Tachiiri et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-021-00418-7(2021) 8:24

REVIEW

Progress in Earth andPlanetary Science

Open Access

Identifying key processes and sectors in theinteraction between climate and socioeconomic systems: a review toward
integrating Earth–human systems
Kaoru Tachiiri1*, Xuanming Su1 and Ken’ichi Matsumoto1,2

Abstract
For the purpose of identifying the key processes and sectors involved in the interaction between Earth and socioeconomic systems, we review existing studies on those processes/sectors through which the climate impacts socioeconomic systems, which then in turn affect the climate. For each process/sector, we review the direct physical andecological impacts and, if available, the impact on the economy and greenhouse gas (GHG) emissions. Based on
this review, land sector is identified as the process with the most significant impact on GHG emissions, while laborproductivity has the largest impact on the gross domestic product (GDP). On the other hand, the energy sector,
due to the increase in the demand for cooling, will have increased GHG emissions. Water resources, sea level rise,natural disasters, ecosystem services, and diseases also show the potential to have a significant influence on GHG
emissions and GDP, although for most of these, a large effect was reported only by a limited number of studies. As
a result, more studies are required to verify their influence in terms of feedbacks to the climate. In addition,although the economic damage arising from migration and conflict is uncertain, they should be treated as
potentially damaging processes.
Keywords: Earth system model, Integrated assessment model, Socio-economic system, Climate system, Humansystem

1 Introduction
Earth system models (ESMs) are global climate
models (GCMs) that are coupled with biogeochemicalcomponents, such as the carbon cycle (Hajima et al.
2012; Kawamiya et al. 2020), and can be employed tosimulate climate change for specific greenhouse gas(GHG) emission pathways. ESMs are characterized bytransient climate sensitivity to airborne carbon, carbon sensitivity to airborne carbon, and carbon sensitivity to climate change (Friedlingstein et al. 2006),
which, in combination, reflect the transient climate* Correspondence: tachiiri@jamstec.go.jp
1
Research Institute for Global Change, Japan Agency for Marine-Earth
Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama
236-0001, Japan
Full list of author information is available at the end of the article

response to cumulative carbon emissions (TCRE;
Gregory et al. 2009). The TCRE is a crucial parameterfor carbon budget estimates that are designed to meet
specific temperature targets, such as 1.5 °C or 2 °C
set by the United Nations Paris Agreement (Rogelj
et al. 2019). When ESMs are run for future climate
projections, GHG-emission and land-use scenarios arequantified using the outputs of socio-economic
models or integrated assessment models (IAMs; e.g.,
O’Neill et al. 2016). However, for more complete
modeling, this approach may need to be modified toconsider the interaction between Earth and human(particularly socio-economic) systems.
Climate change affects a wide variety of human systems. For example, Burke et al. (2015), Carleton and© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
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(2021) 8:24

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Hsiang (2016), and Hsiang et al. (2017) have shown thatclimate change impacts many socio-economic processes,
such as human mortality rates, agricultural production,
total factor productivity (TFP), migration, and conflict.
Moreover, Diffenbaugh and Burke (2019) concluded that
global warming has increased global economic inequality,
and Moore and Diaz (2015) pointed out that warminginduced slowing of GDP growth is more significant in
low-income countries. However, Beckage et al. (2018)
raised the possibility that human behavior could have asignificant impact on climate change projections. In thisvein, Woodard et al. (2019) reported that economic carbon cycle feedbacks could be of a size comparable withand in the opposite direction of natural climate–carbon
cycle feedbacks; thus, they may consequently offset eachother. In particular, they decomposed CO2 emissions by
population, gross domestic product (GDP) per capita, energy intensity (energy use per unit GDP), and carbon intensity (CO2 emissions per unit of energy) based on the
Kaya identity (Kaya and Yokobori 1997) and found that
GDP per capita was the strongest factor of carbon emissions. GDP per capita is one of the more difficult-topredict quantities, and it has been claimed that a climatechange-produced gain in GDP per capita would reverse
the sign of Woodard et al.’s (2019) main conclusion(Caldeira and Brown 2019).
To consider the interaction between Earth and humansystems, one solution is to couple individual models ofthese systems. One such example is the simple coupledclimate–economy system model proposed by KellieSmith and Cox (2011), which suggested that historicallyobserved economic growth rates and decarbonization
can lead the climate–economy system to experiencedamaging oscillations. When coupling more complicatedmodels (e.g., an ESM and an IAM), van Vuuren et al.
(2012) concluded that the most appropriate coupling approach depends on the situation under analysis and full
coupling may not always be the most desirable approach.
Indeed, coupling ESMs and IAMs is not an easy task because it requires matching spatial and temporal resolution (Chou et al. 2018), and differences between IAMs’
focus on land use and ESMs’ focus on land cover should
be carefully accounted for (Bond-Lamberty et al. 2014;
Robinson et al. 2018). Improving ESMs in terms of theirsimulations of agriculture and other types of land
management is also important (McDermid et al. 2017;
Pongratz et al. 2018).
Collins et al. (2015) developed an integrated Earth system and socio-economic model. Previously, Di Vittorio
et al. (2014) suggested that using this type of model
would moderate the gap between land use in IAMs andland cover in ESMs. Using the same model as Collins
et al. (2015), Jones et al. (2018) showed that the inclusion of human-driven responses in an ESM altered bothterrestrial concentration–carbon and climate–carbon
feedbacks to increased carbon storage. Other similar
models have also been proposed (Sokolov et al. 2005;
Yang et al. 2015, 2016; Monier et al. 2018), including amodel using an ESM of intermediate complexity (EMIC)
instead of a state-of-the-art ESM (Mercure et al. 2018).
However, Calvin and Bond-Lamberty (2018) concluded
that more research and models were needed to robustly
quantify the sign and magnitude of human–Earth system
feedbacks.
This study aims to identify the key processes involvedin the feedbacks from socio-economic systems to the climate. Given that the most important feedbacks fromsocio-economic systems to the climate occur via changes
in GHG emissions, which in many cases occur via achange in economic growth, we evaluate the impact ofthese feedbacks in terms of the impact on the economy
as a whole. It should be noted that there are other feedback processes linking socio-economic systems to theclimate, which are discussed later.
In Section 2, we review previous studies on individual
processes and sectors through which the climate affects
human or socio-economic systems, particularly in termsof physical and ecological effects. These processes wereselected from previous lists of processes/sectors used for
impact assessment (e.g., IPCC 2014; Yokohata et al.
2019) based on the impact on the economy as a whole.
This section focuses on presenting the results of relatively recent studies, particularly those that havefollowed the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), although it
also refers to some important older studies. Section 3
discusses these findings, and Section 4 provides aconclusion.

Tachiiri et al. Progress in Earth and Planetary Science

2 Feedback processes from climate to socioeconomic systems
In this section, after the impact on the whole economy,
we discuss the impacts of climate change on eight key
processes: land productivity (i.e., cropland and pasture),water resources, sea level rise (SLR) and inundation, natural disasters, other ecosystem services, human health(i.e., labor productivity and disease/health), industry andrelated economic activities (i.e., energy, infrastructure,
tourism and transportation, insurance, and finance), andmigration and civil/international conflict. Each subsection describes the physical and/or ecological effects ofclimate change on the process and the economic and
GHG emission effects if available. In the end, we refer toadditional topical issues and adaptation if available.
Many studies referred to here use Representative Concentration Pathways (RCPs), but some studies use the Special
Report on Emissions Scenarios (SRES) scenarios; see
Supplementary Information (SI) for their comparison. In

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Table 1 Impact of climate change on GDP
Study

GDP
impactClimatechangescenario

Model

Note

Stern (2007)

5–20%

BAU

Formal economic models

The costs of mitigation can be limited to around 1% of global
GDP each year. There has been criticism on
the method of estimating damage and selection of parametervalues, including the utility discount rate (see, for example, Cole 2008).

Burke et al.
(2015)

23%

RCP8.5(2100)–

SSP*5

Takakura et al.
(2019)

6.6% (3.9–
8.6%)

RCP8.5(2100)

Five ESMs

SSP3, strict mitigation could limit the impact to less than 1%

Nordhaus (1994)

0.60%

2 °C
increase

DICE

The reference reports a damage function with which we
calculated the GDP impacts of 2 °C warming.

Nordhaus andSztorc (2013),
Nordhaus (2014)

1.10%

2°C
increase

DICE

The reference reports a damage function with which we
calculated the GDP impacts of 2 °C warming.

Tol (2018)

0.1–3.0%,
0.9–5.1%,
6.1–6.7%

2.5 °C, 2.9–
3.2 °C, 5.4–
6.0 °C

Summarizing 27 studies on the –impact of the climate on theeconomyaa

In terms of welfare-equivalent income, *Shared Socio-economic Pathway

SI, the basic information of the studies cited are also presented as tables.
2.1 Whole economy

Stern (2007) estimated that for the business-as-usual(BAU) case, the overall costs and risks of climate change
will be equivalent to losing 5–20% of GDP, while thecosts of mitigation can be limited to around 1% of global
GDP each year. In response to this, there has been criticism on the method of estimating damage and selectionof parameter values, including the utility discount rate(see, for example, Cole 2008). We should also note thatthe impact on the economy strongly depends on the scenario and the level of warming. The GDP impacts reported in existing studies are summarized in Table 1. In
addition, Tol (2018) showed that warming of up to 1 °C
had a positive effect on the economy, which is not dramatically different from Ueckerdt et al. (2019), who concluded that 1.9–2.0 °C was the economically optimalwarming point when considering mitigation and adaptation costs. That is, slight warming could have a positive
impact on the economy.
Meanwhile, Roson and van der Mensbrugghe (2012)concluded that changes in GHG emissions were nonnegligible; for example, they found that climate change
impacts would lead to a 4.7% reduction of CO2 emissions worldwide by 2100 in addition to declines in methane (8.4%) and N2O (7.8%) emissions1.
2.2 Land productivity

By altering temperature, precipitation, and radiation patterns that influence plant productivity, climate change
1

For the BAU scenario.

affects cropland and pasture yields, which are vital tomeet the demand for food. Sometimes, the effect of increasing the CO2 concentration, including the so-called
CO2 fertilization and change in stomatal conductance, is
excluded from the impact of climate change, but we include that impact, as climate change is always associatedwith a CO2 concentration increase.
2.2.1 Cropland

For the past, Iizumi et al. (2018) concluded that climatechange decreased the mean global yields of maize,
wheat, and soybeans in 1981–2010 by 4.1%, 1.8%, and
4.5%, respectively, relative to when the preindustrial climate is input. Regionally, Matsumoto and Takagi (2017)
found that an inverted U-shaped curve describes the relationship between temperature and rice production andalso reported that, when climate change is large, production decreases in many cities, while the impact is relatively low in high-latitude regions.
For the future change, a comparison of multiple global
gridded crop models revealed strong negative effects ofclimate change on crop (wheat, rice, maize, soybean)
yields, particularly with high levels of warming at low
latitudes and for models that explicitly incorporated nitrogen effects (Rosenzweig et al. 2014). In another study,
it was observed for the same four crops that maize andsoybean crop yields peaked for relatively slight warming(of 2–3 °C), while rice yield kept increasing for greater
warming (Iizumi et al. 2017)2. Under the SRES A1B scenario in 2050, changes in yields of spring wheat,2

In their study, wheat crops were negatively affected in low-income(which are often located at low latitudes) but not high-income countries (which are often located at mid and high latitudes).

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soybean, and maize, relative to the case in the absence ofclimate change, are projected to be + 1%, − 13%, and −22%, respectively (Arnell et al. 2016a). Global (tuber) potato yields are also projected to decline by 2–6% by2055, with larger declines expected by 2085 (2–26%) depending on the RCP scenarios (Raymundo et al. 2018).
Consistently, 12–53% (adaptation of sowing date andthermal time requirements to give highest yields) and −2–27% (a more conservative adaptation of sowing date
and thermal time requirements) increases are reported
for the European Union (EU) for three SRES scenarios(A1B/B1/B2), and technological progress could evenraise these numbers by 17–51% (Zimmermann et al.
2017). Another study on the EU found that, with CO2
fertilization, crop yield does not decline (RCP8.5, 2030),
with the exception of maize (Blanco et al. 2017).
In the full adaptation case3, it has been estimated thatthe change in gross agricultural product will be around+ 0.06% in 2100, and that it will remain positive until
the 2190s (Tol 2002b). Tol (2002b) also reported thateach region has its own optimal warming for the agricultural sector, ranging from 0.45 °C to 3.41 °C (dependingon the region).
In terms of impacts on GDP, Fujimori et al. (2018)concluded that the negative impact of changes in crop
yield on GDP would be 0.02–0.06% (globally, the first
and third quartiles) in 2100 even for RCP8.5, and thesocio-economic assumption (choice of Shared Socioeconomic Pathway [SSP]) had an impact one order larger. A change in crop productivity could affect croplandarea and crop price (Thornton et al. 2017; Calvin et al.
2019).
For the impact on total GHG emissions (including
pastoralism) by 2050, 0.8 and 0.5 PgCO2 increases, relative to the no climate change case, are estimated for
SRES A1 and B1 scenarios, respectively (Bajželj andRichards 2014), which is equivalent to 5 and 3% of total
emissions for the two scenarios. The impact of warming
on agriculture estimated by Takakura et al. (2019) isslightly positive for RCP2.6, 4.5, and 6.0 but negative for
RCP8.5, while Nordhaus and Boyer (1999) estimated thedamage for 2.5 °C warming as 0.06% of GDP (output
weighted).
For additional information, Reilly et al. (2007) argued
that climate change and increases in CO2 levels will generally have a positive effect, but ozone damage could offset these benefits if it is not strongly controlled.
Attention also needs to be paid to subsistence and smallholder farmers, predominantly those in developing

countries, which are vulnerable to climate change (Morton 2007).
Additionally, the adaptation cost needed to offset thenegative effect of climate change is estimated to bearound 7 billion US dollars (USD) per year, including
mainly regional target changes (e.g., irrigation efficiency,
rural roads, and agricultural research) (Nelson et al.
2009). Smit and Skinner (2002) classified the agricultural
adaptation options into four categories: (1) technologicaldevelopments, (2) government programs and insurance,(3) farm production practices, and (4) farm financial
management. They include crop development, weather
and climate information systems, and resource management innovations (1); agricultural subsidy and support
programs, private insurance, and resource management
programs (2); farm production, land use, land topography, irrigation, and timing of operations (3); and crop
insurance, crop shares and futures, income stabilization
programs, and household income (4).

3

4Tachiiri et al. Progress in Earth and Planetary Science

It assumes substantial changes to agricultural systems withinvestment in regional/national agricultural infrastructure and policychanges, including large shifts in planting dates, an increase in fertilizer
requirements, the installation of irrigation systems, and thedevelopment of new crop varieties (Rosenzweig and Parry 1994).

2.2.2 Pasture and livestock

Climate change is a threat to livestock production due
to its influence on feed crop and forage quality, water
availability, animal and milk production, livestock diseases, animal reproduction, and biodiversity (RojasDowning et al. 2017). In addition, the variety of livestock
systems in operation globally complicates the discussion
surrounding the effects of climate change (Lopez-iGelats 2014; Rivera-Ferre et al. 2016). Inter-model uncertainty (e.g., what Chen et al. (2018) demonstrated regarding carbon sequestration from grazing activity) also
remains a problem.
For RCP8.5 in 2050, herbaceous net primary production (NPP) would increase slightly by an average of 3
gC/m2/year, although overall NPP is expected to decrease (Boone et al. 2018). It was also pointed out that
greater precipitation variability, which is a likely consequence of global warming, could have a negative impact
on vegetation recovery (Martin et al. 2014). In particular,
it was suggested that desertification could have a negative impact (Nardone et al. 2010). For economic impact,globally, under RCP8.5, it is projected that there will be
a 7.5–9.6%4 decline in grazing livestock as a proportionof total stocking in rangelands and an economic loss of
9.7–12.64 billion USD by 2050 (Boone et al. 2018).
In addition, many studies (e.g., Olwoch et al. 2008;
Gray et al. 2009; Wilson and Mellor 2009) have reported
a positive relationship between temperature and the expansion of the geographical range of arthropod vectors
For 7 GCMs
For example, the study by Bett et al. (2017) on tsetse flies, whichtransmit a range of trypanosome parasites in sub-Saharan Africa. The
same study also found that extreme events such as drought promote
livestock diseases.

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for livestock diseases, but other research has found the
opposite5.
For adaptation, as the impact of climate change isknown to be region-dependent, it is pointed out thatlocking pastoral societies into specified development
pathways could be maladaptive (Herrero et al. 2016a).
The following are mentioned as adaptation measures:
modifying production and management systems (e.g., diversification of livestock animals, application of agroforestry), changing breeding strategies, and improving
farmers’ perceptions and adaptive capacity (Rojas-Downing et al. 2017). As mitigation measures, the same study
mentioned performing carbon sequestration through decreasing deforestation rates and/or improving animaland herd efficiency, reducing enteric fermentation
through practices such as improvement of animal nutrition and genetics, improving manure management andfertilizer management, and shifting human dietary
trends.

Arnell (2016) reported 37% and 53% population exposure to water scarcity without climate change impact with
WCI < 1,000 m3/capita/year and WSI > 0.4, respectively7. In their multi-model study, Schewe et al. (2014),
using WCI for RCP8.5, projected that, compared with apopulation-growth-only scenario, global warming of 2 °C
above current temperatures would lead to a 15% increase in the global population facing a severe reductionin access to water resources. They also projected thatthe number of people experiencing absolute water scarcity (defined as < 500 m3 per capita annually) would increase by an additional 40%. All of the baseline anddifferent mitigation scenarios considered in the paper resulted in 51–57%8 global population exposure to water
scarcity in 2050 (Hejazi et al. 2014 using WSI), while
Hanasaki et al. (2013) using WSI suggested smaller percentages of 25–28%9 for RCP2.6, 4.5, and 8.5. Arnell and
Lloyd-Hughes (2014) using WCI estimated that under
RCP2.6, exposure to a higher frequency of flooding
would be reduced by around 16% in 2050, and exposure
to greater water scarcity would be reduced by 22–24%
compared with RCP8.5. The number of people exposed
to water resource stress for the SRES A2 scenario is projected to be 10–20% larger than that for the A1B and B2
scenarios (Arnell et al. 2016b).
At least on a regional scale, mitigation and adaptation
policies could increase water scarcity. Hejazi et al. (2015)concluded that twenty-first-century United States (US)emissions mitigation could increase water stress (e.g.,
irrigation for biocrops), and Haddeland et al. (2014) indicated that the water scarcity due to irrigation was significant in parts of southern and eastern Asia in 1970–
2000, and it is expected to become even larger in the
future.
For economic impact, the water resource damage for 1°C warming is estimated to be 84 billion USD (Tol
2002a). Under Tol’s (2002b) scenario10, in 2100, this willcause a damage corresponding to around 0.6%11of theworld GDP. The adaptation measures fall into two areas,
management (socio-economic context) and technical solutions, and they are then categorized as follows: wider
scope of management, adequate governance and strong
institutions, intensification of demand management andsupply enhancement, system interconnection andoptimal operation, revisiting water rights and water

Tachiiri et al. Progress in Earth and Planetary Science

2.3 Water resources

On average, precipitation is increased by global warming. However, the spatial heterogeneity of the rainfallchange and increased demand for water (mainly due toincreasing populations and economic growth) can cause
water scarcity in some regions (this is already evident,
and it will further increase in the future).
Two indicators are widely used for assessing water
scarcity: water crowding index (WCI)6 and water stressindex (WSI)6.
For the past, using WSI, it is estimated that 800 million people or 27% of the global population were living
under water-stressed conditions in 1960, and this number eventually increased to 2.6 billion or 43% by 2000
(Wada et al. 2011). This number was slightly lowered by
Gosling and Arnell (2016) who, using 21 GCMs, estimated that between 1.6 (based on WCI) and 2.4 (WSI)
billion people, respectively, were experiencing water
scarcity in 2000. However, the main reason for that is
considered the increase of human water demand rather
than climate change.
For the future, the global population exposed to water
shortages (i.e., the situation in which water stress is acknowledged to be a factor limiting development) is projected to further increase significantly. Gosling and5

For example, the study by Bett et al. (2017) on tsetse flies, whichtransmit a range of trypanosome parasites in sub-Saharan Africa. The
same study also found that extreme events such as drought promote
livestock diseases.
6
WCI is an index of the annual water resources per capita in awatershed, and a WCI threshold of < 1,000 m3/capita/year is often
used as an indicator of water scarcity. WSI is an index of the ratio ofwater withdrawals to resources, and a WSI of > 0.4 is used to indicate
water scarcity. In many cases, WSI > 0.4 results in larger (by 30–50%)
population exposure than a WCI threshold of < 1,000 m3/capita/year.

7

For the future population of the SRES A1B storyline represented by
an IAM.
8
It depends on the radiative forcing target and tax regime (see Table 4of Hejazi et al. 2014).
9
These values vary depending on the RCP scenario.
10
In Tol (2002b), the scenario used for that study is not described. We
assumed that the author used the FUND standard scenario, which isrelatively similar to the SRES/B2 scenario (Warren et al. 2006).
11
Central value

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allocation procedures, and early warning and risk management (of drought) (Garrote 2017). For the Maldives,the desalination of water has been mentioned as a
“hard”12 adaptation measure (Sovacool 2012).

emission changes (Bigano et al. 2008); in particular,
the impact of SLR on the tourism sector was considerable. Wetland loss for 1 m SLR was projected to
be 170,000 km2, and the protection cost will be 1.06
trillion USD (Tol 2002a).
It was identified that SLR is a particular risk forrice production in Bangladesh, Japan, Taiwan, Egypt,
Myanmar, and Vietnam. Using a global rice trademodel with an SLR of 0–5 m, it was found that global rice production would fall by 1.60% for the no
SLR case and 1.81–2.73% for 1–5 m SLR and that
global rice prices would rise by 7.14% and 8.12–
12.77%, respectively (Chen et al. 2011).
The global total damage cost by SLR is estimated toreach 200/1000/2000 billion USD for 0.5 m/1 m/2 m
SLR, respectively, and for a 0.5-m rise, it is shownthat protection (of developed coastal areas) and wetland damage are the two main factors (others considered are dryland damage and mitigation cost)(Anthoff et al. 2010). For a 1-m rise, the Federal
States of Micronesia have > 5% of GDP damage, but
by protection, 500 billion USD can be saved in 2100
(Anthoff et al. 2010). The inundated area is estimatedto be 370,000 (RCP2.6)–420,000 km2 (RCP8.5), andthe affected population is expected to range from55.3 million (RCP2.6, SSP1) to 106 million (RCP8.5,
SSP3) for 2100 (Tamura et al. 2019). Nordhaus andBoyer (1999) estimated the damage for 2.5 °C warming as 0.32% (output weighted) or 0.12% (population
weighted) of GDP, indicating that high-income regions have large damages.
For the US, it was found that incorporating sitespecific episodic storm surges doubled national damage estimates relative to SLR-only estimates (Neumann et al. 2014). At the city level, Hallegatte et al.
(2011) estimated sector-by-sector losses due to an
SLR of 2 m above the current sea level for
Copenhagen, Denmark in the absence of any protective measures. Transport, postal services, telecommunications, public and personal services, and the housing
sector were predicted to experience the greatest direct
losses (> 800 million EUR), followed by manufacturing, electricity, gas and water supply, and finance andbusiness activities (400–600 million EUR).
For adaptation, it is suggested that dikes of 1 m inheight may reduce the total inundated area by approximately 40% below the no-adaptation baseline under thesame RCP scenarios through 2020–2100 (Tamura et al.
2019). The adaptation cost is estimated to be 10 and 50
billion USD in 2050 for new dikes and updated dikes,
and for 2100, the cost will be 100–200 (new dikes) and
30–50 billion USD (updated dikes). The benefit–costratio is ≤ 1 for new dikes and > 1 for upgraded dikes forboth RCP2.6 and 8.5 (Tamura et al. 2019).

Tachiiri et al. Progress in Earth and Planetary Science

2.4 SLR and inundation

Global warming causes additional ocean heat uptake,which causes thermal expansion of the ocean water andthe melting of ice and snow, leading to freshwater inflow
from land to oceans, both of which contribute to SLR.
SLR inundates coastal areas, negatively affecting the
local people’s lives and economic activities. Relative SLR
has a range of potential impacts, including higher extreme sea levels (and associated flooding), coastal erosion, the salinization of surface water and groundwater,
and the degradation of coastal habitats such as wetlands(Nicholls 2011).
The satellite observations in 1993–2009 showed that
SLR was spatially varying. In the western tropical Pacific
Ocean, Southern Ocean, and part of the North Atlantic
Ocean, SLR (around 12 mm/year) has increased 3–4
times faster than the global mean (3.2 mm/year) (Meyssignac et al. 2012). For RCP4.5 in 2081–2100 (relative to
1986–2005), SLR of 0.5–0.613 m is projected in thenorthern tropical ocean and southern Pacific Ocean, and
0.4–0.613 m is projected for other small island regions(Nurse et al. 2014). These figures indicate the severe future condition for the Pacific small island countries.
Under the SRES A1B scenario in 2050, the loss ofcoastal wetland area and the additional number ofpeople affected by coastal flooding compared with a
non-climate change case14 are projected to be 15% and
1.3 million, respectively (Arnell et al. 2016a).
For economic impact, a study using a static computable general equilibrium (CGE) model found that
SLR could have a significant impact on agriculture
in 2050 when no protection is applied (Bosello et al.
2007). In that case, a slight GDP loss (0.0–0.1% foreight regions) and small change in CO2 emissions (−0.02 to + 0.04% for the same regions) was estimated,
and for the total protection scenario, those numbers
were − 0.10 to + 0.02% (GDP loss) and − 0.34 to +0.07% (CO2 emissions). A 25-cm SLR in 2050 resultsin 0–0.2% (among 16 regions) of GDP direct cost,
0.00–0.11% GDP loss, and − 0.15 to + 0.03% CO2
12Hard adaptation measures typically employ capital-intensive, large,
complex, and inflexible technology and infrastructure, whereas soft
adaptation measures prioritize natural capital, community control,
simplicity, and appropriateness (Sovacool 2011). Sovacool (2012) reported one hard and one soft measure for water scarcity and otherproblems (e.g., SLR, tidal inundation; see also the end of Section 2.9.1).
13
The range appears to be for different regions.
14
For HadCM3 model (with 3.5 K equilibrium climate sensitivity;
Hunter et al. 2019).

Tachiiri et al. Progress in Earth and Planetary Science(2021) 8:24

2.5 Natural disasters

A warm atmosphere can hold more water (by about 7% per
1 °C), which causes more rainfall. A change in the spatial
pattern of rainfall distribution has been observed, with dry
areas (generally throughout the subtropics) becoming drier
and wet areas becoming wetter (Trenberth 2008), whiledeveloping countries and smaller economies are more vulnerable to natural disasters (Noy 2009). Although Bouwer(2011) concluded that anthropogenic climate change todate has not had a significant impact on losses from natural
disasters, all projections indicated that there would be anincrease in extreme weather losses due to climate change.
Until 2040, however, greater exposure to natural disasters due to population growth and the higher capitalvalue at stake are likely to have an equal or larger effecton disaster losses than anthropogenic climate change(Bouwer 2013). In particular, for tropical cyclones, a review article concluded that a future projection indicatesa 2–11% increase in intensity by 2100, with a 6–34% decrease in frequency (with a 20% increase in the most intense cyclones) (Knutson et al. 2010). For RCP2.6 and
8.5, climate change by 2050 would increase exposure toriver flood risk for 93–530 and 100–580 million15
people, respectively (Arnell and Lloyd-Hughes 2014). In
an analysis under the SRES A1B scenario, Arnell and
Gosling (2016) concluded that, in 2050, 31–450 million
people and 59,000–430,00016 km2 of cropland would be
exposed to the risk of river flooding with doubled frequency. Even without climate change, tropical cyclone
damage will be doubled (56 billion USD/year, equivalent
to 0.01% of GDP from the current 26 USD billion/year,
equivalent to 0.04% of GDP) likely due to future increases in income, and climate change damage doubles
that (53 billion/year). That damage was concentrated in
North America, East Asia, and the Caribbean–Central
America (0.0–0.2% as GDP loss rate, Mendelsohn et al.
2012). Gariano and Guzzetti (2016) presented a map displaying projected future occurrences of four landslide
types, suggesting that the frequency of debris flow andshallow landslides would increase in many parts of theworld. Cloutier et al. (2016) reported that in Canada,
warming would impact permafrost, glaciers, and ice
caps, leading to landslides. In a CGE-based analysis, theimpact of fluvial floods on GDP was negligible (Takakura et al. 2019).
For adaptation, many studies (e.g., Mercer 2010) haveemphasized the importance of integrating disaster risk
reduction and climate change adaptation, and Birkmann
and von Teichman (2010) concluded that the main barriers to this can be grouped into three categories: (1)spatial and temporal scales, (2) norm systems (e.g.,
15
16

For 19 GCMs.
For 21 GCMs.

Page 7 of 23

legislative, cultural, or behavioral), and (3) knowledge
types and sources. For drought in the Sahel, considering
the uncertainty in future climate change, Mortimore(2010) emphasized that the adaptation policies should
aim to build on the platform of past achievements andexisting local knowledge to enable flexibility and diversity and the protection of assets of small-scale farmers
and herders.

2.6 Ecosystem services

Climate change can alter the living environments ofmany creatures, influencing their existence, which inturn impacts the profit they give us. The functions of
ecosystem services can be categorized into four types:
supporting (e.g., primary production, nutrient cycling),
provisioning (e.g., food, water), regulating (e.g., climate,water, disease), and cultural (e.g., recreation, education)(Millennium Ecosystem Assessment 2005). The Economics of Ecosystems and Biodiversity (TEEB) (2009)
mentioned three climate-related issues: coral reef, forest
carbon, and investment for climate adaptation. Moreover, Staudinger et al. (2012) for the US identified climate change as having an effect on marine fishery yields,
nature-dependent tourism, hazard reduction, and carbon
storage/sequestration. Similarly, Grimm et al. (2013) reported that the loss of sea ice, rapid warming, and higherorganic inputs affect marine and lake productivity, while
the combined influence of wildfires and insect outbreaks
reduces forest productivity, mostly in arid and semi-arid
western regions of the US. They also reported that ecosystem feedbacks, especially those associated with the release of CO2 and CH4 from wetlands and the thawing of
permafrost soils, accelerate the rate of climate change.
Shaw et al. (2011) examined how two important ecosystem services in California, carbon sequestration andrain-fed forage production for livestock, were affected byclimate change in terms of their distribution and production levels. Based on various climate change scenarios, they predicted that the availability and value of theseecosystem services would decline under most GHG
trajectories.
Traditional healthcare systems, which rely on medicinal plants, are also likely to be affected by climatechange. In the Himalayan region, persistent climatic
variability would alter the habitats of medicinal plants(Maikhuri et al. 2017)17. The total ecosystem value at2005 is evaluated as 1.3 trillion international dollars, and
20–50% of that will experience the climate categorytransition by 2100 (Watson et al. 2020).
17

Changes in the phenophases of medicinal plants would require an
adjustment to the collection period for these plants, which would goagainst tradition, while other species would need to be used as
substitutes in traditional healthcare systems.

(2021) 8:24

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For the future, as an example of hazard reduction, it
was suggested that GHG mitigation (to 3.7 W/m2 target)
had potential ecosystem service benefits, on determining
the wildfire pattern, of 3.5 billion USD on average in2005 dollars18 for 2013–2115 (Lee et al. 2015).
It was concluded that in 2070–2099 (with RCP8.5), forsouthern California, water runoff (+ 127% to − 60%)19
and carbon storage (+ 52% to − 31%)19 were likely to be
most significantly affected. Moreover, one-third of highbiodiversity areas were threatened by potential water
deficits, and the annual costs of sediment removal were
estimated to be around 172 million USD and carbon
storage was estimated to be worth 7.5 billion USD(Underwood et al. 2019). For a Mediterranean river
basin, it was reported that the availability of drinking
water was expected to decrease by 3–49%20, while totalhydropower production would decrease by 5–43%20. In
addition, erosion control was estimated to be 23% lower,
indicating that the costs required for dredging reservoirs
and treating drinking water would also increase (Bangash et al. 2013). Impacts of 1 °C warming and CO2
fertilization are projected to be 439 million and − 50
million USD for forestry and natural ecosystems, respectively (Tol 2002a). The cost of loss of species, ecosystems, landscapes, and so on is estimated to be around
0.04% of GDP in 2100 (Tol 2002b).
For the impact of adaptation on ecosystem services,not only the complex interactions between different ecosystem processes but also tradeoffs between ecosystemservices, including the positive effects of increasing temperatures on food and timber production and the longer
growing season and the negative effects of more frequent
fungal disease and insect outbreaks, were found in cool
Finnish regions (Forsius et al. 2013). Recently,
ecosystem-based adaptation (EbA), defined as “the useof biodiversity and ecosystem services to help people
adapt to the adverse effects of climate change” (SCBD2009), is advocated and attempted here and there (e.g.,
Huq et al. 2017; Newsham et al. 2018).

reduced labor capacity to an estimated 90% during peakmonths over the past few decades (Dunne et al. 2013),
with the impact greater for those working outdoors (e.g.,
agricultural workers) than those working indoors (e.g.,
office workers; Kjellstrom et al. 2009b). A case study on
the steel industry in southern India found that the productivity loss of workers exposed to wet-bulb globe temperatures (WBGTs) above the threshold wassignificantly higher among workers with direct rather
than indirect heat exposure (Krishnamurthy et al. 2017).
In construction projects in China, it was found that direct working time declined by 0.57% and idle time rose
by 0.74% when the WBGT was increased by 1 °C (Liet al. 2016). Using an econometric approach, Zhang
et al. (2018) also estimated the effects of temperature on
firm-level TFP, factor inputs, and outputs in Chinese
manufacturing plants for 1998–2017 and predicted thatin the absence of additional adaptation measures, Chinese manufacturing output would fall by 12% by the middle of the twenty-first century.
For the future, Coffel et al. (2017) estimated that exposure to WBGTs higher than those of recent heatwaves
may increase five- to tenfold by 2070–2080 under
RCP4.5 and RCP8.5, and under RCP8.5, exposure to
WBGTs over 35 °C could amount to over one million
person-days per year by 2080. Matthews et al. (2017) investigated the relationship between heat stress, measured
by the heat index (see Anderson et al. 2013) andtemperature and found that the relationship is nonlinear.
They also suggested that under a middle-range population growth scenario with global warming of only 1.5 °C,
double the number of megacities could become heatstressed by 2050, meaning that 350 million more people
would be faced with dangerous heat conditions. Kjellstrom et al. (2009b), the earliest study to investigate this
topic on a global scale, investigated the relationship between work capacity and climate conditions (measured
by WBGT) and estimated the future loss of work capacity caused by climate change for various work intensities. They showed that the greatest absolute losses ofwork capacity would be in Southeast Asia, Latin andCentral America, and the Caribbean (11–27%) for the
SRES A2 scenario.
Dunne et al. (2013) also predicted that labor capacity
would reduce further to 80% during peak months by
2050 and to less than 40% by 2200 under RCP8.5, withmost tropical and mid-latitude zones experiencing
extreme heat stress. Under 2 °C target scenarios, thisreduction was likely to be smaller, but the labor capacity
would still be lower than historical levels. Kjellstrom
et al. (2016) found that 30–40% of work capacity during
the daylight working hours would be lost by 2085 insome areas for RCP8.5. The outdoor labor capacity during the 2090s would be 0.54 under the highest emission

Tachiiri et al. Progress in Earth and Planetary Science

2.7 Human health

Human health can also be affected by climate change.
Here, we focus on the impact on labor productivity due
to heat stress, the spread of disease, and other healthrelated issues.
2.7.1 Decrease in labor productivity

Hot working environments affect workers (Tawatsupa
et al. 2013; Xiang et al. 2014); heat stress has already18

Assuming a 3% discount rate.
Depending on GCM.
20
Depending on scenario (SRES/A2 or B1) and period (2001–30,
2031–2070, 2071–2100).
19

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scenario, and the workday would have to be shifted to
5.7 hours earlier to maintain the labor capacity of thebase-year level (Takakura et al. 2018). Reducing work intensity or increasing the frequency of short breaks are
effective measures for the prevention of heat-related
effects (Kjellstrom et al. 2009b; National Institute for
Occupational Safety and Health 2016). However, suchinterventions reduce work hours and labor productivity(Kjellstrom et al. 2009a; Dunne et al. 2013; SuzukiParker and Kusaka 2016; Donadelli et al. 2017; Takakura
et al. 2017) and result in economic losses (Roson andSartori 2016; Donadelli et al. 2017; Takakura et al. 2017;
Rezai et al. 2018; Zhang et al. 2018). Many studies havereported an impact of temperature and climate change
on socio-economic activities via changes in labor productivity (Kjellstrom et al. 2009b; Hsiang 2010; Roson andvan der Mensbrugghe 2012; Dunne et al. 2013; Kjellstrom et al. 2016; Roson and Sartori 2016; Suzuki-Parker
and Kusaka 2016; Takakura et al. 2017, 2018; Matsumoto 2019).
Using a CGE model with a BAU scenario, Roson andvan der Mensbrugghe (2012) projected GDP losses of up
to 6% from the decrease in labor productivity in mostregions except Europe, which exhibited an increase in
GDP by < 1%. In similar research, Roson and Sartori(2016) found lowered labor productivity for the agricultural sector, which faced a mean variation of − 2.52 to −17.48% (for 1–5 °C warming), than the manufacturing
and service sectors.
Using a CGE model, Takakura et al. (2017) found that
total worldwide GDP losses in 2100 would be around
2.6–4.0%21 for no-mitigation scenarios but that theselosses would be less than 0.5% if the 2 °C climate changetarget was achieved22. Even with the introduction of
additional adaptation measures, GDP was still projected
to suffer severe losses under high-emission scenarios(e.g., 1.0–2.4%23 under the RCP8.5 scenario) (Takakura
et al. 2018). Another CGE study found that total GDP
would fall by 0.5–0.9%24 globally in 2100 under the BAU
scenario, although this impact of climate change would
differ by region (from + 0.16 to – 5.92%). Moreover, theimpact on CO2 emissions was predicted to be smaller
than the impact on GDP (– 0.25 to – 0.45%)24, and thaton temperature was slight (Matsumoto 2019). As another example, using an IAM (including CGE), Rosonand van der Mensbrugghe (2012) reported 1.8% and4.6% GDP loss for 2050 and 2100, respectively due tothe change in labor productivity.
Day et al. (2019) summarized the adaptation options
into three categories: technical (e.g., air conditioning,
ventilation, shading), infrastructural (climate-smart society, economic shift, early warning, education, monitoring), and behavioral (work choice, time shift, individual
heat-reducing activities) adaptation measures.

Tachiiri et al. Progress in Earth and Planetary Science21

For RCP8.5 with SSPs 1–3.
Takakura et al. (2017) also reported that the relationship between
GDP losses and the rise in global average temperature was roughly
linear.
23
Among four GCMs.
24
Considering the uncertainty in climate parameters (17th, 50th, and83rd percentiles are used).
22

2.7.2 Diseases and other health issues

Climate change has been identified as potentially themost significant global health threat of the twenty-first
century (Costello 2009), and it is expected to increase
health risks due to increases in the frequency and magnitude of extreme weather events, decreases in air andwater quality, and a rise in food-borne, vector-borne,
and zoonotic diseases (Séguin 2008; Berry et al. 2014).
For example, Ebi et al. (2017) presented a range ofstrategies for the detection and attribution analysis of
the health impacts of climate change. They discussed
three case studies (i.e., heatwave-related deaths, theemergence of Lyme disease in Canada, and Vibrio emergence in the Baltic Sea) and concluded that a proportionof climate-sensitive health outcomes can be attributed toclimate change. For mosquito-borne diseases, Mordecai
et al. (2019) analyzed 11 pathogens transmitted by 15
different mosquito species, including globally importantdiseases such as malaria, dengue, and Zika, and foundthat transmission varied strongly and unimodally withtemperature, peaking at 23–29 °C and declining to zeroat 9–23 °C and 32–38 °C. These effects of temperature
on transmission varied between mosquito and parasite
species; for example, of the tropical pathogens investigated, malaria and Ross River virus had the lowest thermal optima (25–26 °C), while dengue and Zika viruses
had the highest (29 °C).
For future change, Honda et al. (2014) evaluated theheat-related excess mortality arising from climate change
under the SRES A1B scenario using a distributed lag
nonlinear model. They found that Asia was most vulnerable and that, in some regions, heat-related excess
deaths would account for 0.6% of all deaths. Healthcare
facilities are thus vital to the management of climatechange-induced health impacts. The increase in the frequency and magnitude of extreme weather events causedby climate change is expected to negatively impact
healthcare facilities (Paterson et al. 2014).
Because of climate change, vector-borne diseases canspread to new areas. However, even though initial projections suggested future increases in the geographic
range of infectious diseases, more recent models now
predict range shifts in disease distributions, with little
net increase in area (Lafferty 2009). It is also argued that
there was little evidence that climate change had favored(2021) 8:24

Page 10 of 23

infectious diseases and that many factors can affect infectious diseases, some of which may overshadow the effects of the climate (Lafferty 2009). For example, Béguin
et al. (2011) projected that, for the SRES A1B scenario,
climate change would have a much weaker effect onmalaria than would the increase in GDP per capita. For
vector-borne diseases, the responses of both pathogens
and vectors to climate change are important. Tol(2002a) estimated that 1 °C warming will globally cause
a decrease in deaths by 56,000 but a significant increase
in South and Southeast Asia and Africa.
Heat-related excess mortality and occupational healthcost are two main factors of impact of warming for all
four RCPs (Takakura et al. 2019). A study on theeconomy-wide impacts of climate change on humanhealth using a CGE model found, for the SRES B1 scenario in 2050, that climate-change-induced health impacts range from + 0.08% to − 0.07%25 of GDP and from+ 0.13% to − 0.18%25 of CO2 emissions (Bosello et al.
2006). Another CGE model-based study, Hasegawa et al.
(2016a), evaluated the impact of climate change on human health via undernourishment based on two economic measures. These were (1) changes in morbidity
and mortality due to nine diseases caused by being
underweight as a child, which affect the labor force,
population, and demand for healthcare, and (2) the valueof lives lost and willingness to pay to reduce the risk.
They showed that the economic valuation of healthy
lives lost due to undernourishment in response to climate change under a pessimistic scenario based on SSP3
and RCP8.5 was around − 0.4–0.0%26 of global GDP in
2100, although this differed by region (ranging from −4.0–0.0%). They also showed that global economic losses
associated with the effects of additional health expenditure and the decrease in the labor force due to undernourishment caused by climate change were − 0.1–
0.0%27 of GDP and − 0.2–0.0%27 of household consumption, respectively (RCP8.5/SSP3, in 2100). Nordhaus andBoyer (1999) estimated the damage of 2.5 °C warming as
0.10% (output weighted) or 0.56% (population
weighted)28 of GDP.
As adaptation measures for the increase in infectious
diseases, the following are recommended: (1) further research on the relation between climate change and infectious diseases, (2) improvement of the prediction of thespatial–temporal process of climate change and the associated shifts in infectious diseases at various spatial andtemporal scales, and (3) establishment of locally effective

early warning systems for the health effects caused byclimate change (Wu et al. 2016).

Tachiiri et al. Progress in Earth and Planetary Science25

Depending on the region.
Mainly from the choice of values of life. Climate and crop model
uncertainty is also included.
27
Climate and crop model uncertainty.
28
The difference indicates that damages are concentrated in lowincome regions.
26

2.8 Industry and related economic activity

This section covers a wide range of economic activities
related to industry, including energy supply and demand,
infrastructure, insurance and finance, tourism, andtransportation. It was difficult to include manufacturing
as a sub-section, as only qualitative assessments of the
supply chain were available (e.g., Tenggren et al. 2020).
2.8.1 Energy sector

The energy sector is relatively sensitive to climatechange in terms of both supply and demand. Vulnerable
portions on the supply side include thermoelectric
power, hydropower, and renewable sources such as windenergy. On the other hand, significant variation exists inenergy demand—particularly related to the energy used
for thermal comfort, including heating, cooling, and dehumidification—between different latitudes. Thus, colder
areas may not require as much energy for warming as
temperatures increase, while warmer regions will use
more for cooling (de Cian et al. 2013). In turn, efforts on
climate change mitigation could induce technologicaldevelopment, which positively affects the economy (Matsumoto 2011a, 2011b, 2012).
For the supply side, the water stress resulting fromglobal warming can lead to insufficient water availability,
which in turn reduces thermoelectric and nuclear power
generation. Similarly, hydropower is expected to exhibit
more seasonal variability due to climate change; particularly, in summer, energy generation would decline byaround 14% (RCP4.5, 2091–2100) compared with thepresent generation, which would be a matter of concern
for the energy managers (Chilkoti et al. 2017). However,globally, no notable aggregated impacts on GDP are expected in the future when comparing low and high physical impact scenarios, although substantial differences
will be observed between regions29. In addition, because
climate change affects wind speeds, it is likely to influence the generation of wind energy30 (Pryor et al. 2012).
For the demand side, it is indicated that urban
economies in developed (developing) countries needto exhibit a cooling electricity response of 35–90 W/
29

For example, the maximum achievable hydropower generation might
range from − 37% in the Middle East to approximately 20% in theformer Soviet Union under RCP2.6 in 2100 compared to the level of
1960–1989, and for GDP, it might range from − 0.11% in Brazil toapproximately 0.21% in the former Soviet Union in the RCP2.6
scenario in 2100 relative to the scenario without considering thephysical climate impacts (Zhou et al. 2018a).
30
For example, Pryor et al. (2012) presented evidence of lower intensewind speeds, particularly in the western regions of the US in 2041–
2062, but expected no differences in extreme wind speeds relative to
1979–2000 under SRES A2.

(2021) 8:24

Page 11 of 23°C (2–9 W/°C)31 per capita as a cooling electricity
above room temperature for cooling (interquartile
range of estimates), with the difference attributed tothe adoption of air conditioning. For heating, the relation becomes less clear, as non-electricity-based
equipment can be used (Waite et al. 2017). Becauseof the rising household incomes and global warming,the increasing adoption of air conditioning by middleincome countries is confirmed by another study, sincemost of the homes in high-income countries are
already equipped with air-conditioning systems (Davisand Gertler 2015). Globally, heating energy demand
will decrease by 34% and cooling demand will increase by 72% by 2100, and the impact on global
CO2 emissions is small for heating but significant(some 1.6 GtCO2 in 2100) for cooling based on a reference scenario (Isaac and van Vuuren 2009). Under
the SRES A1B scenario in 2050, changes in heating
and cooling energy demands are projected to be −30% and + 73%, respectively, relative to the situationin 2050 in the absence of climate change (Arnell
et al. 2016a). While the percentage changes in heating
requirements are not significantly different among the
SRES A1B, A2, and B2 scenarios, the percentagechanges regarding cooling requirements show significant differences (up to around 20%) in the A2 and B2
scenarios compared with the A1B scenario (Arnell
et al. 2016b). As with the economic costs, thechanges in the global heating and cooling cost per 1°C warming are estimated to be −120 and 75 billion
USD, respectively (Tol 2002a). The impact on GDP is
expected to be − 0.34% for RCP8.5 due to changes inenergy demand for space heating and cooling systems,
and most of them are attributable to additional investment cost for introducing heating and coolingtechnology (Hasegawa et al. 2016b). The monetized
impacts of global warming on cooling/heating demand
are globally negative for all four RCPs under thesocio-economic assumptions of SSP1–5 (Takakura
et al. 2019).
Adaptation options, such as increasing plant efficiency
and replacing system types and fuel switches, are mentioned as effective alternatives to reduce the assessedvulnerability to changing climate and freshwater resources (van Vliet et al. 2016). Behrens et al. (2017)
compared four adaptation strategies of power generation
to water supply: further switch to seawater cooling,
retrofitting of plants to dry air cooling, early retirementof existing power plants, and cancelation of plannedplants in favor of renewables. They found that increased
future seawater cooling would ease some pressures,
highlighting the need for an integrated, basin-levelapproach in energy and water policy. Some other options are mentioned by Rübbelke and Vögele (2011), vanVliet et al. (2012), Zhou et al. (2018b), and Davide et al.
(2018), who analyzed the adaptation options regardingwater(conservation/harvesting/recycling/distribution,
irrigation [and its efficiency], desalinization), infrastructure (transport, electrification, multipurpose dams),
renewable energy, energy efficiency, health (early warning systems, medical services), living conditions (buildings, heating/cooling, water heating), information andeducation, and food (food storage, livestock) under the
Sustainable Development Goals32.

Tachiiri et al. Progress in Earth and Planetary Science31

Depending on the city.

2.8.2 Infrastructure

Energy, transport, industrial, and social infrastructure isusually constructed to have a long lifespan, meaning it
can potentially be vulnerable to long-term changes in
the climate.
Here, as we could not find global assessments, we
mention some city/country-scale analyses. The costs associated with damage to critical infrastructure due to climate extremes in Europe could be three times higher
than the figure in 1981–2010 of 3.4 billion EUR per yearby the 2020s, six times higher by the 2050s, and morethan 10 times higher by 2100 with the assumed biascorrected climate projections under the SRES A1B emissions scenario (Forzieri et al. 2018). It has also been reported that greater damage and/or the earlier failure ofpavement in the US is expected under the RCP8.5 and
4.5 climate change scenarios (Gudipudi et al. 2017).
Moreover, the incorrect selection of materials for US
pavement infrastructure could lead to additional costs ofapproximately 13.6, 19.0, and 21.8 billion USD by 2010,
2040, and 2070, respectively, under RCP4.5, increasing
to 14.5, 26.3, and 35.8 USD billion, respectively under
RCP8.5 (Underwood et al. 2017). In addition, from 2015
to 2099, damage to Alaskan public infrastructure
would cost approximately 5.5 billion (2015 USD, 3%
discount) under RCP8.5 and 4.2 billion USD under
RCP4.5, most of which is for roads and buildings33(Melvin et al. 2017).
Here, the potential adaptation options considered forinfrastructure include increased diameter culverts anddrainage systems (against flooding in roads and runways), base-layer modification and thermosyphon installation (permafrost thaw, roads, and runways), modified
binder/sealant application, and base-layer strengthening(precipitation, roads, and runways), increased diameterroof drainage systems (precipitation, buildings), and32

Furthermore, they proposed a framework connecting adaptation,
mitigation, and sustainable development.
33
However, the costs are reduced to 2.9 and 2.3 billion USD when
proactive adaptation is applied for the same scenarios (Melvin et al.
2017).

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base-layer modification and thermosyphon installation(permafrost thaw, railroads).

production may also affect passenger and freight transport indirectly (Koetse and Rietveld 2009).
Regarding the potential economic impacts or the
resulting CO2 emissions, Berrittella et al. (2006), using aworld CGE model under the SRES B1 scenario, evaluated the economic implications of climate-changeinduced variation in tourism demand and predicted regional variation in GDP of − 0.3–0.5%35 and CO2 emissions of − 0.001–0%35 in 2050. As an example ofimpacts through disasters, disruption costs in Newcastle
upon Tyne in the United Kingdom due to a 1-in-50-year
event leading to pluvial flooding could be 66% higher by
the 2080s, although interventions could lead to a 32% reduction in travel delays (Pregnolato et al. 2017).
For adaptation, Scott and Becken (2010) pointed outthat climate change adaptation research on tourism issignificantly delayed compared with other economic sectors, with low awareness of climate change and less strategic planning in anticipation of future changes inclimate. They also claim that the tourism industry has
shown its relatively high adaptive capacity in the sector
to the shocks but that knowledge of the capacity to copewith future climate and the broader environmental impacts and societal ramifications remains insufficient.
Scott et al. (2012) listed technical, managerial, policy, research, educational, and behavioral adaptation measures
for tourism businesses and emphasized the climatechange challenges that exist in tourism to provide implications and suggestions for policy-makers to reduce theclimate-related risks or to take the advantage of relevant
opportunities.

Tachiiri et al. Progress in Earth and Planetary Science

2.8.3 Tourism and transportation

Weather and climate conditions are important determinants of travel times and destinations for leisure and recreation. Thus, global warming can have a significant
impact on the tourism industry.
For the present status, the existing literature has foundthat cold-weather activities may be at greater risk due toclimate change, especially skiing (Steiger et al. 2019),
while there might be increased opportunities for warmweather activities (Hewer and Gough 2018). Anothersurvey on the weather preferences of French tourists revealed that tourists generally show a high tolerance forheat and even to heatwaves, while rainy conditions are adisincentive to travel (Dubois et al. 2016).
For the future projections, tourism will grow in the future, and the influence of changes in population and income is expected to be larger than that from climatechange (Hamilton et al. 2005). It is important to note,however, that there are significant gaps in knowledge
when predicting future tourism trends, including outdated
climate science, climate models, and climate change scenarios; inconsistent assessments between different regions;and a lack of assessment methods for weather sensitivity
and potential impacts of climate change for outdoor recreation and tourism activities (Hewer and Gough 2018).
The transportation sector also faces indirect impactsof climate change. Obradovich and Rahwan (2019)
showed that moving from freezing temperatures to 30°C increased vehicle miles traveled by over 10% in the
US and increased public transit trips by nearly 15%,
while temperatures beyond 30 °C had little influence onthese measures. As a result, over 1 trillion cumulative
vehicle miles traveled and 6 billion public transit trips in
the US are expected due to global warming under
RCP8.5. In contrast, climate change could also lower
water levels, making traditional river transport less reliable, as in Canada’s Mackenzie River (Du et al. 2017).
Global warming melts sea ice in the Arctic, increasing
accessibility for shipping (Ng et al. 2018), and the risksfacing Arctic aviation and marine transportation could
be assessed using the Arctic Climate Change Vulnerability Index, combining exposure, sensitivity, and adaptive
capacity (Debortoli et al. 2019). The transportation sector is also increasingly vulnerable to climate change incoastal areas, islands, and other places exposed to flooding34. Shifts in tourism patterns as well as agricultural
34

For example, in Caribbean Small Island Developing States, a rise intemperature could disrupt transport operations, and some airports
may be affected by more frequent coastal inundation due to climatechange (Monioudi et al. 2018).

2.8.4 Insurance and finance

Increased disasters will lead to insurance claims, andthus, the premiums should be raised. It is pointed outthat climate change can have negative impacts on the affordability and availability of insurance, slow the growthof the industry, and become a greater burden to governments and individuals (Mills 2005).
As a past example, Cannon et al. (2020), in their analysis of post-Katrina New Orleans in the US, argued thatuninsured or underinsured homes in cities prone toflooding have a greater risk of facing both flooding andhigher insurance premiums due to climate change.
A future projection of flood insurance premiums in
the Netherlands concluded that the premiums may increase considerably as a result of socio-economic andclimate changes but that this increase would be very
region-specific (Aerts and Botzen 2011). It is also reported that the expected “climate value at risk” for global financial assets was 1.8% under a BAU emissions
scenario (16.9% for the 99th percentile), falling to 1.2%
35

For eight regions.

(2021) 8:24

Page 13 of 23(9.2% for the 99th percentile) under a 2 °C scenario(Dietz et al. 2016). A study using an agent-based
climate–macroeconomic model predicted that banking
crises would be more frequent in the face of climatechange (by 26–248%) and that propping up insolvent
banks would lead to an annual fiscal burden of around
5–15% of GDP, doubling the ratio of public debt to
GDP (Lamperti et al. 2019). Dafermos et al. (2018)
argued that the liquidity of firms is likely to deteriorate,
leading to higher default rates and damaging both financial and non-financial corporate sectors. They also predicted that climate-induced financial instability may
have a negative influence on credit expansion, worsening
the overall impact on economic activity, and that introducing a green corporate quantitative easing program
could reduce this instability and slow global warming.
In the longer term, due to the decreased availability
and affordability of insurance coverage, insufficient
adaptation in areas of increasing risk could threaten theconcept of insurability itself (Herweijer et al. 2009). In
developing countries (Linnerooth-Bayer and Mechler
2006), particularly for agriculture (Nnadi et al. 2013),
insurance will work as an adaptation measure.

requires extensive relief efforts, had a modest to insignificant impact on migration, while heat stress consistently increased the long-term migration of men when no
relief systems were introduced, driven by the negative effect on farm and non-farm incomes. On the other hand,
Shayegh (2017) suggested the possibility that the migration of skilled people due to climate change may reduce
local income inequality.
Land degradation due to climate variability is an indirect effect that can trigger migration. For example, it issuggested that land scarcity and degradation are linked
to out-migration in general and to migration from theforest frontier of northern Guatemala in particular(López-Carr 2012). Good soil quality significantly reduces migration, particularly temporary labor migration,in Kenya but marginally increases migration in Uganda(Gray 2011). In low-lying island countries, there is growing consensus that international migration should beplanned for and coordinated (Hauer et al. 2020). In
Malé, the capital of the Maldives, more than 50% of respondents believed that future SLR was a serious challenge at the national level and they considered migration
to other countries as a potential option in response tothis, although many other factors (e.g., cultural, religious,
economic, and social factors) are believed to play an important role in migration-related decision-making (Stojanov et al. 2017).
McLeman and Smit (2006) and Tacoli (2009) argued
that migration could be an adaptation measure; labor
migration could be a particularly good opportunity toutilize migration to promote adaptation to climatechange (Barnett and Webber 2009). For the Maldives,the development of artificial islands (such as Hulhumalé)and coral propagation around existing islands are mentioned as hard and soft adaptation strategies for climatechange (Sovacool 2012).

Tachiiri et al. Progress in Earth and Planetary Science

2.9 Migration and civil/international conflict

It is commonly assumed that climate change will heavily
impact patterns of human migration and conflict via
shortages of resources (e.g., food, water), although it is difficult to assess the economic damage arising from this. In
addition, at present, it is difficult to project future trends
in the frequency and scale of migration and conflicts.
2.9.1 Migration

A global survey found that climatic conditions strongly
influenced migration for the period 2011–2015 byincreasing the severity of drought and the chances ofarmed conflict (Abel et al. 2019).
The following studies are examples of regional migration in the past. A study on the Indian Sundarbans
assessed the environmental, economic, and social factorsthat spurred migration and identified environmental factors as the most important (Guha and Roy 2016). In
rural Ethiopia, it was indicated that the migration ofmen for employment increased with drought and that
land-poor households were most vulnerable, while themovement of women for marriage decreased withdrought (Gray and Mueller 2012). Similarly, in Burkina
Faso, Henry et al. (2004) suggested that residents of drier
regions were more likely to participate in both temporary and permanent migration to other rural areas andthat short-term drought tended to increase long-term
migration to rural areas and decrease short-term migration to distant destinations. In rural Pakistan, Mueller
et al. (2014) found that flooding, a climate shock that

2.9.2 Civil and international conflict

Water and food shortages, and significant deteriorationof the living environment caused by climate change, canin turn lead to raiding or conflicts. For the past, a survey
on global- and country-scale analyses reported that anincrease of 1 standard deviation in temperature or rainfall raises interpersonal violence and intergroup conflictby 4% and 14%, respectively (Hsiang et al. 2013), while
policymakers have been warned against drawing general
conclusions from current data (Bernauer et al. 2012;
Hendrix 2017; Adams et al. 2018). Another global survey
pointed out that in western Asia in the period 2010–2012, when many countries were undergoing politicaltransformation, the effect of climate on conflict occurrence was confirmed (Abel et al. 2019). It is also reported that the risk of armed conflict was greater in thepresence of climate-related natural disasters in countries(2021) 8:24

Page 14 of 23

characterized by ethnic division (Schleussner et al.
2016). Moreover, it is reported that transboundary waterdisputes are also potential flashpoints for conflict in a
number of regions around the world, with climatechange and associated variability in the weather increasing the uncertainty surrounding access to clean water(Kreamer 2012). The global association between extreme
weather events and civil conflict was also observed for
droughts in Somalia36 (Maystadt and Ecker 2014). In theHorn of Africa, Solomon et al. (2018) demonstrated thatconflict had a strongly negative impact on the environment and that climate variability exacerbated the impactof this conflict.
For future warming, experts agree that the climate canaffect organized armed domestic conflict but claim that
other drivers, such as low socio-economic development
and poor state performance, are more influential. On
average, for the 2 °C scenario, the number of experts
expecting “negligible change” is similar to the total of
those selecting “moderate increase” and “substantialchange,” while for the 4 °C scenario, the numbers of experts selecting the three answers are similar to eachother (Mach et al. 2019).
For adaptation, a research study on Bangladesh mentioned the possibility that adaptation projects can potentially harm others and intensify violent conflicts andclaimed that the project planners and practitioners need
to become more aware of this fact (Sovacool 2018). Another study on Kenyan drylands also argued that consideration of the political dimensions of local adaptation isneeded for climate change adaptation policies to be successful and that conflict is not an external factor inhibiting local adaptation strategies but a part of theadaptation process (Eriksen and Lind 2009). On theother hand, also in (northern) Kenya, it is argued that
promoting adaptation includes reducing the level of conflict and insecurity. In addition, the following three key
recommendations are mentioned: strengthening of intercommunal conflict prevention and resolution mechanisms, establishing a regional framework that promotes
pastoral mobility across international borders, and reducing the availability of small arms through intergovernmental agreements and harmonized disarmament efforts(Schilling et al. 2011).

were of course unable to cover all or most studies, wewere able to review a considerable number of excellent
studies. In addition, it is also possible that existing studies contain gaps and/or biased results. Thus, although inthis section, we attempt to draw some conclusions fromthe review performed in the previous section, we need to
do so carefully. In addition, we need to be careful incomparing studies using the relatively simple damage
function approach and those using process-basedmodeling.
As reviewed in Section 2, many processes and sectors
experience non-negligible physical or ecological impacts
due to climate change. However, identifying those processes/sectors that are most important in terms of theinteraction between Earth and socio-economic systems
is difficult, because there is no established approach forcomparing these processes/sectors. The most effective
strategy for evaluating the potential feedbacks to the climate could be to compare consequent changes in GHG
emissions, but very few studies have addressed this.
Thus, we compared the impact on GDP, thinking that
GHG is roughly proportional to GDP (i.e., assuming that
other conditions are not affected by a GDP reduction of
up to 1%). Table 2 summarizes previous quantitative assessments of the impact on GHG emissions and GDP.
Many of the studies cited in this table employed a CGE
model, meaning that the impacts were evaluated after
the propagation of the shock for the economy as awhole. The second column from the right indicates theimportance of the process/sector in terms of its GDP or
GHG impacts, and the rightmost column displays thefeasibility based on the authors’ judgment.
For GHG emissions, among the sectors for which literature is available, those related to land productivity(i.e., cropland and pasture) have the strongest effect.
These sectors have a relatively low impact on GDP, but
the resultant change in land cover is 2–3%37, and that inland-use emission is 8–13%38 (Bajželj and Richards2014). In addition, changes in land cover can influence
the physics of the land surface, including albedo andevapotranspiration, as well as carbon absorption.
Changes in land surface conditions can affect fire occurrence, which can lead to other physical and biochemicalchanges. Other studies have also suggested the importance of changes in land cover and management (Brovkin
et al. 2013; Luyssaert et al. 2014; Harper et al. 2018).
Hence, this sector should not be ignored when considering the interaction between Earth and human systems.
In modeling, cropland allocation is typically determinedusing the suitability based on land conditions (Ramankutty et al. 2002; Di Vittorio et al. 2016). Areas with highTachiiri et al. Progress in Earth and Planetary Science

3 Discussion: interactive coupling of Earth andhuman systems
We have reviewed a plethora of existing studies on representative important sectors and processes. While we
36

Using the data of 1997–2009, Maystadt and Ecker (2014) also
estimated that an increase of 1 standard deviation in drought intensity
and length in Somalia would increase the likelihood of conflict by 62%,
with conflict arising from changes in livestock prices and theestablishment of livestock markets.

37
38

SRES B1 (2%) and A1 (3%) (relative to the no climate change case).
SRES B1 (8%) and A1 (13%) (relative to the no climate change case).

Tachiiri et al. Progress in Earth and Planetary Science(2021) 8:24

Page 15 of 23

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

CO2/GHG emission reduction (%) GDP loss (%)(negative value means GHG(negative valueemission increase)means GDP
growth)Remarks

Importance (inFeasibility
terms of GHG or of
GDP impacts)*
modeling**

2.2.1

Agriculture

3 and 5a0.02–0.06b, 0.13
(0.17)c, 0.06d

Feedback via land cover change(albedo, carbon flux, etc.)

H

E

2.2.2

Livestock

0.01e

Feedback via land cover change
and changes in livestock number

M

E

2.3

Water
resources

–

0.6d

–

M

M

2.4

SLR

0.0–0.15f, – 0.04–0.02g

0.32 (0.12)c, 0.0–
0.1f, 0.0–0.03g

Life-threatening

M

M

2.5

Naturaldisasters–

Negligibleh, 0–0.2i

Life-threatening

M

M

2.6

Ecosystem
services–

0.17 (0.10) c1,
0.4d1

–

M

D

2.7.1

Labor
productivity

0.25–0.45j

2.6–4.0k, 0.5–0.9j –
1.8l, 4.6l, 1.0–2.4m

H

E

2.7.2

Other healthissues− 0.13–0.18g

0.10 (0.56)c, 0.0–
0.1n, − 0.08–
0.07g

For VSL*** (mortality): 0.0–0.4%n,
0.17%/°Cc,o

M

M

2.8.1

Energy0 (2050), –1.1 (2100)p

0.2 (0.3)d2, 0.0–
0.2h, 0.34 (0.03)q

No notable aggregated impacts(supply)r

M

M

2.8.2

Infrastructure–

0.01–0.02s (EU),
0.1–0.2t (US)

-

L

M

2.8.3

Tourism and 0–0.001utransportation– 0.5–0.3u

Almost no change as a whole

L

D

2.8.4

Insurance
and finance––

5–15% needed to rescue
insolvent banksv

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 lifeaBajželj and Richards (2014): 5 and 3 for SRES A1 and B1 scenariosb
Fujimori et al. (2018), RCP8.5, SSP 2, 2100, CGEcNordhaus and Boyer (1999) output weighted (population weighted) for 2.5 °C warming, CGE; c1: settlement and ecosystem were merged in their evaluationdTol (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)h
Takakura et al. (2019), CGE; e1: among the scenarios (RCP-SSP)i
Mendelsohn et al. (2012)j
Matsumoto (2019) BAU, 2100, CGEkTakakura et al. (2017) BAU, 2100, CGE
lRoson and van der Mensbrugghe (2012). 1.8% and 4.6% for 2050 and 2100 (IAM including CGE)m
Takakura et al. (2018): RCP8.5 with reasonable time shift (< 3 h) as adaptationnHasegawa et al. (2016a) RCP8.5/2.6, 2100, CGEoBosworth 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)q
Hasegawa et al. (2016b) CGE, in 2100 with RCP8.5 (RCP2.6)
r
Zhou et al. (2018a) (CGE)s
Forzieri 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)v
Lamperti et al. (2019), BAU, agent-based model– No data from our review

suitability are allocated in order so that the total crop
yield (estimated using an agricultural crop yield model)
satisfies the food (and biofuel) demands given fromanother source (e.g., a socio-economic model). The crop
type to plant is then chosen to maximize the profit (e.g.,
Meiyappana et al. 2014; Hasegawa et al. 2017). Models

(2021) 8:24

Page 16 of 23

could be further improved through model intercomparisons (e.g., Prestele et al. 2016; Lawrence et al. 2016).
The effect of the livestock sector has been assessed tobe more moderate than that for the agricultural sector,
and an estimated GDP impact of 0.01%. However, raising livestock generates relatively high GHG emissions,particularly CH4 and N2O (Herrero et al. 2016b). The
manner for modeling this sector is in many cases simpler than that used for cropland (e.g., Yokohata et al.
2020), indicating there is more potential for sophistication. In Bajželj and Richards (2014), total agricultural(including pastoralism) GHG emissions, including net
forest cover, tropical pristine forest cover, and land-use
change emissions, were estimated as 3% and 5% for the
SRES B1 and A1 scenarios (relative to the no climatechange case), respectively. This is large compared withother sectors/processes (Table 2), and as this sector is apoint of contact between humans and natural systems,
the sector is considered to be one of the most importantin coupling human and Earth system models.
In addition to agriculture and livestock, the impactof labor productivity on GHG emissions appears to
be relatively significant (0.25–0.45%). Considering that
Matsumoto (2019), using a simpler approach, resulted
in a smaller GDP impact than Takakura et al. (2017),
it is possible that the GHG impact was underestimated in Matsumoto (2019). Considering the difference in numbers reported by these studies, the GHG
impact of labor productivity could be comparablewith that of the agricultural sector if Takakura et al.’s(2017) model is used. To incorporate this effect, theeasiest way is to use the simplified (linearized) relationship between temperature and labor productivity
for each of agriculture, manufacturing, and service
sectors presented by Roson and Sartori (2016), as
attempted by the example in Matsumoto (2019). Theenergy sector can also have a significant effect,causing a 1% increase in GHGs, mainly due toincreasing cooling demands. In Isaac and van Vuuren(2009), demand was calculated as a product of thenumber of households who owned air conditioners
and the unit energy consumption that was determinedwith cooling degree days39 and income. It should be
noted that there could be some inter-sectoral interaction, such as between energy demand and laborproductivity (via using air conditioning).
In terms of GDP, finance may have more impact
than labor productivity, with Lamperti et al. (2019)
reporting that 5–15% of GDP would be needed tobail out insolvent banks. However, they employed anagent-based economic model to arrive at theirestimates; when this type of model is not available, itwould be difficult to quantify this effect.
Other processes that have been reported to havepotentially large impacts are water resources, SLR andinundation, natural disasters, ecosystem services, disease,and other health issues, although most of these have
only been supported by a small number of studies each.
Thus, more research is needed for firm conclusions tobe drawn. In contrast, infrastructure, tourism, and transportation are predicted to have a relatively small impact.
However, it should be noted that a process or sector thathas a limited global impact on socio-economic systems
does not necessarily have a negligible regional impact.
Indeed, from an impact assessment perspective, regional
impacts may be more important, although this was not
the main focus of the current study. Of the five processes/sectors (i.e., water resources, SLR, natural disasters, ecosystem services, disease, and other healthissues), ecosystem services may be the most difficult tomodel due to their wide variety.
For life-threatening processes such as disease, migration, and conflict, although there are some examples to evaluate the monetary damage assessments(which causes feedbacks to the climate via GDP andthen emission changes), it is difficult to assess all theeffects to the climate. Here, we presented some examples of other approaches knowing it is difficult to obtain quantitative information on the amount offeedback that could be given to the climate. For disease and health issues, a larger impact was reported
when the value of statistical life (VSL) was employed
to predict additional mortality, but this does not directly impact GDP, and monetizing the value of humanlife is controversial. Thus, we may need more discussion when comparing the impact on GDP and the
VSL. However, we should not underestimate the importance of processes that are difficult to discuss interms of their impact on GDP. For example, natural
disasters and conflict can expose a large number ofpeople to danger, and migration can have a number ofdeleterious impacts (e.g., poverty [through the loss ofassets], discrimination, and the loss of identity) that
may not be easy to monetize. Possibly the most representative example of this may be small Pacific island
countries, who stand to lose a significant proportionof their territory due to the effects of climate change.
In addition, to incorporate migration and conflict ineconomic models, more knowledge of their causes andconsequences is required. For some studies, cases withadaptation were also evaluated, but originally, thecases without adaptation should be first evaluated andincorporated into the model (in this case, a human–
Earth system model), and then, the effects of adaptation should be evaluated using the model.

Tachiiri et al. Progress in Earth and Planetary Science

39

Cumulative temperature over 18 °C.

(2021) 8:24

Page 17 of 23

Although not mentioned in Section 2 or Table 2,another possible source of large uncertainty would behuman behavior. Beckage et al. (2018) showed such a
possibility leading to behavioral uncertainty of a comparable magnitude to physical uncertainty. However, thelarge uncertainty is not a result of the accumulation ofmany processes but a choice in the equation to calculate
for human behavior; three types (linear, logistic, andcubic) of equations were applied despite the fact that thetheory of planned behavior generally uses a linear functional form. Thus, we expect follow-up studies.
As noted above, this is a current overview, but as also
noted, some sectors’ assessments are based on a small
number of studies, and in some cases, there are no
global assessments on the impact on GDP and GHG
emissions. Thus, we need to accumulate more studies
for each sector to draw solid conclusions.

GHG: Greenhouse gas; IAM: Integrated assessment model;
IPCC: Intergovernmental Panel on Climate Change; RCP: Representative
Concentration Pathway; SI: Supplementary information; SLR: Sea level rise;
SRES: Special Report on Emission Scenarios; SSP: Shared Socioeconomic
Pathway; TCRE: Transient climate response to cumulative carbon emissions;
TEEB: The Economics of Ecosystems and Biodiversity; TFP: Total factor
productivity; US: United States; USD: US dollar; VSL: Value of statistical life;
WBGT: Wet-bulb globe temperature; WCI: Water crowding index; WSI: Water
stress index

Tachiiri et al. Progress in Earth and Planetary Science

4 Conclusions
In this study, we reviewed the impacts of climate change
on processes and sectors that were selected from a list of
impact assessment studies based on their potential tocause feedbacks to the climate. We focused primarily on
recent quantitative studies; although it was not possible
to review all of these, we reviewed a sufficient number
to overview how a specific process/sector is affected byclimate change and the extent of its impact on the economy and subsequently the climate by evaluating its relative importance in terms of GHG emissions and GDP.
For GHG emissions, we identified land productivity, particularly agriculture, as a key process with the additional
benefit of high modeling feasibility, while labor productivity (in terms of both GHG emissions and GDP) andthe energy sector (GHG) were also found to play important roles. We also identified water resources, SLR,
natural disasters, ecosystem services, and disease andother health issues as having a non-negligible impact on
GDP and/or GHG.
To incorporate life-threatening processes such as disease, migration, and conflict, a solely economic impact
assessment may be insufficient, because even though
they are obviously vulnerable processes, it is difficult toestimate their monetary costs. In addition, the financesector is likely to have a large impact on GDP, but conventional CGE models may be unable to incorporate
this, so more sophisticated models, such as agent-based
economic models, should be considered. Based on the
results of this review, we intend to look at coupling
Earth and socio-economic models in future work.
Abbreviations
BAU: Business as usual; CGE: Computable general equilibrium; DICE: Dynamic
Integrated Climate-Economy (model); EbA: Ecosystem-based adaptation;
EMIC: Earth system model of intermediate complexity; ESM: Earth systemmodel; GCM: Global climate model; GDP: Gross domestic product;

5 Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40645-021-00418-7.
Additional file 1.
Acknowledgements
The authors thank Drs. Kiyoshi Takahashi and Tokuta Yokohata from the
National Institute for Environmental Studies, Japan, Dr. Atsushi Kurosawa
from the Institute of Applied Energy, and Dr. Kaoru Kakinuma from Shanghai
University for their helpful comments on an earlier version of the manuscript.
This work was supported by the Integrated Research Program for Advancing
Climate Models (TOUGOU) Grant Number JPMXD0717935715 from the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.
Authors’ contributions
All three authors contributed to literature survey. XS wrote the energy,
infrastructure, tourism, and transportation parts. KM wrote the human healthpart. KT wrote all other parts including the Introduction, Discussion, andConclusion. All authors read and approved the final manuscript.
Authors’ information
All authors are the members of the “Earth - Socioeconomic System
Interaction” Team in Theme B: “Sophisticated Earth system model forevaluating emission reductions needed” of the Integrated Research Programfor Advancing Climate Models (the TOUGOU program).
Funding
This work was supported by the Integrated Research Program for Advancing
Climate Models (TOUGOU) Grant Number JPMXD0717935715 from the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan,
and JSPS KAKENHI grant number JP18K11754.
Availability of data and materials
Not applicable

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
Research Institute for Global Change, Japan Agency for Marine-Earth
Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama
236-0001, Japan. 2Faculty of Economics, Toyo University, 5-28-20 Hakusan,
Bunkyo-ku,, Tokyo 112-8606, Japan.
Received: 31 March 2020 Accepted: 12 March 2021

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