells a
di e en la i udes and loca ions. These calcula ions a e pe o med o e sho
pe iods o ime, o “ ime s eps”, allowing he model o mimic wea he phenomena
o clima ic e en s on imescales anging om hou s o hund eds o yea s
(IPCC, 2013).
While GCMs ha e g ea ly imp o ed ou unde s anding o he Ea h’s clima e
sys em and i s esponse o ex e nal in luences such as sola a ia ions and GHG
emissions, hey a e no wi hou laws. Model pa ame e s a e p one o unce ain y,
small-scale p ocesses canno be ully esol ed, and i is challenging o p ecisely
ep esen eedback mechanisms, cloud o ma ion, and egional clima e dynamics.
Clima e change modelling | 19
Clima e scien is s con inue o e ine and alida e hese models using obse a ional
da a. Also, mul iple models o ensemble simula ions a e used o accoun o
unce ain ies and p esen a ange o po en ial u u e clima e scena ios. The
Coupled Model In e compa ison P ojec (CMIP) is pa amoun in his ega d, as i
b ings oge he clima e modelling g oups om a ound he wo ld o compa e and
analyse clima e model simula ion esul s.
Like GCMs, egional clima e models (RCMs) a e used o simula e and p ojec u u e
clima e condi ions. Howe e , RCMs p o ide mo e de ailed in o ma ion on he
clima e in a pa icula egion, such as a coun y, a own, o a i e basin. They can
cap u e egional ea u es, opog aphy, and local clima e d i e s ha may no be
adequa ely esol ed in GCMs. In o he wo ds, he p ima y dis inc ion be ween
RCMs and GCMs lies in hei spa ial esolu ion and he scope o he a eas hey
co e .
RCMs o en use he ou pu s om la ge -scale GCMs as ini ial and bounda y
condi ions. Tha is, hey ake he coa se-scale in o ma ion p o ided by GCMs and
e ine i o p o ide mo e localised clima e p ojec ions. This downscaling makes i
possible o p o ide clima e da a a scales ha a e impo an o egional planning,
impac assessmen s, and decision-making. I is impo an o no e ha RCMs ha e
he same limi a ions and unce ain ies as GCMs, including pa ame isa ion and
model biases. Mo eo e , he quali y o RCM p ojec ions is in luenced by how
accu a ely GCMs supply he bounda y condi ions (IPCC, 2013).
2.3 GREENHOUSE GAS EMISSIONS SCENARIOS
Human beha iou con inues o inc ease he le el o GHG emissions. GHG
accumula es in he a mosphe e, e lec ing he ou going adia ion om he sun
back o he Ea h, essen ially apping he sun’s hea . As GHG emissions a e he
main d i e o obse ed changes in ou clima e, unde s anding he u u e pa h o
GHG emissions is essen ial o es ima e u u e clima e change. Gi en he
unce ain y o wha u u e GHG emissions will be, di e en scena ios ha e been
de eloped by he in e na ional communi y o clima e esea che s. These scena ios
o GHG emissions a e an in eg al pa o clima e change modelling and a e use ul
o se e al pu poses, including unde s anding and p edic ing u u e clima e
change. They help in es ablishing a connec ion be ween a mosphe ic GHG
concen a ions and changes in global empe a u e and o he clima e a iables. By
simula ing a ious emissions scena ios in clima e models, clima e scien is s can
assess he clima e sys em’s sensi i i y o a ious amoun s o g eenhouse gases.
To ensu e consis ency ac oss esea ch applying u u e clima e change scena ios,
he IPCC de eloped a Special Repo on Emissions (SRE) wi h concomi an
scena ios (SRES) in 2000. These scena ios we e eplaced by he Rep esen a i e
Concen a ion Pa hway (RCP) scena ios o he IPCC i h Assessmen Repo (AR5)
in 2014. RCPs ep esen he di e en u u e ajec o ies o GHG concen a ions in
he a mosphe e based on a wide ange o assump ions ega ding popula ion
g ow h, economic de elopmen , echnological inno a ion and a i udes o social
and en i onmen al sus ainabili y (IPCC, 2014). Fo ins ance, all RCPs include he
Clima e change modelling | 20
assump ion ha ai pollu ion con ol becomes mo e s ingen o e ime as a esul
o ising income le els ( an Vuu en e al., 2011). The e a e ou main RCPs wi h
nume ical alues 2.6, 4.5, 6.0, and 8.5. These numbe s ep esen he adia i e
o cing (i.e., he di e ence be ween he incoming and ou going ene gy om he
sun) alues in he yea 2100. The ou RCPs comp ise a mi iga ion scena io (RCP2.6)
ha esul s in a e y low o cing le el, wo s abilisa ion scena ios (RCP4.5 and
RCP6.0), and a scena io (RCP8.5) ha has ex emely high GHG emissions. In o he
wo ds, RCP2.6 ep esen s a pa hway whe e GHG emissions a e signi ican ly
educed, leading o an es ima ed 1.6°C inc ease in global a e age empe a u e by
2100 ela i e o he p e-indus ial pe iod (1850–1900). In wha ollows, we will
e e o his as he “Pa is Ag eemen ” Scena io. This is an ambi ious in e p e a ion
o he Pa is Ag eemen , e lec ing he goals o he ag eemen , and does no e e
o he cu en pledges unde he Pa is Ag eemen which would esul in
signi ican ly highe concen a ions. RCP8.5 is a pa hway whe e GHG emissions
con inue o g ow unmi iga ed, esul ing in a bes es ima e global a e age
empe a u e ise o 4.3°C by 2100. We will e e o his pa hway as he
“no mi iga ion” pa hway, acknowledging ha his is an ex eme in e p e a ion o
no clima e ac ion and e e s o a wo s case scena io. RCP4.5 and RCP6.0 a e wo
medium s abilisa ion pa hways, wi h a ying le els o mi iga ion (Me O ice,
2018). RCP4.5 is e e ed o as he “mos likely” scena io in he con ex o his
epo . The inc ease in global mean empe a u e p edic ed by he RCP pa hways
o he la e 21s cen u y is shown in Table 2.1.
RCPs and Sha ed Socio-economic Pa hways (SSPs) a e concep ually ela ed, which
is c ucial o highligh . SSPs a e a se o scena ios aimed a p o iding a consis en
amewo k o examining he in e ac ion be ween socio-economic de elopmen
and clima e change. The e a e i e SSPs, and hey ep esen he ange o possible
u u es based on di e en assump ions o u u e socie al ends. See below o a
summa y o SSP na a i es (Riahi e al., 2017).
SSP1 (Sus ainabili y – aking he g een oad)
This ep esen s a u u e cha ac e ised by g adual bu pe asi e shi s owa ds a
mo e sus ainable pa h, emphasising mo e inclusi e de elopmen ha espec s
pe cei ed en i onmen al bounda ies. This implies a wo ld wi h a balanced use o
esou ces and a ansi ion o enewable ene gy sou ces.
SSP2 (Middle o he oad)
This ep esen s a u u e ha does no di e signi ican ly om his o ical ends in
he a eas o social, economic, and echnological de elopmen . This means a wo ld
whe e socie al and en i onmen al policies e ol e slowly, wi h some imp o emen s
bu also pe sis en challenges.
SSP3 (Regional i al y – a ocky oad)
This ep esen s a u u e ma ked by esu gen na ionalism, conce ns abou
compe i i eness and secu i y, and egional con lic s ha push coun ies o
inc easingly ocus on domes ic o , a mos , egional issues. This means a wo ld
wi h g owing inequali ies, egional con lic s, and une en access o esou ces.
Clima e change modelling | 21
SSP4 (Inequali y – a di ided oad)
This ep esen s a u u e cha ac e ised by highly unequal in es men s in human
capi al, combined wi h inc easing dispa i ies in economic oppo uni y and powe
wi h he esul an consequences being inc easing inequali ies and s a i ica ion
bo h ac oss and wi hin coun ies. This means a wo ld wi h signi ican social
dispa i ies, esou ce deple ion, and en i onmen al deg ada ion.
SSP5 (Fossil- uelled de elopmen – aking he highway)
This ep esen s a u u e whe e he push o economic and social de elopmen is
coupled wi h he exploi a ion o abundan ossil uel esou ces and he adop ion
o esou ce- and ene gy-in ensi e li es yles. This means a wo ld hea ily elian on
ossil uels, wi h high GHG emissions and limi ed clima e change mi iga ion e o s.
The p e ious IPCC SRES scena ios we e de eloped in a sequen ial ashion whe e
he socio-economic assump ions we e ansla ed in o esul ing adia i e o cing
and empe a u e change. In he new p ocess, he RCPs and SSPs we e de eloped
sepa a ely and la e linked. This has led o a lack o a clea connec ion be ween he
SSPs and RCPs, we e an SSP could be linked o di e en RCPs. Table 2.1 displays
he SSPs linked o he ou main RCP scena ios and he esul ing es ima ed
empe a u e change.
TABLE 2.1 THE INCREASE IN GLOBAL MEAN TEMPERATURE COMPARED TO PREINDUSTRIAL
LEVEL
SSP Scena io
RCP Scena io
Change in empe a u e
(oC) by 2081–2100
SSP 1
RCP2.6 “Pa is Ag eemen ”
1.6 (0.9 o 2.3)
SSP 2
RCP4.5 “mos likely”
2.4 (1.7 o 3.2)
RCP6.0
2.8 (2.0 o 3.7)
SSP 5
RCP8.5 “no mi iga ion”
4.3 (3.2 o 5.4)
Sou ce: Me O ice (2018), based on Table 12.3 o IPCC AR5 Wo king G oup One.
No es: Numbe s in pa en heses indica e he likely ange.
2.4 FUTURE CLIMATE PROJECTIONS FOR IRELAND
The app oach o egional clima e modelling was employed by clima e esea che s
a he ICHEC o accu a ely model he I ish clima e, cap u e i s dis inc i e ea u es,
and p o ide high spa ial esolu ion (wi h g ids o 4km2) clima e in o ma ion o he
e alua ion o he local e ec s o clima e change. Two RCMs, he Conso ium o
Small-scale Modelling-Clima e Limi ed-a ea Modelling (COSMO-CLM) and he
Wea he Resea ch and Fo ecas ing (WRF), we e used o downscale i e CMIP–
P ojec 5 (CMIP5) GCM da ase s: CNRM-CM5, EC-EARTH ( ou ensemble
membe s), HadGEM2-ES, MIROC5, and MPI-ESM-LR. The simula ions we e un o
he base pe iod 1981–2000 and he u u e pe iod 2041–2060. The di e ence
be ween he wo pe iods p o ides a measu e o clima e change. To accoun o he
unce ain y in u u e GHG emissions, he u u e clima e was simula ed unde bo h

Clima e change modelling | 22
he RCP4.5 (“mos likely”) and RCP8.5 (“no mi iga ion”) scena ios (Nolan and
Flanagan, 2020).
Acco ding o hei simula ion analysis, I eland will be exposed o highe mean
empe a u es in he u u e. The mean annual empe a u e is p ojec ed o inc ease
by 1–1.6°C by he middle o he cen u y (i.e., 2041–2060) compa ed o he
e e ence pe iod 1981–2000, unde he RCP4.5 (“mos likely”) clima e scena io.
Wi h his wa ming will come ho e days and nigh s. In compa ison o he baseline
pe iod, he wa mes 5 pe cen o daily maximum empe a u es a e p ojec ed o
inc ease by 1–2.2°C while he coldes 5 pe cen o daily minimum empe a u es
a e p ojec ed o ise by 1–2.4°C. Howe e , hese p ojec ions show a ia ions in
empe a u e ac oss he coun y ha a e expec ed o be ma ked by inc eased
empe a u es in eas e n egions. Fo ins ance, Figu e 1 demons a es ha he
annual maximum empe a u e in Dublin Coun y has been ising be ween 1961 and
2021. The ICHEC simula ions show ha such an upwa d end is mo e likely o
pe sis , especially in he high- isk scena io implied by he RCP8.5 (“no mi iga ion”)
emission ajec o y. No e ha he yellow line has a conside able deg ee o
a iabili y because i ep esen s he yea ly maximum empe a u e ha was
obse ed in Dublin Coun y.
FIGURE 2.1 DUBLIN COUNTY HISTORICAL AND FUTURE ANNUAL MAXIMUM TEMPERATURE
Sou ce: Me Éi eann g idded wea he da ase o his o ical obse a ions (1961–2021), ICHEC simula ions o u u e
p ojec ions (Nolan and Flanagan, 2020). Au ho s’ calcula ion o agg ega ion a he Dublin Coun y le el.
No es: Yellow line ep esen s he obse ed annual maximum empe a u e in Dublin Coun y. The o ange and ed lines gi e
he p ojec ions o he annual maximum empe a u e in Dublin Coun y o RCP4.5 (o ange) and RCP8.5 ( ed),
a e aged o e a 10-yea olling window among he i e GCMs conside ed in ICHEC simula ions.
Clima e change modelling | 23
The p ojec ed annual and seasonal empe a u es o I eland a e shown in Figu es
2.2 and 2.3, espec i ely. I should be no ed ha in each igu e and scena io, he
u u e pe iod, 2041–2060, is compa ed wi h he 1981–2000 pe iod. Also, he
numbe s included in each plo a e he minimum and maximum p ojec ed changes,
displayed a hei loca ions.
FIGURE 2.2 PROJECTIONS OF TEMPERATURE CHANGE FOR RCP4.5 “MOST LIKELY” (A) AND
RCP8.5 “NO MITIGATION” (B) SCENARIOS
Sou ce: Nolan and Flanagan (2020)
Clima e change modelling | 24
FIGURE 2.3 MID-CENTURY SEASONAL PROJECTIONS OF TEMPERATURE CHANGE FOR RCP4.5 “MOST LIKELY”
(A) AND RCP8.5 “NO MITIGATION” (B) SCENARIOS
Sou ce: Nolan and Flanagan (2020)
Hea wa es a e also p edic ed o become mo e equen by he middle o he
cen u y, wi h he sou heas expe iencing he bigges inc eases. Table 2.2 p esen s
he p ojec ed inc eases o e he 20-yea pe iod 2041–2060 o he “mos ly likely”
and “no mi iga ion” scena ios.
TABLE 2.2 PROJECTED INCREASE IN THE NUMBER OF HEATWAVE EVENTS OVER THE PERIOD
2041–2060
Scena io
P ojec ed inc ease
RCP4.5 (“mos likely”)
1 o 8
RCP8.5 (“no mi iga ion”)
3 o 15
Sou ce: Nolan and Flanagan (2020)
In he RCP4.5 (“mos likely”) and RCP8.5 (“no mi iga ion”) scena ios, he numbe
o “ os days”, o days wi h a minimum empe a u e below 0oC, is expec ed o
decline by 45 pe cen and 58 pe cen , espec i ely. Addi ionally, unde he RCP4.5
(“mos likely”) and RCP8.5 (“no mi iga ion”) scena ios, espec i ely, i is p edic ed
ha he p opo ion o ice days (days wi h a maximum empe a u e colde han
0oC) will decline by 68 pe cen and 78 pe cen . Fo p ecipi a ion, a subs an ial
dec ease is p ojec ed o he summe mon hs, al hough non-summe mon hs a e
p ojec ed o eco d ma ginal changes. O e all, i is p ojec ed ha p ecipi a ion will
Clima e change modelling | 25
display mo e a iabili y by he middle o his cen u y as a esul o an inc easing
equency o d ough s and hea y ain all e en s. Mo e so, he p ojec ed inc ease
in hea wa es will di ec ly impac public heal h and mo ali y, bu his may be o se
by he p ojec ed dec ease in os and ice days (Nolan and Flanagan, 2020).
2.5 SUMMARY
This chap e ga e an o e iew o he clima e modelling behind he clima e
p ojec ions used in his epo . P ojec ing u u e clima e change and social and
economic de elopmen s emains a complex ask wi h high le els o unce ain y.
Applying an I ish-speci ic clima e model ha egionalises global p ojec ions is
he mos obus way o app oach p ojec ions o he u u e clima e o I eland.
In he ollowing analysis, we ocus on h ee RCP scena ios, namely he RCP8.5
(“no mi iga ion”), RCP4.5 (“mos likely”), and he RCP2.6 (“Pa is Ag eemen ”).
Heal h e ec s o empe a u e change | 32
The dependen a iable, 𝑌𝑐,𝑤,𝑦, ep esen s he eme gency in-pa ien admission
a e pe 100,000 popula ion in a pa icula coun y, week and yea . The
empe a u e a iable, 𝑡𝑒𝑚𝑝, is cha ac e ised in a numbe o di e en ways in o de
o es a ious speci ica ions o he ela ionship be ween empe a u e and
eme gency hospi al admissions (desc ibed in g ea e de ail below). Lagged
empe a u e a iable is also included in he speci ica ion because he e can be
esidual e ec s on hospi al admissions om empe a u e in p e ious weeks.
13
Speci ically, 𝑡𝑒𝑚𝑝𝑐,𝑤,𝑦 ep esen s he indica o a iable o empe a u es in bin j
o he cu en week. Simila ly, 𝑡𝑒𝑚𝑝𝑐,𝑤−𝑘,𝑦 ep esen s he indica o a iable o
empe a u es in bin j o he k- h lagged week (o he p e ious week), and
𝑡𝑒𝑚𝑝𝑐,𝑤−𝑙,𝑦 ep esen s he indica o a iable o empe a u es in bin j o he l- h
lagged week (o he wo p e ious weeks). The coe icien s associa ed wi h hese
a iables a e ep esen ed by 𝛽𝑗, 𝜙𝑗,𝑘, and 𝛿𝑗,𝑙 espec i ely. In addi ion, he
diagnosis ca ego ies a e ep esen ed by 𝜂𝑐,𝑤,𝑦, yea end by 𝜏, coun y ixed e ec
by 𝜑, and mon h ixed e ec by 𝑚. The yea end and mon h ixed e ec s a e
included o con ol o annual and seasonal ac o s espec i ely. Rain all is used o
accoun o he e ec s o humidi y along wi h i s lagged alues, as is done in
p e ious s udies (Whi e, 2017). Howe e , o b e i y, i has been excluded om
equa ion (4.1) bu was con olled o du ing he es ima ion p ocess. We include a
numbe o con ol a iables o accoun o he casemix o pa ien s, socio-economic
s a us, and case se e i y which may di e ac oss hospi al, yea s, and weeks wi hin
a gi en yea . The ec o , 𝑿𝑐,𝑤,𝑦, deno es he socio-demog aphic a iables ha we
con ol o in ou eg ession analysis. These con ols a iables cap u e he mean
composi ion o admi ed pa ien s by age and sex o each coun y, week and yea ,
as well as he mean ma i al s a us composi ion, mean p opo ion o pa ien s ha
a e admi ed as p i a e pa ien s, and he mean p opo ion o pa ien s wi h a
medical ca d. Addi ionally, he a iable, 𝐶𝐼𝑐,𝑤,𝑦, is he mean Cha lson co-mo bidi y
index aken o each coun y, week and yea . The Cha lson co-mo bidi y index is a
sco e based on all o a pa ien ’s diagnoses (using ICD codes) and se es as a
p edic o o mo ali y isk wi hin a yea ollowing hospi alisa ion (Cha lson e al.,
1987).
3.3.3 Model speci ica ion 2: Mean de ia ion model
The p e ious empe a u e bins speci ica ion examines all mon hs and explo es he
impac o bo h colde and ho e wea he on eme gency in-pa ien hospi alisa ions
o ou pa ien g oups. Howe e , a key ques ion in his s udy is he implica ions o
inc eases in ho e days on hospi alisa ions, as ho e days a e likely o become
mo e p e alen due o clima e change in he u u e. The e o e, in his analysis, we
ocus only on qua e 2 (Ap il, May, June) and qua e 3 (July, Augus , Sep embe ),
he pe iods whe e ho wea he in I eland occu s.
13
This is common p ac ice in simila li e a u e and he s anda d numbe o lags is 30 days o h ee weeks (Haja e al.,
2006; Baccini e al., 2008; B ei ne e al., 2014; Whi e, 2017; Gibney e al., 2022).

Heal h e ec s o empe a u e change | 33
In his speci ica ion, we cons uc he dependen and empe a u e a iable o
in e es using a me hodology ha es ima es de ia ions om a mul i-yea (2015–
2019) mean. Fo each coun y, week, and yea (deno ed as 𝑐,𝑤,𝑦), he numbe o
admissions (𝑎𝑑𝑚𝑐,𝑤,𝑦) and he empe a u e (𝑡𝑒𝑚𝑝𝑐,𝑤,𝑦) a e compa ed agains he
espec i e i e-yea a e ages (𝑎𝑑𝑚






𝑐,𝑤 o admissions and 𝑡𝑒𝑚𝑝







𝑐,𝑤 o
empe a u e):
𝑑𝑖𝑓𝑓(𝑎𝑑𝑚)𝑐,𝑤,𝑦 =𝑎𝑑𝑚𝑐,𝑤,𝑦−𝑎𝑑𝑚







𝑐,𝑤
𝑎𝑑𝑚







𝑐,𝑤 ,
whe e 𝑎𝑑𝑚𝑐,𝑤,𝑦 is he numbe o eme gency in-pa ien hospi al admissions by
coun y, week and yea . 𝑎𝑑𝑚






𝑐,𝑤,𝑦 ep esen s he a e age numbe o admissions
pe coun y and week o e he i e-yea span om 2015 o 2019. This app oach
allows us o measu e he admissions o a speci ic coun y, week, and yea as a
p opo ion o i s i e-yea a e age.
A simila app oach is applied o he empe a u e a iable o in e es :
𝑑𝑖𝑓𝑓(𝑡𝑒𝑚𝑝)𝑐,𝑤,𝑦 =𝑡𝑒𝑚𝑝𝑐,𝑤,𝑦−𝑡𝑒𝑚𝑝








𝑐,𝑤
𝑡𝑒𝑚𝑝








𝑐,𝑤 ,
whe e 𝑡𝑒𝑚𝑝𝑐,𝑤,𝑦 ep esen s he a e age empe a u e o a coun y, week, yea
(in °C) and 𝑡𝑒𝑚𝑝







𝑐,𝑤 ep esen s he a e age empe a u e pe coun y and week
o e he i e-yea span om 2015 o 2019.
Table 3.2 p o ides a nume ical illus a ion o his p ocess, using week 27 in
No h Dublin as an example. A mean le el o admissions is calcula ed o each
week ac oss he i e yea s (column 3), indica ed abo e by he 𝑎𝑑𝑚






𝑐,𝑤 a iable.
A di e ence is aken be ween he ac ual le el o admissions (𝑎𝑑𝑚𝑐,𝑤,𝑦) o ha
week and he i e-yea mean (column 4). This di e ence is hen calcula ed as a
pe cen age (column 5). This is done o each coun y, week, and yea obse a ion
o bo h hospi al admissions and maximum empe a u e.
Heal h e ec s o empe a u e change | 34
TABLE 3.2 ILLUSTRATION OF MEAN DEVIATION ADMISSIONS VARIABLE
Yea
To al Admissions
Mean Admissions,
2015–2019
Di e ence
% Di e ence
2015
252
274.46
-22.46
-8.18
2016
265
274.46
-9.46
-3.45
2017
258
274.46
-16.46
-6.00
2018
283
274.46
8.54
3.11
2019
307
274.46
32.54
11.86
Sou ce: Au ho s’ analysis.
The speci ica ion is as ollows:
𝑑𝑖𝑓𝑓(𝑎𝑑𝑚)𝑐,𝑤,𝑦=𝛼+𝛽𝑗𝑑𝑖𝑓𝑓(𝑡𝑒𝑚𝑝)𝑐,𝑤,𝑦+𝜙𝑗𝑑𝑖𝑓𝑓(𝑡𝑒𝑚𝑝)2𝑐,𝑤,𝑦+ 𝑿𝑐,𝑤,𝑦
+𝐶𝐼𝑐,𝑤,𝑦+𝜂𝑐,𝑤,𝑦+𝜏+𝜑+𝑚+𝜀𝑐,𝑤,𝑦 (3.2)
whe e c ep esen s he coun y o esidence, w ep esen s he week, and y he yea .
The dependen a iable, 𝑎𝑑𝑚𝑐,𝑤,𝑦, ep esen s he numbe o admissions
compa ed agains he espec i e i e-yea a e age (𝑎𝑑𝑚






𝑐,𝑤). 𝑡𝑒𝑚𝑝𝑐,𝑤,𝑦
ep esen s he numbe o admissions compa ed agains he espec i e i e-yea
a e age (𝑡𝑒𝑚𝑝







𝑐,𝑤). Once mo e, he ec o , 𝑿𝑐,𝑤,𝑦 deno es he socio-demog aphic
and casemix a iables ha we con ol o in ou eg ession analysis, and 𝐶𝐼𝑐,𝑤,𝑦
is he mean Cha lson co-mo bidi y index aken o each coun y, week and yea .
Diagnosis ca ego ies a e ep esen ed by 𝜂𝑐,𝑤,𝑦, yea end by 𝜏, coun y ixed e ec
by 𝜑, and mon h ixed e ec by 𝑚. The yea end and mon h ixed e ec s a e
included o con ol o annual and seasonal ac o s espec i ely. Rain all is used o
accoun o he e ec s o humidi y along wi h i s lagged alues.
3.4 RESULTS
Be o e p esen ing he esul s o he main model speci ica ions ( empe a u e bins
and mean de ia ion), in Table 3.3 we show how eme gency in-pa ien hospi al
admissions (pe 100,000 popula ion) a ied o e he pe iod 2015–2019. This able
shows he a e age weekly eme gency hospi al admissions a e ac oss coun ies o
each yea -qua e .
Heal h e ec s o empe a u e change | 35
TABLE 3.3 EMERGENCY IN-PATIENT HOSPITAL ADMISSIONS (PER 100,000 POPULATION)
2015–2019
2015
2016
2017
2018
2019
Qua e 1
57.8
58.6
58.5
64.9
64.9
Qua e 2
53.3
56.3
58.3
59.2
59.8
Qua e 3
48.3
52.3
53.6
55.0
55.4
Qua e 4
53.3
61.0
61.4
62.1
59.9
Sou ce: Au ho s’ analysis.
Figu e A.1 in Appendix A shows how eme gency hospi al in-pa ien admissions
(pe 100,000 popula ion) a y by coun y o esidence (using 2019 as an example).
The da a show ha admissions a e highes in he wes and midlands. This could be
due o an ageing popula ion in hose a eas.
3.4.1 Tempe a u e bin analysis
Table 3.4 shows he esul s om he empe a u e bin analysis. The e e ence
ca ego y used is he empe a u e bin 10–13°C. This empe a u e bin has he
g ea es p opo ion o week-coun y obse a ions. All o he coe icien s a e
in e p e ed wi h espec o his empe a u e bin. These coe icien s a e also
plo ed in Figu e 3.2.
The esul s show ha o empe a u es g ea e han 16°C, he e a e g ea e a es
o eme gency hospi al admissions. The e is a s a is ically signi ican ela ionship
be ween empe a u es o 16–25°C and eme gency hospi al admissions. The
coe icien plo illus a es how he coe icien ge s la ge a highe empe a u es.
The wo highes empe a u e bins show no s a is ically signi ican ela ionship,
which is likely due o wo easons: he e a e much ewe obse a ions in hese
empe a u e bins (see also Table 3.1), and a highe empe a u es, indi iduals a e
mo e likely o ake p ecau iona y measu es ha p o ec hem om high
empe a u es (e.g., s aying indoo s du ing he ho es pe iod o he day).
The coe icien s in Table 3.4 can be in e p e ed as changes pe 100,000 admissions.
Fo example, a empe a u es be ween 22°C and 25°C, he e was an inc ease in
eme gency hospi al admissions o 4.71 pe 100,000 popula ion compa ed o when
empe a u es a e in he e e ence ca ego y (10–13°C). These coe icien s can also
be in e p e ed as pe cen age changes i hey a e in e p e ed wi h espec o he
mean admissions a e (55.61 pe 100,000 popula ion). In his case, he e is an
inc ease in eme gency admissions o 8.5 pe cen
14
when he empe a u e is
be ween 22°C and 25°C compa ed o 10–13°C.
Full model esul s can be ound in Appendix B.
14
Calcula ed as a p opo ion o he weekly mean admissions a e: ((4.71/55.61)*100).
Heal h e ec s o empe a u e change | 36
TABLE 3.4 TEMPERATURE BIN ANALYSIS
Admissions Ra e
Tempe a u e Bin: 1–4°C
-5.17**
(2.28)
Tempe a u e Bin: 4–7°C
1.73**
(0.83)
Tempe a u e Bin: 7–10°C
-1.93***
(0.47)
Tempe a u e Bin: 10–13°C
Re .
-
Tempe a u e Bin: 13–16°C
0.91
(0.58)
Tempe a u e Bin: 16–19°C
1.97**
(0.78)
Tempe a u e Bin: 19–22°C
3.29***
(0.91)
Tempe a u e Bin: 22–25°C
4.71***
(1.30)
Tempe a u e Bin: 25–28°C
3.94
(2.75)
Tempe a u e Bin: 28°C+
2.78
(3.76)
Coun y Fixed E ec s
Yes
Yea T end
Yes
Mon h Fixed E ec s
Yes
Lagged Tempe a u e Bins
Yes
Socio-Demog aphic Con ols
Yes
Mean dependen a iable
55.61
N
6,88915
R2
0.56
Sou ce: Au ho s’ analysis.
No es: S anda d e o s in pa en heses.
* p<0.05, ** p<0.01, *** p<0.001
15
In o de o calcula e an admissions a e, No h Dublin and Sou h Dublin we e agg ega ed – he e o e, he inal sample size
is 6,889 (5 yea s x 53 weeks x 26 coun ies – 1 week Ca low 2017).
Heal h e ec s o empe a u e change | 37
FIGURE 3.2 COEFFICIENT PLOT OF TEMPERATURE BINS
Sou ce: Au ho s’ analysis.
The analysis was also ca ied ou sepa a ely o each age g oup; he esul s a e
p esen ed in Table 3.5. Resul s show ha he e ec o empe a u e has a la ge
impac on hospi alisa ion o child en (0–14) compa ed o o he age g oups. A
s a is ically signi ican e ec o empe a u e eme ges abo e 16°C, simila o he
main analysis. A empe a u es 22–25oC he e was an inc ease in eme gency
hospi al admissions o 7.02 pe 100,000 popula ion compa ed o when
empe a u es a e in he e e ence ca ego y. This equa es o an inc ease in
eme gency admissions o 12.2
16
pe cen among child en.
Fo he wo king age g oup (15–64), he e ec s a e smalle in magni ude, and
simila ly signi ican only be ween 16 and 25 deg ees. A empe a u es 22–25oC
he e was a 7.8
17
pe cen inc ease in eme gency hospi al admissions compa ed o
when empe a u es a e in he e e ence ca ego y (10–13°C). Hospi al admissions
o he olde (65+) age g oup exhibi a simila pa e n o he esul s in Table 3.4
al hough none o he e ec s (wi h he excep ion o empe a u es be ween 4–7oC)
a e s a is ically signi ican . While he esul s may indica e no e ec o empe a u e
change on eme gency in-pa ien hospi alisa ions o he olde popula ion o e he
pe iod, hese esul s could also indica e adap i e beha iou on he pa o olde
16
(7.02/57.64)*100
17
(2.14/24.45)*100

Heal h e ec s o empe a u e change | 38
people o highe empe a u es in I eland, an e ec ha may kick in a lowe
empe a u es han o o he popula ion g oups.
TABLE 3.5 TEMPERATURE BIN ANALYSIS – AGE GROUP ANALYSIS
Child en (0–14)
Wo king Age G oup
(15–64)
Olde Adul s (65+)
Tempe a u e Bin:
1–4°C
-6.79
(4.27)
-1.80
(1.63))
-13.20
(9.32)
Tempe a u e Bin:
4–7°C
1.03
(1.55)
-0.05
(0.59)
17.58***
(3.27)
Tempe a u e Bin:
7–10°C
-2.15**
(0.89)
-1.16***
(0.34)
-0.26
(1.82)
Tempe a u e Bin:
10–13°C
Re .
-
Re .
-
Re .
-
Tempe a u e Bin:
13–16°C
1.75
(1.10)
0.24
(0.42)
1.73
(2.32)
Tempe a u e Bin:
16–19°C
3.41**
(1.46)
1.15**
(0.56)
-3.19
(3.03)
Tempe a u e Bin:
19–22°C
6.26***
(1.72)
1.52**
(0.66)
-1.93
(3.52)
Tempe a u e Bin:
22–25°C
7.02***
(2.43)
2.14**
(0.93)
3.25
(4.88)
Tempe a u e Bin:
25–28°C
0.26
(5.25)
0.60
(2.01)
11.47
(10.57)7)
Tempe a u e Bin:
28°C+
-5.29
(7.26)
2.71
(2.78)
-1.52
(15.37)
Coun y Fixed
E ec s
Yes
Yes
Yes
Yea T end
Yes
Yes
Yes
Mon h Fixed
E ec s
Yes
Yes
Yes
Lagged
Tempe a u e Bins
Yes
Yes
Yes
Socio-
Demog aphic
Con ols
Yes
Yes
Yes
Mean Dependen
Va iable
57.64
27.45
179.45
N1
6,853
6,886
6,884
R2
0.41
0.34
0.51
Sou ce: Au ho s’ analysis.
No es: S anda d e o s in pa en heses.
1 The sample sizes di e ac oss speci ica ions because some coun y-week-yea uni s will ha e no HIPE obse a ions
o ha age g oup.
* p<0.05, ** p<0.01, *** p<0.001
Heal h e ec s o empe a u e change | 39
This analysis is also ca ied ou o each o he i e diagnosis g oups and Table 3.6
shows hese esul s. The esul s o he diagnosis analysis show e y simila
pa e ns o he whole empe a u e bin analysis, excep ha he e a e also
s a is ically signi ican esul s o he coe icien s a lowe empe a u es
(apa om me abolic diseases, which a e signi ican only a lowe empe a u es).
A empe a u es be ween 1–4°C, i can be seen ha he e is a dec ease in he
eme gency hospi al admissions a e ac oss all diagnos ic g oups. As poin ed ou by
p e ious li e a u e, his is likely a beha iou al e ec whe e he e y cold wea he
p e en s people om seeking medical ca e (Gibney e al., 2022).
Fo all o he chap e s on diseases (excep me abolic diseases), we can see ha
he e a e s a is ically signi ican e ec s o bo h cold and wa m empe a u es on
eme gency hospi al admissions. A lowe empe a u es, he e is a dec ease in
eme gency hospi al admissions. This is consis en wi h he li e a u e in his ield
which posi s ha a cold empe a u es, he e is an a oidan heal h-seeking
beha iou as people do no wan o en u e ou in unsa e condi ions (Gibney e
al., 2022). A wa me empe a u es, ce ain heal h condi ions end o be
exace ba ed such as ci cula o y, espi a o y and in ec ious diseases as well as
inju ies. Ou analysis shows ha eme gency admissions due o me abolic diseases
a e no s a is ically signi ican ly esponsi e o cold empe a u es. Howe e , hey
a e s a is ically signi ican ly esponsi e o wa me empe a u es. Some o he
in e na ional li e a u e also posi s ha he e ec o ising empe a u es on
in ec ious diseases (including E.coli VTEC) may be due o con amina ed wa e
sou ces, whe e he wa m wea he allows bac e ia o li e longe and en e d inking
wa e s eams (Romanello e al., 2022).
Heal h e ec s o empe a u e change | 40
TABLE 3.6 TEMPERATURE BIN ANALYSIS – DIAGNOSTIC GROUP ANALYSIS
(1)
Ci cula o y
Disease
(2)
Respi a o y
Disease
(3)
Me abolic
Diseases
(4)
In ec ious
Diseases
(5)
Inju ies
Tempe a u e Bin: 1–4°C
-5.34**
(2.25)
-5.22**
(2.29)
-4.15*
(2.21)
-5.38**
(2.23)
-5.07**
(2.25)
Tempe a u e Bin: 4–7°C
1.91**
(0.82)
1.76**
(0.83)
1.03
(0.83)
2.01**
(0.80)
1.46*
(0.82)
Tempe a u e Bin: 7–10°C
-1.65***
(0.46)
-1.87***
(0.47)
-0.91**
(0.45)
-1.28***
(0.45)
-1.65***
(0.46)
Tempe a u e Bin: 10–13°C
Re .
-
Re .
-
Re .
-
Re .
-
Re .
-
Tempe a u e Bin: 13–16°C
0.87
(0.58)
0.79
(0.59)
0.89
(0.56)
0.76
(0.57)
0.82
(0.58)
Tempe a u e Bin: 16–19°C
1.81**
(0.77)
1.67**
(0.78)
1.86**
(0.76)
1.74**
(0.76)
1.79**
(0.77)
Tempe a u e Bin: 19–22°C
3.05***
(0.90)
2.86***
(0.92)
2.72***
(0.88)
2.96***
(0.89)
3.23***
(0.91)
Tempe a u e Bin: 22–25°C
4.44***
(1.27)
4.36***
(1.30)
4.42***
(1.25)
4.42***
(1.25)
4.64***
(1.27)
Tempe a u e Bin: 25–28°C
3.89
(2.71)
3.89
(2.76)
6.22**
(2.73)
4.71*
(2.71)
3.72
(2.72)
Tempe a u e Bin: 28°C+
1.69
(3.71)
1.88
(3.76)
2.44
(3.61)
1.66
(3.61)
1.69
(3.71)
Coun y Fixed E ec s
Yes
Yes
Yes
Yes
Yes
Yea T end
Yes
Yes
Yes
Yes
Yes
Mon h Fixed E ec s
Yes
Yes
Yes
Yes
Yes
Lagged Tempe a u e Bins
Yes
Yes
Yes
Yes
Yes
Socio-demog aphic con ols
Yes
Yes
Yes
Yes
Yes
Mean dependen a iable
56.30
56.22
57.63
56.54
56.28
N1
6,869
6,887
5,969
6,743
6,874
R2
0.57
0.56
0.61
0.58
0.57
Sou ce: Au ho s’ analysis.
No es: S anda d e o s in pa en heses.
1 The sample size di e s by diagnosis g oup because o some coun y-week-yea uni s he e we e no HIPE
obse a ions o ce ain diagnosis g oups.
* p<0.05, ** p<0.01, *** p<0.001
Heal h e ec s o empe a u e change | 41
3.4.2 Mean de ia ion model
Table 3.7 p esen s he esul s om he mean de ia ion model, which is es ima ed
o he mon hs Ap il–Sep embe (qua e s 2 and 3) only. The e ec size o he
di e ences model is 0.18, which means ha a 1 pe cen age poin de ia ion in
maximum weekly empe a u e om he mean induces a 0.18 pe cen age poin
de ia ion in weekly hospi al admissions om he mean.
TABLE 3.7 MEAN DEVIATION MODEL, QUARTERS 2 AND 3
%Δ In-Pa ien Hospi al Admissions
% Di e ence in Maximum Tempe a u e
0.18***
(0.03)
% Di e ence in Maximum Tempe a u e2
-0.64***
(0.18)
Tempe a u e Lag 1
-0.04
(0.03)
Tempe a u e Lag 2
-0.01
(0.03)
Coun y Fixed E ec s
Yes
Yea T end
Yes
Mon h Fixed E ec s
Yes
Diagnosis Fixed E ec s
Yes
Socio-demog aphic Con ols
Yes
N
3,518
R2
0.11
Sou ce: Au ho s’ analysis.
No es: S anda d e o s in pa en heses.
* p<0.05, ** p<0.01, *** p<0.001
Table 3.7 clea ly shows ha empe a u e inc eases a e ela ed o highe a es o
hospi alisa ion. To p o ide a mo e in ui i e illus a ion o indings o highligh he
size o his e ec , Table 3.8 applies he esul s om Table 3.7 o he i s week o
July in No h Dublin as a linea p edic o . In his coun y-week obse a ion, he
mean empe a u e ac oss he i e-yea pe iod is 19.3°C and he mean numbe o
weekly admissions is 278. We es ima e he p edic ed change in eme gency
in-pa ien hospi alisa ions o 1°C, 5°C, and 10°C inc ease in empe a u e. I is clea
ha o e y la ge changes, a numbe o addi ional admissions (23, o 8.3% pe
admissions) esul s.
Heal h bene i s and co-bene i s o clima e change mi iga ion measu es | 48
TABLE 4.2 HEALTH AND ECONOMIC BENEFITS OF AIR POLLUTION REDUCTIONS (PER ANNUM)
Cen al
Es ima e
Lowe
Bound
Uppe
Bound
Dea h a e ed om all (na u al)
causes in adul s (≥ 30 yea s)
66
50
73
Ca dio ascula hospi al
admissions (all ages)
12
2
21
Respi a o y hospi al admissions
(all ages)
26
0
54
Res ic ed ac i i y days (all ages)
77,883
72,754
83,012
Los wo kdays in he employed
popula ion (18–65 yea s)
17,470
14,863
20,059
Li e yea s gained
659
498
739
To al economic bene i ($)1
214,173,681
98,834,955
336,129,634
Sou ce: CLIMAQ-H (WHO, 2023)
No e: 1 Assesses he o al economic bene i in $US 2020 p ices assuming a 5% discoun a e.
4.3 HEALTH EFFECTS OF ACTIONS TO MITIGATE TEMPERATURE
CHANGE IN IRELAND: CASE STUDY
Chap e 3 highligh ed a ela ionship be ween empe a u e luc ua ions and
eme gency in-pa ien hospi al u ilisa ion in I eland, which can be in e p e ed as a
p oxy o mo bidi y. This aises conce ns abou how con inued clima e change will
a ec popula ion heal h, he I ish heal hca e sys em and in as uc u e in he
u u e, and how di e en empe a u e pa hs (and associa ed mi iga ion e o )
may lead o g ea e o lesse impac s on eme gency hospi alisa ion u ilisa ion. In
his sec ion, we conduc an illus a i e p ojec ion analysis o es ima e he e ec
ha di e en scena ios o inc eased empe a u es would ha e on eme gency
in-pa ien hospi alisa ion in he u u e. We ollow he b oad app oach ou lined
abo e by using a ious scena ios o u u e empe a u e change o simula e he
p ojec ed impac on eme gency in-pa ien hospi alisa ions, using pa ame e
es ima es om models es ima ed on he HIPE da a used in Chap e 3.
Da a
The da a applied in his analysis conce ns a e age daily maximum empe a u es,
empe a u e h esholds and p ojec ed numbe o days abo e hese h esholds
o e he pe iod 2020–2100 o h ee RCP scena ios. The RCP scena ios ha e been
desc ibed in de ail in Chap e 2. We apply he RCP4.5 (“mos likely”) scena io in
his analysis.
19
19
RCP4.5 – in e media e emissions. CO2 emissions inc ease only sligh ly be o e decline commences a ound 2040
(consis en wi h ull implemen a ion o all cu en policies).

Heal h bene i s and co-bene i s o clima e change mi iga ion measu es | 49
We apply wo key a iables wi hin his p ojec ion analysis:
1. The a e age daily maximum empe a u e pe qua e pe 20-yea
p ojec ion pe iod up o 2100;
2. The p opo ion o days pe qua e pe 20-yea pe iod abo e he 75 h,
90 h and 95 h empe a u e pe cen iles.
Each o hese a iables has alues o each RCP, each 20-yea pe iod, each qua e
and each coun y. This is illus a ed in Table 4.3 o he RCP4.5 (“mos likely”)
scena io.
TABLE 4.3 ILLUSTRATION OF TEMPERATURE SCENARIO DATA
RCP Scena io
Baseline: 1980–2000
Q1 – 4
Coun ies + Dublin PCs1
RCP4.5
Baseline: 1980–2000
Q1 – 4
Coun ies + Dublin PCs
2021–2040
Q1 – 4
Coun ies + Dublin PCs
2041–2060
Q1 – 4
Coun ies + Dublin PCs
2061–2080
Q1 – 4
Coun ies + Dublin PCs
2081–2100
Q1 – 4
Coun ies + Dublin PCs
No e: 1 PC is an ac onym o pos code.
I is di icul o explici ly p ojec ex eme wea he e en s, and o example he
numbe o e y ho days in a gi en pe iod in he u u e. In he p ojec ion analyses,
we include h ee h eshold alues ha a e equi alen o he 75 h pe cen ile
empe a u e, he 90 h pe cen ile empe a u e and he 95 h pe cen ile
empe a u e. These h eshold alues a e based on he baseline da a o he pe iod
1980–2000. These h esholds a e used o p oxy o e y high empe a u e days in
which he empe a u e exceeded each di e en h eshold in he baseline pe iods.
The da a om he RCP scena ios also con ain p ojec ions o he numbe o days
pe qua e pe 20-yea pe iod abo e each o hese h esholds. Gi en he
p opo ion o days p ojec ed o be abo e a ce ain h eshold in a qua e , we
mul iplied his p opo ion by he numbe o days in ha qua e .
The p ojec ions o he numbe o days pe qua e pe 20-yea pe iod abo e each
o hese h esholds di e ac oss RCP scena ios. Table 4.4 p esen s he numbe o
days abo e he 90 h pe cen ile h eshold o each qua e and 20-yea p ojec ion
pe iod o he RCP4.5 (“mos likely”) scena io. This scena io p ojec s ha in
qua e 3 in he 2020–2040 and 2040–2060 pe iods, he e will be app oxima ely
22–25 days in which he empe a u e exceeds he 90 h pe cen ile empe a u e
om he 1980–2000 baseline pe iod. This dec eases in subsequen pe iods as
clima e change mi iga ion measu es a e adop ed.
Heal h bene i s and co-bene i s o clima e change mi iga ion measu es | 50
TABLE 4.4 NUMBER OF DAYS PER QUARTER ABOVE THE 90TH THRESHOLD FOR THE RCP4.5
(“MOST LIKELY”) SCENARIO
2020–2040
2040–2060
2060–2080
2080–2100
Qua e 1
11.3
10.8
24.3
19.6
Qua e 2
11.9
19.4
18.7
17.1
Qua e 3
22.2
24.9
15.3
21.6
Qua e 4
25.9
16.1
22.9
32.8
Me hodology
In o de o align he clima e p ojec ions da a wi h he HIPE da a used in Chap e 3,
he HIPE model was es ima ed a he coun y, qua e and yea le el. Using he
empe a u e da a o 2015–2019, i was possible o calcula e whe he a day was
abo e he 75 h, 90 h o 95 h empe a u e h esholds. The numbe o days we e
agg ega ed up o he qua e le el o each coun y and yea . Table 4.5 p o ides an
illus a ion o he a e age empe a u e h eshold o each qua e based on he
his o ical 1980–2000 da a.
TABLE 4.5 AVERAGE QUARTERLY THRESHOLD VALUES FOR THE 75TH, 90TH AND 95TH
THRESHOLDS
Mean empe a u e h esholds by qua e o I eland
75 h Th eshold
90 h Th eshold
95 h Th eshold
Qua e 1
10.80
11.97
12.64
Qua e 2
16.48
18.53
19.94
Qua e 3
19.95
21.85
23.03
Qua e 4
12.56
13.88
14.57
This da a was hen used o es ima e a model (one o each empe a u e h eshold)
ha eg essed he numbe o days pe coun y pe qua e pe yea abo e he
espec i e h eshold on he a e o eme gency in-pa ien hospi al admissions pe
coun y pe qua e pe yea . As be o e, yea and coun y ixed e ec s we e
included. The coe icien es ima es om his model indica e he e ec ha one
ex a day abo e he h eshold in a qua e has on eme gency hospi al admissions.
The model is es ima ed only o qua e 2 and qua e 3 and is applied only o
qua e 2 and qua e 3 p ojec ions in his analysis. We do no apply he model
es ima es o all qua e s, as highe han a e age empe a u es in win e mon hs
(qua e 4 and qua e 1) would possibly esul in ewe eme gency
hospi alisa ions.
20
The e o e, ou numbe o obse a ions in he es ima ed model
is 260 (26 coun ies, 2 qua e s and 5 yea s o da a).
20
When he model was es ima ed o qua e 1 and qua e 4 only, he coe icien ( o he numbe o days pe qua e abo e
he ele an h eshold) was nega i e (and no s a is ically signi ican ). When he model was es ima ed o qua e 2 and
qua e 3 only, he coe icien was posi i e and s a is ically signi ican .
Heal h bene i s and co-bene i s o clima e change mi iga ion measu es | 51
Nex , we apply hese coe icien es ima es o he p ojec ed qua e ly es ima es o
he numbe o days abo e he espec i e h esholds (see Table 4.4 o hese da a
o each 20-yea p ojec ion pe iod o he RCP4.5 (“mos likely”) scena io o he
90 h pe cen ile). Fo each empe a u e h eshold, RCP scena io and 20-yea
p ojec ion window, his gi es us a p ojec ion o he a e age numbe o ex a
hospi al admissions in a qua e ha we would expec . Finally, we agg ega e he
qua e ly p ojec ions o de i e an annual es ima e o eme gency in-pa ien
hospi alisa ions. This p ocedu e esul s in 36 possible p ojec ions (3 RCP scena ios
x 3 empe a u e h esholds x 4 20-yea p ojec ion windows). In he discussion ha
ollows, we mainly ocus on he esul s using he 90 h pe cen ile h eshold, bu
ull esul s o he RCP4.5 (“mos likely”) scena io a e a ailable in Appendix E.
Resul s
Table 4.6 p esen s he coe icien es ima e esul s o he analysis; each column is
o a di e en empe a u e h eshold. The dependen a iable is he eme gency
hospi al admissions a e (pe 100,000 popula ion). The coe icien es ima es he
e ec ha one ex a day in a qua e abo e he h eshold has on he eme gency
hospi al admissions a e. The model is es ima ed o qua e 2 and qua e 3 only.
TABLE 4.6 EMERGENCY IN-PATIENT MODEL RESULTS, HIPE 2015–2019
75 h Pe cen ile
Th eshold
90 h Pe cen ile
Th eshold
95 h Pe cen ile
Th eshold
Coe icien es ima e
1.79**
(0.52)
2.85***
(0.71)
3.22***
(0.90)
Obse a ions
260
260
260
R2
0.91
0.92
0.91
Mean dependen
a iablea
1,036
1,036
1,036
No e: a ela es o eme gency in-pa ien hospi alisa ions pe 100,000 popula ion. Con ols include socio-demog aphic
cha ac e is ics, medical casemix, coun y ixed e ec s, yea ixed e ec s.
*p<0.05, **p<0.01, ***p<0.001
Ou esul s abo e sugges ha an addi ional day in qua e 2 o qua e 3 abo e
he 90 h empe a u e pe cen ile inc eases he a e o eme gency hospi al
admissions by 2.85 pe 100,000 popula ion. We apply he es ima e abo e o he
90 h h eshold o he numbe o p ojec ed days abo e he 90 h pe cen ile in bo h
qua e 2 and qua e 3. These es ima es a e also o alled o gi e an annual
es ima e.
Table 4.7 shows he es ima ed numbe o addi ional eme gency hospi alisa ions
pe qua e , a e aged o e he 20-yea pe iod. These esul s gi e us an indica ion
o wha he a e age qua e ly inc ease in hospi al admissions would be unde he
RCP4.5 (“mos likely”) scena io o each p ojec ed 20-yea pe iod. Ou esul s
sugges ha he e would be an inc ease o 97.2 pe 100,000 each yea in he pe iod
Heal h bene i s and co-bene i s o clima e change mi iga ion measu es | 52
2021–2040. This igu e inc eases o 126 pe 100,000 in he pe iod 2041–2060.
As a p opo ion o he mean le el o admissions, his is indica i e o a 9.4 pe cen
21
inc ease in eme gency hospi al admissions annually in he pe iod 2021–2040.
This inc eases o 12.2 pe cen annually o he pe iod 2041–2060.
TABLE 4.7 PROJECTED INCREASE IN EMERGENCY IN-PATIENT HOSPITALISATIONS BY 20-YEAR
PERIOD (RCP4.5, 90TH PERCENTILE)
2021–2040
2041–2060
2061–2080
2081–2100
Qua e 2
34.0
55.2
53.4
48.8
Qua e 3
63.2
71.1
43.5
61.6
To al
97.2
126.3
97.0
110.4
Clima e change adap ion scena io
I is unlikely ha he e will be no adap ion o a wa me clima e unde aken by bo h
indi iduals and he heal hca e sys em. As such, we use he 95 h pe cen ile
h eshold as an es ima e o he lowe bound in his p ojec ion analysis. I will
p o ide a p elimina y indica ion o wha he bu den on he heal hca e sys em
migh be i he popula ion and heal hca e sys em adap o highe empe a u es.
I his occu s, hen a highe empe a u e h eshold would be mo e indica i e o
he e ec o clima e change on eme gency hospi alisa ions in he u u e. Using he
coe icien es ima e in he inal column o Table 4.6, we ob ain he ollowing esul s
ha we can use as ou lowe bound.
TABLE 4.8 LOWER BOUND ESTIMATES (RCP4.5, 95TH PERCENTILE)
2021–2040
2041–2060
2061–2080
2081–2100
Qua e 2
22.0
39.3
37.3
34.5
Qua e 3
53.4
57.0
26.9
46.0
To al
75.5
96.3
64.2
80.5
In his case, he annual inc ease in eme gency hospi al admissions o he
2021–2040 pe iod is 75.5 pe 100,000 popula ion. This is equi alen o a 7.3 pe
cen annual inc ease. Fo he pe iod 2041–2060, his annual inc ease becomes
9.3 pe cen .
Al hough hese a e lowe bound es ima es, hey s ill indica e a ela i ely la ge
inc ease in annual eme gency hospi al admissions. This is pa icula ly ue o a
heal hca e sys em ha is cu en ly unc ioning a ull capaci y. These es ima es
sugges he need o clima e change adap ion in he heal hca e sys em o p e en
u u e capaci y p oblems om a ising. Table 4.9 compa es he p ojec ion analysis
and lowe bounds unde he RCP2.6, RCP4.5 and RCP8.5 scena ios.
21
We ake he annual es ima e (97.2) as a p opo ion o he mean dependen a iable (1,036).
Heal h bene i s and co-bene i s o clima e change mi iga ion measu es | 53
TABLE 4.9 COMPARISON OF ANNUAL INCREASES ACROSS RCP SCENARIOS
RCP2.6 – Pa is Ag eemen
RCP4.5 – mos likely
RCP8.5 – no mi iga ion
90 h
pe cen ile
95 h
pe cen ile
90 h
pe cen ile
95 h
pe cen ile
90 h
pe cen ile
95 h
pe cen ile
2021–2040
10.7%
9.1%
9.4%
7.3%
10.2%
7.9%
2041–2060
14.1%
10.4%
12.2%
9.3%
13.0%
10.0%
2061–2080
7.3%
5.0%
9.4%
6.2%
13.5%
9.5%
2081–2100
9.1%
7.2%
10.7%
7.8%
18.3%
14.1%
These indings p edic a signi ican inc ease in he numbe o high empe a u e
days in I eland unde all RCP scena ios. E en unde he mos op imis ic RCP
scena io, he p ojec ed ise in eme gency in-pa ien hospi alisa ions du ing he
2021–2040 and 2041–2060 pe iods is s a k. Howe e , he mo e op imis ic RCP
scena ios do p ojec a lesse e ec on hospi al demand in la e pe iods.
When conside ed alongside in e na ional e idence, hese esul s emphasise he
po en ial need o policymake s o implemen adap i e measu es and inc ease
capaci y o accommoda e he highe hospi al demand om highe empe a u es.
The ou comes in his sec ion should be in e p e ed as a e age annual inc eases
wi hin each p ojec ed pe iod. Howe e , he su ges in hospi al demand due o high
empe a u es a e likely o be concen a ed a ound speci ic yea s, da es, and
loca ions. The inc eased demand o hospi al ca e due o highe empe a u es is
expec ed o be mos acu e in he summe mon hs, wi h qua e 3 eco ding he
highes numbe o days exceeding each h eshold. Fu he mo e, geog aphical
a ia ions in high- empe a u e days may also in luence he hospi als ha migh
expe ience he highes demand o ca e. The e o e, he heal h sys em’s
adap abili y o handle highe empe a u es a pa icula imes and in speci ic
egions will be c ucial in adjus ing o he ele a ed empe a u es in he coming
yea s.
4.4 SUMMARY
In his chap e , we i s p o ided an o e iew o he heal h bene i s and co-bene i s
o clima e change mi iga ion in I eland, using app oaches ha simula e he e ec s
o a ious empe a u e pa h scena ios on heal h, he eby de i ing an assessmen
o he po en ial heal h bene i s and co-bene i s o di e en scena ios. The COACCH
p ojec esul s show ha mi iga ion in line wi h he Pa is Ag eemen (consis en
wi h RCP2.6) would s ill esul in app oxima ely 200 hea -a ibu ed dea hs in
he las decade o he 21s cen u y. A u u e consis en wi h he RCP8.5
(“no mi iga ion”) scena io would esul in mo e han 1,000 addi ional dea hs in he
same decade compa ed o RCP2.6 (“Pa is Ag eemen ”). Focusing speci ically on
ai pollu ion mi iga ion measu es, he esul s o he WHO CLIMAQ-H model o

Heal h bene i s and co-bene i s o clima e change mi iga ion measu es | 54
I eland sugges ha 66 dea hs pe annum could be a e ed, wi h conside able
addi ional social and economic bene i s (e.g., in e ms o wo ke p oduc i i y).
In he second pa o he chap e , we applied clima e p ojec ions o he HIPE da a
used in Chap e 3. This analysis shows ha , unde he mos benign clima e change
scena io (RCP2.6, Pa is Ag eemen ), eme gency in-pa ien admissions in qua e 2
and qua e 3 could inc ease by an a e age o 10 pe cen pe annum o e he
pe iod 2020–2040. I clima e change mi iga ion ac ions we e implemen ed ully in
acco dance wi h he Pa is Ag eemen (RCP2.6), he impac on eme gency
in-pa ien hospi alisa ions could be lowe in he la e hal o his cen u y.
Howe e , i no policy changes a e implemen ed o educe emissions (i.e., a u u e
consis en wi h he RCP8.5 scena io), he e could be a s eep inc ease in annual
eme gency in-pa ien admissions ac oss qua e 2 and qua e 3, o up o 18.3 pe
cen in he pe iod 2080–2100 (depending on he empe a u e h eshold used).
Simula ion exe cises such as hese a e subjec o nume ous assump ions ha a e
necessa y in o de o simpli y he analysis bu which may induce e o in
p ojec ions. In pa icula , in his applica ion, he model es ima ed o HIPE da a
o e he pe iod 2015–2019 assumes ha he ela ionship be ween he a e age
numbe o days pe qua e abo e he a ious empe a u e h esholds and
eme gency in-pa ien hospi alisa ions holds o e he en i e p ojec ion pe iod, i.e.,
up o 2100. As discussed in Chap e 1, p e ious li e a u e has sugges ed ha
indi iduals may adop adap i e s a egies o cope wi h ho e empe a u es;
he e o e, he beha iou s we a e assuming o 2015–2019 may ep esen an uppe
bound on he simula ed e ec s o e ime.
Summa y, discussion and policy implica ions | 55
CHAPTER 5
Summa y, discussion and policy implica ions
5.1 SUMMARY OF MAIN FINDINGS
Recogni ion o he need o limi clima e change has d i en global nego ia ions
conce ning combined e o s o dec ease g eenhouse gas (GHG) emissions.
In I eland, he Clima e Ac ion and Low Ca bon De elopmen (Amendmen ) Ac
o 2021 commi s o he achie emen o an annual emissions educ ion a ge o
7 pe cen , esul ing in a 51 pe cen educ ion o emissions by 2030, and u he
commi s o ne -ze o emissions by 2050. The Clima e Ac ion Plans p o ide a
de ailed plan o measu es needed o his ansi ion.
When analysing he e ec s o clima e change, he ocus is o en on he economic
cos s o socie y. Popula ion heal h impac s o clima e change a e also impo an
and emain ela i ely unexplo ed o I eland. In addi ion, he e is also li le
discussion o he po en ial bene i s and co-bene i s o emission educ ion policies
o popula ion heal h. The esea ch de ailed in his epo , ca ied ou as pa o a
esea ch p og amme unded by he I ish Hea Founda ion and I ish Cance Socie y
on behal o he Clima e and Heal h Alliance, aimed o help ill his gap. I ocuses
on he heal h impac s o clima e change (and speci ically empe a u e change)
and how global and na ional commi men s o limi ing empe a u e change may
educe hese impac s in I eland. The e o e, i also aims o be e unde s and he
heal h bene i s and co-bene i s o clima e change mi iga ion e o s.
Acco ding o he clima e simula ion analysis, I eland will be exposed o highe
mean empe a u es in he u u e. The mean annual empe a u e is p ojec ed o
inc ease by 1–1.6°C by he middle o he cen u y (i.e., 2041–2060) compa ed o
he e e ence pe iod 1981–2000, unde he RCP4.5 clima e scena io ( he mos
likely scena io). Wi h his wa ming will come ho e days and nigh s. The
implica ions o highe empe a u es on mo bidi y we e hen assessed by
combining de ailed me eo ological da a wi h da a on acu e public hospi al
eme gency in-pa ien hospi alisa ions om he Hospi al In-Pa ien Enqui y (HIPE)
sys em o e he pe iod 2015–2019. The esul s showed ha empe a u es
be ween 22°C and 25°C a e associa ed wi h an 8.5 pe cen inc ease in eme gency
in-pa ien hospi alisa ions, wi h child en pa icula ly a ec ed.
In e ms o heal h bene i s and co-bene i s, he esea ch highligh ed indings om
he COACCH p ojec which es ima e ha annual mo ali y unde he mos
pessimis ic scena io (RCP8.5) could be a ound 1,400 addi ional dea hs by he end
o he 21s cen u y, in con as o 216 unde he mos op imis ic scena io (RCP2.6).
Focusing speci ically on ai pollu ion mi iga ion measu es, he esul s o he WHO
CLIMAQ-H model o I eland sugges ha 66 dea hs pe annum could be a e ed,
wi h conside able addi ional social and economic bene i s (e.g., in e ms o wo ke
p oduc i i y). In e ms o mo bidi y, an analysis o he HIPE da a showed ha an
Summa y, discussion and policy implica ions | 56
addi ional day in summe mon hs abo e he 90 h empe a u e pe cen ile in he
pe iod 2081–2100 could be associa ed wi h a 10.7 pe cen age inc ease in annual
eme gency in-pa ien hospi alisa ions unde he RCP4.5 scena io (“mos likely”),
and as high as 18.3 pe cen age inc ease unde he mos pessimis ic (“no
mi iga ion”) RCP8.5 scena io.
5.2 DISCUSSION AND POLICY IMPLICATIONS
The analysis in his epo has highligh ed a numbe o key indings o I eland in
ela ion o clima e change in heal h, in e ms o u u e empe a u e pa hs, impac s
on mo bidi y and p ojec ions o u u e heal h impac s o selec ed empe a u e
pa hs. The analysis is necessa ily limi ed in scope, and does no conside a numbe
o o he impo an issues ha would need o be conside ed o assess a) he ull
heal h e ec s o clima e change in I eland and b) he ull heal h bene i s and
co-bene i s o clima e change mi iga ion ac ions. In pa icula , as illus a ed in
Figu e 1.1, he impac o clima e change on heal h is complex, co e ing mul iple
pa hways ha link a a ie y o clima e change ea u es (e.g., ising empe a u es,
ising sea le els, mo e ex eme e en s, e c.) wi h a my iad o heal h ou comes
(e.g., mo ali y, ch onic disease incidence, men al heal h, e c.).
In his epo , we ocused on he impac o he ea u e o clima e change (i.e.,
inc easing empe a u e) ha is conside ed one o he mos likely and ha m ul
clima e change ea u es o I eland (Desmond e al., 2017). Fu u e wo k in I eland
could conside he impac o o he clima e change e en s on heal h in I eland,
a b oade se o heal h ou comes, and he po en ial o heal h bene i s and
co-bene i s on o he dimensions o heal h in addi ion o mo ali y and eme gency
hospi al admissions. In addi ion, while he analysis in his epo has ocused on
he po en ial heal h co-bene i s o mi iga ion ac ion in e ms o a ious p ojec ed
u u e empe a u e pa hs (consis en wi h di e en mi iga ion scena ios), u u e
wo k could assess he heal h bene i s and co-bene i s o selec ed mi iga ion
ac ions (e.g., mo e sus ainable anspo ).
22
A numbe o implica ions o policy a ise om he analysis in his epo . Policy
esponses o clima e change all b oadly in o wo ca ego ies: mi iga ion
(p e en ing o educing he scale o u u e ha m), and adap a ion, implemen ing a
wide ange o adap a ion solu ions, including e ec i e hea heal h ac ion plans,
u ban g eening, app op ia e building design and cons uc ion, and adjus ing
wo king imes (Henshe , 2023; Kaźmie czak e al., 2022). The analysis in his epo
has ocused p ima ily on mi iga ion, and he po en ial heal h bene i s and
co-bene i s o limi ing u u e empe a u e ises and impac s. An impo an
conclusion o policy is ha he e a e conside able heal h bene i s and co-bene i s
om mi iga ion ha should be conside ed in policymaking. The b oade li e a u e
22
This ype o analysis was ou side o he scope o he cu en epo due o he da a and me hodological equi emen s.
Fo example, in assessing he heal h bene i s/co-bene i s o inc eased cycling, an assessmen is needed o a) how much
ex a cycling, b) how cycling is linked o heal h and c) wha aspec o heal h is a ec ed, and how is ha quan i ied?
Summa y, discussion and policy implica ions | 57
highligh s he impo ance o ca e ul conside a ion o mi iga ion and adap a ion
measu es, including an assessmen o he po en ial o unin ended consequences.
Fo example, pas decades o e o s o educe CO2 emissions in Eu ope wi hou
adequa e conside a ion o heal h include p omo ing diesel o e gasoline-powe ed
ehicles, and he p omo ion o biomass o esiden ial hea ing, bo h o which
esul ed in conside able emissions o heal h-damaging ai pollu an s ( an Daalen
e al., 2022). Simila conce ns can be aised o e adap a ion echnologies; o
example, Deschenes (2022) no es ha mo e a en ion needs o be de o ed o
inc easing oppo uni ies and inding solu ions o p o ec human heal h om
ex eme hea while a he same ime minimising he damages om he local and
global ex e nali ies caused by he elec ici y gene a ion necessa y o mee ing he
inc eased cooling demand ha clima e change will b ing.
None heless, he indings highligh he con inued impo ance o policy measu es
o achie e he a ge s se ou in he Clima e Ac ion Plans. In e ms o ai quali y,
o example, he ecen Clean Ai S a egy commi s o achie ing he inal WHO ACQ
alues by 2040 (Go e nmen o I eland, 2023), and a EU le el, he p oposed
e ision o he Ambien Ai Quali y Di ec i e will se in e im 2030 EU ai quali y
s anda ds, aligned mo e closely wi h WHO guidelines, and se Eu ope on a
ajec o y o achie e ze o pollu ion o ai by 2050. Policy measu es o mi iga e he
impac s o clima e change, such as deca bonising home hea ing, p omo ing ac i e
a el and ansi ioning o elec ic ehicles, will be an impo an componen o he
policy esponse, and will also ha e concomi an bene i s o popula ion heal h
( an Daalen e al., 2022).
The indings in ela ion o empe a u e e ec s on eme gency hospi al admissions
highligh he impo ance o conside ing he b oade impac s o clima e change on
he heal h sec o . The EEA no e ha imp o ing he esilience o heal hca e acili ies
ac oss Eu ope is necessa y no only due o he p essu e on hei capaci y o deli e
pa ien ca e du ing hea wa es o diagnos ics du ing ou b eaks o clima e-sensi i e
in ec ious diseases, bu also due o he ac ha hey end o be loca ed in u ban
a eas ha a e mo e p one o he ‘u ban hea island’ e ec (Kaźmie czak e al.,
2022). Many heal hca e sys ems ha e also in oduced plans o adap o clima e
change. In Oc obe 2020, he English Na ional Heal h Se ice (NHS) became he
wo ld’s i s na ional heal h sys em o commi o becoming ‘ne ze o’, pledging o
educe i s ca bon emissions o ne ze o by 2040, including in i s acili ies and
buildings.
23
The HSE has now ollowed sui . In June 2023, he HSE launched i s
Clima e Ac ion S a egy 2023–2050.
24
This is a heal h se ice-wide s a egy ha
aims o educe he impac s o clima e change on he heal h se ice and deli e
heal hca e in a mo e en i onmen al and socially sus ainable manne . A key goal is
o achie e ne -ze o emissions o he HSE by 2050.
23
NHS Ne Ze o Building S anda d www.england.nhs.uk/es a es/nhs-ne -ze o-building-
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Appendix | 69
APPENDIX A – DATA, VARIABLES AND SUMMARY STATISTICS
Table A.1 p o ides a summa y o he a iables used in ou analysis om he HIPE
da ase .
TABLE A.1 SUMMARY OF VARIABLES USED FROM HIPE
Va iable
No es
Coun y
All coun ies in he Republic o I eland; incl.:
No h Dublin; Sou h Dublin
Tippe a y No h; Tippe a y Sou h
Wa e o d Coun y; Wa e o d Ci y
Co k Coun y; Co k Ci y
Lime ick Coun y; Lime ick Ci y
Galway Coun y; Galway Ci y
No I eland
No ixed abode
Age (5y B acke s)
F om 0–4 o 100+
Sex
Male/Female
Public o P i a e S a us
Public/P i a e
Medical Ca d S a us
No Medical Ca d
Medical Ca d
Unknown
Ma i al S a us
Single
Ma ied
Widowed
O he (incl. sepa a ed)
Unknown
Di o ced
Ci il Pa ne
Fo me Ci il Pa ne
Su i ing Ci il Pa ne
Leng h o S ay
Measu ed in days, minimum alue is 0.5
Diagnosis Codes
F om p ima y diagnosis o 30 h diagnosis code
Uses ICD-10-AM
Mode o Eme gency Admission
Eme gency Depa men
AMAU-In-pa ien
O he
Unknown
AMAU Only
Local Inju y Uni
ASAU-In-pa ien
ASAU Only

Appendix | 70
Elec i e/Eme gency/Ma e ni y
Indica es whe he an admission is elec i e, eme gency o ma e ni y
Admission Mon h
Janua y–Decembe
Admission Week
Weeks 0–52
Weeks s a on he i s Sunday o each yea .
2017 has weeks numbe ed 1–53 because i s a s on a Sunday
TABLE A.2 ICD-10-AM CODES FOR DIAGNOSIS VARIABLES
Diagnosis
ICD-10-AM Codes
Ci cula o y Diseases
I00 – I99
Respi a o y Diseases
J00 – J99
Me abolic Diseases
E00 – E99
In ec ious Diseases
A00 – A99 & B00 – B99
Inju ies
T00.1 – T00.9; T01.0 – T01.4;
Appendix | 71
Figu e A.1 shows he eme gency in-pa ien hospi al admissions a e (pe 100,000
popula ion) by coun y o 2019. The admissions a e is calcula ed o e e y week
o he yea and hese weekly admission a es a e a e aged ac oss he whole yea
( o 2019). The igu e below shows he a ia ion in eme gency hospi al admissions
a es ac oss coun y o esidence. The e a e highe a es o eme gency hospi al
admissions among people who li e in he wes o I eland and in he midlands.
FIGURE A.1 EMERGENCY IN-PATIENT HOSPITAL ADMISSIONS RATE BY COUNTY (2019)
Appendix | 72
APPENDIX B – LAGGED EFFECTS OF TEMPERATURE BIN MODEL
Table B.1 shows he coe icien es ima es o he con ol a iables o he
empe a u e bin analysis.
TABLE B.1 COEFFICIENT ESTIMATES FOR TEMPERATURE BIN ANALYSIS
Hospi al
Admissions Ra e
Mean o Cha lson Co-Mo bidi y Index
0.59
(0.71)
Mean Numbe o Medical Ca d
7.02***
(1.90)
Mean Public/P i a e S a us
6.12***
(2.39)
Mean Ma ied o Cohabi ing
-1.52
(1.06)
Male – Age 0–9
-5.91
(7.88)
Male – Age 10–19
-16.88*
(8.74)
Male – Age 20–29
-12.74**
(9.27)
Male – Age 30–49
-24.01***
(8.19)
Male – Age 50–69
-13.85*
(7.75)
Male – Age 70–79
-17.51**
(7.82)
Male – Age 80–89
-11.10
(8.01)
Male – Age 90+
Re
-
Female – Age 0–9
-18.42**
(8.01)
Female – Age 10–19
-4.69
(9.29)
Female – Age 20–29
-24.11**
(10.60)
Female – Age 30–49
-18.72**
(8.55)
Female – Age 50–69
-12.55
(7.90)
Female – Age 70–79
-13.47*
(7.96)
Female – Age 80–89
-8.78
(8.02)
Female – Age 90+
-14.19
(9.12)
Appendix | 73
APPENDIX C – MEAN DEVIATION MODEL: GROUP/DIAGNOSIS
ANALYSIS AND FULL MODEL RESULTS
Table C.1 shows he esul s o he mean de ia ion model when i es ima ed by age
g oup. The age g oups a e hose ha a e used in he empe a u e bin analysis and
a e g ouped acco dingly: (1) child en (ages 0–14); (2) wo king (ages 15–64); and
(3) olde (ages 65+). The analysis shows ha he e a e signi ican e ec s o
maximum empe a u e on eme gency in-pa ien hospi alisa ions o he child en’s
age g oup and he wo king age g oup o he di e ences model. This is likely o be
a e lec ion o how he model is cons uc ed ma hema ically using a de ia ion om
he mean le el o hospi al admissions by age g oup. The olde age g oup would
ha e a ela i ely high a e age numbe o hospi al admissions, so any de ia ions
om his mean would be qui e small in p opo ion.
TABLE C.1 MEAN DEVIATION MODEL BY AGE GROUP
(1)
Child
(2)
Wo king
(3)
Re i ed
% Di e ence in Maximum
Tempe a u e
0.30***
(0.07)
0.14***
(0.05)
0.07
(0.05)
% Di e ence in Maximum
Tempe a u e2
-0.92**
(0.39)
-0.58*
(0.30)
-0.38
(0.29)
Tempe a u e Lags
Yes
Yes
Yes
Coun y Fixed E ec s
Yes
Yes
Yes
Yea T end
Yes
Yes
Yes
Mon h Fixed E ec s
Yes
Yes
Yes
Socio-demog aphic Con ols
Yes
Yes
Yes
N
3,521
3,532
3,522
R2
0.05
0.04
0.19
No e: Analysis includes p ecipi a ion con ols, including lagged p ecipi a ion o h ee weeks.
S anda d e o s in pa en heses.
* p<0.05, ** p<0.01, *** p<0.001
Table C.2 illus a es he esul s o he model when i is es ima ed o each diagnosis
g oup. The analysis shows ha o inju ies, he ela ionship be ween empe a u e
and admissions is s a is ically signi ican . P e ious li e a u e suppo s he ac ha
isky and agg essi e beha iou becomes mo e common du ing ho spells o
wea he – his could also be due o he ac ha people a e mo e likely o consume
alcohol du ing ho e wea he , inc easing he likelihood o acciden s (Hags öm,