Identifying key processes and sectors in the interaction between climate and socio-economic systems: a review toward integrating Earth–human systems

Tachiiri, Kaoru; Su, Xuanming; Matsumoto, Ken’ichi

doi:10.1186/s40645-021-00418-7

Progress in Earth and Planetary Science

Table 2 GHG emissions and GDP changes caused by each process and sector

From: Identifying key processes and sectors in the interaction between climate and socio-economic systems: a review toward integrating Earth–human systems

Section	Process/sector	CO₂/GHG emission reduction (%) (negative value means GHG emission increase)	GDP loss (%) (negative value means GDP growth)	Remarks	Importance (in terms of GHG or GDP impacts)*	Feasibility of modeling**
2.2.1	Agriculture	3 and 5^a	0.02–0.06^b, 0.13 (0.17)^c, 0.06^d	Feedback via land cover change (albedo, carbon flux, etc.)	H	E
2.2.2	Livestock	3 and 5^a	0.01^e	Feedback via land cover change and changes in livestock number	M	E
2.3	Water resources	–	0.6^d	–	M	M
2.4	SLR	0.0–0.15^f, – 0.04–0.02^g	0.32 (0.12)^c, 0.0–0.1^f, 0.0–0.03^g	Life-threatening	M	M
2.5	Natural disasters	–	Negligible^h, 0–0.2ⁱ	Life-threatening	M	M
2.6	Ecosystem services	–	0.17 (0.10) ^c1, 0.4^d1	–	M	D
2.7.1	Labor productivity	0.25–0.45^j	2.6–4.0^k, 0.5–0.9^j 1.8^l, 4.6^l, 1.0–2.4^m	–	H	E
2.7.2	Other health issues	− 0.13–0.18^g	0.10 (0.56)^c, 0.0–0.1ⁿ, − 0.08–0.07^g	For VSL^*** (mortality): 0.0–0.4%ⁿ, 0.17%/°C^c,o	M	M
2.8.1	Energy	0 (2050), –1.1 (2100)^p	0.2 (0.3)^d2, 0.0–0.2^h, 0.34 (0.03)^q	No notable aggregated impacts (supply)^r	M	M
2.8.2	Infrastructure	–	0.01–0.02^s (EU), 0.1–0.2^t (US)	-	L	M
2.8.3	Tourism and transportation	0–0.001^u	– 0.5–0.3^u	Almost no change as a whole	L	D
2.8.4	Insurance and finance	–	–	5–15% needed to rescue insolvent banks^v	H	D
2.9.1	Migration	–	–	Welfare impact	–	D
2.9.2	Conflict	–	–	Life-threatening	–	D

*High (H): > 1%, Medium (M): 0.1–1%, Low (L): < 0.1%
**Easy (E), Medium (M), Difficult (D). This is based on the authors’ judgment considering the modeling framework
***Value of statistical life
^aBajželj and Richards (2014): 5 and 3 for SRES A1 and B1 scenarios
^bFujimori et al. (2018), RCP8.5, SSP 2, 2100, CGE
^cNordhaus and Boyer (1999) output weighted (population weighted) for 2.5 °C warming, CGE; c1: settlement and ecosystem were merged in their evaluation
^dTol (2002b), 2050 (central case); d1: for ecosystem; d2: 2050 (2100): demand for heating [0.4(0.7)] + for cooling [− 0.2(− 0.4)]
^eBoone et al. (2018) divided by GDP in the IIASA SSP dataset (https://tntcat.iiasa.ac.at/SspDb/dsd)
^fBigano et al. (2008), 2050 SLR of 25 cm, CGE (range shows regional difference)
^gBosello et al. (2006), CGE (range shows regional difference)
^hTakakura et al. (2019), CGE; e1: among the scenarios (RCP-SSP)
ⁱMendelsohn et al. (2012)
^jMatsumoto (2019) BAU, 2100, CGE
^kTakakura et al. (2017) BAU, 2100, CGE
^lRoson and van der Mensbrugghe (2012). 1.8% and 4.6% for 2050 and 2100 (IAM including CGE)
^mTakakura et al. (2018): RCP8.5 with reasonable time shift (< 3 h) as adaptation
ⁿHasegawa et al. (2016a) RCP8.5/2.6, 2100, CGE
^oBosworth et al. (2017) for VSL (7 × 10 million USD/person)
^pIsaac and van Vuuren (2009): 0 (at 2050) and 0.32 (at 2100) PgC (their Fig. 10), the latter of which was divided by 28.8 PgC of RCP8.5 in place of their reference scenario (of 3.7 K warming in 2100)
^qHasegawa et al. (2016b) CGE, in 2100 with RCP8.5 (RCP2.6)
^rZhou et al. (2018a) (CGE)
^sForzieri et al. (2018), divided by GDP (http://sres.ciesin.org/final_data.html)
^tUnderwood et al. (2017), divided by GDP (https://sedac.ciesin.columbia.edu/data/set/sdp-downscaled-gdp-a1a2b1b2-1990-2100/data-download)
^uBerrittella et al. (2006), CGE (SRES A1, 2050)
^vLamperti et al. (2019), BAU, agent-based model
– No data from our review

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