Editing IPCC:AR6/WGIII/Chapter-4 (section)

==== 4.2.6.2 Mitigation and Economic Growth in the Near- and Mid-term ====

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A significant part of the literature assesses the impacts of mitigation on GDP, consistent with policymakers’ interest in this variable. It must be noted upfront that computable equilibrium models, on which our assessments are mostly based, capture the impact of mitigation on GDP and other core economic variables while typically overlooking other effects that may matter (like improvements in air quality). Second, even though GDP (or better, GDP per capita) is not an indicator of welfare ( [[#Fleurbaey--2013|Fleurbaey and Blanchet 2013]] ), changes in GDP per capita across countries and over time are highly correlated with changes in welfare indicators in the areas of poverty, health, and education ( [[#Gable--2015|Gable et al. 2015]] ). The mechanisms linking mitigation to GDP outlined below would remain valid even with alternative indicators of well-being ( [[IPCC:Wg3:Chapter:Chapter-5#5.2.1|Section 5.2.1]] ). Third, another stream of literature criticises the pursuit of economic growth as a goal, instead advocating a range of alternatives and suggesting modelling of post-growth approaches to achieve rapid mitigation while improving social outcomes ( [[#Hickel--2021|Hickel et al. 2021]] ). In the language of the present chapter, these alternatives constitute alternative development pathways.

Most country-level mitigation modelling studies in which GDP is an endogenous variable report negative impacts of mitigation on GDP in 2030 and 2050, relative to the reference ( ''robust evidence'' , ''high agreement'' ), for example ( [[#Nong--2017|Nong et al. 2017]] ) for Australia, ( [[#Chen--2013|Chen et al. 2013]] ) for Brazil, ( [[#Dai--2016|Dai et al. 2016]] ; [[#Li--2017|Li et al. 2017]] ; [[#Dong--2018|Dong et al. 2018]] ; [[#Mu--2018a|Mu et al. 2018a]] ; [[#Zhao--2018|Zhao et al. 2018]] ; [[#Cui--2019|Cui et al. 2019]] ) for China, (Álvarez-Espinosa et al. 2018) for Colombia, ( [[#Fragkos--2017|Fragkos et al. 2017]] ) for the EU, ( [[#Mittal--2018|Mittal et al. 2018]] ) for India, ( [[#Fujimori--2019|Fujimori et al. 2019]] ) for Japan, ( [[#Veysey--2014|Veysey et al. 2014]] ) for Mexico, ( [[#Pereira--2016|Pereira et al. 2016]] ) for Portugal, (Alton et al. 2014; [[#van%20Heerden--2016|van Heerden et al. 2016]] ) for South Africa, ( [[#Chunark--2017|Chunark et al. 2017]] ) for Thailand, ( [[#Acar,%C2%A0S.%20and%C2%A0A.E.%20Yeldan--2016|Acar and Yeldan 2016]] ) for Turkey, ( [[#Roberts--2018b|Roberts et al. 2018b]] ) for the UK, ( [[#Zhang--2017|Zhang et al. 2017]] ; [[#Chen--2019|Chen and Hafstead 2019]] ) for USA, ( [[#Nong--2018|Nong 2018]] ) for Vietnam ( ). The downward relationship between mitigation effort and emissions is strong in studies up to 2030, much weaker for studies looking farther ahead. In all reviewed studies, however, GDP continues to grow even with mitigation. It may be noted that none of the studies assessed above integrates the benefits of mitigation in terms of reduced impacts of climate change or lower adaptation costs. This is not surprising since these studies are at national or regional scale and do not extend beyond 2050, whereas the benefits depend on global emissions and primarily occur after 2050. Discussion on reduced impacts is provided in [[IPCC:Wg3:Chapter:Chapter-3#3.6.2|Section 3.6.2]] and Cross-Working Group Box 1 in Chapter 3.

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[[File:c62221328e15fd00229b8677895458e5 IPCC_AR6_WGIII_Figure_4_4.png]]

'''Figure 4.4 | GDP against emissions in country-level modelling studies, in variations relativ''' '''e to reference.'''

Two major mechanisms interplay to explain the impact of mitigation on GDP. First, the carbon constraint imposes reduced use of a production factor (fossil energy), thus reducing GDP. In the simulations, the mechanism at work is that firms and households reduce their use of GHG-intensive goods and services in response to higher prices due to reduced fossil energy use. Second, additional investment required for mitigation partially crowds out productive investment elsewhere ( [[#Fujimori--2019|Fujimori et al. 2019]] ), except in Keynesian models in which increased public investment actually boosts GDP ( [[#Pollitt--2015|Pollitt et al. 2015]] ; [[#Landa%20Rivera--2016|Landa Rivera et al. 2016]] ; [[#Bulavskaya--2018|Bulavskaya and Reynès 2018]] ). Magnitude and duration of GDP loss depend on the stringency of the carbon constraint, the degree of substitutability with less-GHG-intensive goods and services, assumptions about costs of low-carbon technologies and their evolution over time (e.g., [[#Duan--2018|Duan et al. 2018]] ; [[#van%20Meijl--2018|van Meijl et al. 2018]] ; [[#Cui--2019|Cui et al. 2019]] ) and decisions by trading partners, which influence competitiveness impacts for firms (Alton et al. 2014; [[#Fragkos--2017|Fragkos et al. 2017]] ) ( ''high evidence'' , ''h'' ''igh agreement'' ).

In the near term, presence of long-lived emissions intensive capital stock, and rigidities in the labour market ( [[#Devarajan--2011|Devarajan et al. 2011]] ) and other areas may increase impacts of mitigation on GDP. In the mid-term, on the other hand, physical and human capital, technology, institutions, skills or location of households and activities are more flexible. The development of renewable energy may help create more employment and demands for new skills, particularly in the high-skill labour market (Helgenberger, S. et al., 2019). In addition, cumulative mechanisms such as induced technical change or learning by doing on low-emissions technologies and process may reduce the impacts of mitigation on GDP.

Country-level studies find that the negative impacts of mitigation on GDP can be reduced if pre-existing economic or institutional obstacles are removed in complement to the imposition of the carbon constraint ( ''robust evidence'' , ''high agreement'' ). For example, if the carbon constraint takes the form of a carbon tax or of permits that are auctioned, the way the proceeds from the tax (or the revenues from the sales of permits) are used is critical for the overall macroeconomic impacts ( [[#Chen--2013|Chen et al. 2013]] ). (For a detailed discussion of different carbon pricing instruments, including the auctioning of permits, see [[IPCC:Wg3:Chapter:Chapter-13#13.6.3|Section 13.6.3]] ).

shows that depending on the choice of how to implement a carbon constraint, the same level of carbon constraint can yield very different outcomes for GDP. The potential for mitigating GDP implications of mitigation through fiscal reform is discussed in [[#4.4.1.8|Section 4.4.1.8]] .

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[[File:4be61407bede16582e61ffcc636da0a5 IPCC_AR6_WGIII_Figure_4_5.png]]

'''Figure 4.5 | Illustrative ranges of variations in GDP relative to reference in 2030 associated with introduction of carbon constraint, depending on modality of policy implementation.''' Source: based on Alton et al. (2014); [[#Devarajan--2011|Devarajan et al. (2011)]] ; [[#Fernandez--2018|Fernandez and Daigneault (2018)]] ; [[#Glomsrød--2016|Glomsrød et al. (2016)]] ; [[#Nong--2018|Nong (2018)]] ; Asakawa et al. (2021). Stringency of carbon constraint is not comparable across the studies.

More generally, mitigation costs can be reduced by proper policy design if the economy initially is not on the efficiency frontier ( [[#Grubb--2014|Grubb 2014]] ), defined as the set of configurations within which the quality of the environment and economic activity cannot be simultaneously improved given current technologies – such improvements in policy design may include reductions in distortionary taxes. Most of the studies which find that GDP increases with mitigation in the near term precisely assume that the economy is initially not on the frontier. Making the economy more efficient – in other words, lifting the constraints that maintain the economy in an interior position – creates opportunities to simultaneously improve economic activity and reduce emissions. Table 4.9 describes the underlying assumptions in a selection of studies.

Finally, ''marginal'' costs of mitigation are not always reported in studies of national mitigation pathways. Comparing numbers across countries is not straightforward due to exchange rate fluctuations, differing assumptions by modellers in individual country studies, etc. The database of national mitigation pathways assembled for this Report – which covers only a fraction of available national mitigation studies in the literature – shows that marginal costs of mitigation are positive, with a median value of 101 USD2010 tCO 2 –1 in 2030, 244 in 2040 and 733 in 2050 for median mitigation efforts of 21%, 46% and 76% relative to business-as-usual respectively. Marginal costs increase over time along accelerated mitigation pathways, as constraints become tighter, with a non-linearity as mitigation reaches 80% of reference emissions or more. Dispersion across and within countries is high, even in the near term but increases notably in the mid-term ( ''medium evidence'' , ''med'' ''ium agreement'' ).

'''Table 4.9 | Examples of country-level modelling studies finding positive short-term outcome of mitigation on GDP relati''' '''ve to baseline.'''

{| class="wikitable"
|-
! Reference

! Country/region

! Explanation for positive outcome of mitigation on GDP

|-
| Antimiani et al. (2016)

| European Union

| GDP increases relative to reference only in the scenario with global cooperation on mitigation.

|-
| [[#Willenbockel--2017|Willenbockel et al. (2017)]]

| Kenya

| The mitigation scenario introduces cheaper (geothermal) power generation units than in BAU (in which thermal increases). Electricity prices actually decrease.

|-
| [[#Siagian--2017|Siagian et al. (2017)]]

| Indonesia

| Coal sector with low productivity is forced into BAU. Mitigation redirects investment towards sectors with higher productivity.

|-
| [[#Blazquez--2017|Blazquez et al. (2017)]]

| Saudi Arabia

| Renewable energy penetration assumed to free oil that would have been sold at publicly subsidised price on the domestic market to be sold internationally at market price.

|-
| [[#Wei--2019|Wei et al. (2019)]]

| China

| Analyse impacts of feed-in tariffs to renewables, find positive short-run impacts on GDP; public spending boost activity in the RE sector. New capital being built at faster rate than in reference increases activity more than activity decreases due to lower public spending elsewhere.

|-
| [[#Gupta--2019|Gupta et al. (2019)]]

| India

| Savings adjust to investment and fixed unemployment is considered target of public policy, thereby limiting impact of mitigation on GDP relative to other economic variables (consumption, terms of trade).

|-
| [[#Huang--2019|Huang et al. (2019)]]

| China

| Power generation plan in the baseline is assumed not cost minimising.

|}

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