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

=== 4.2.6 Implications of Accelerated Mitigation for National Development Objectives ===

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==== 4.2.6.1 Introduction ====

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This section examines how accelerated mitigation may impact the realisation of development objectives in the near- and mid-term. It focuses on three objectives discussed in the literature, sustaining economic growth ( [[#4.2.6.2|Section 4.2.6.2]] ), providing employment ( [[#4.2.6.3|Section 4.2.6.3]] ), and alleviating poverty and ensuring equity ( [[#4.2.6.4|Section 4.2.6.4]] ). It complements similar review performed at global level in [[IPCC:Wg3:Chapter:Chapter-3#3.6|Section 3.6]] . For a comprehensive survey of research on the impact of mitigation in other areas (including air quality, health, and biodiversity), see [[#Karlsson--2020|Karlsson et al. (2020)]] .

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==== 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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'''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"
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! Reference

! Country/region

! Explanation for positive outcome of mitigation on GDP

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| Antimiani et al. (2016)

| European Union

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

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| [[#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.

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

| Indonesia

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

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| [[#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.

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| [[#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.

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| [[#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).

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

| China

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

|}

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==== 4.2.6.3 Mitigation and Employment in the Short- and Medium-term ====

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Numerous studies have analysed the potential impact of carbon pricing on labour markets. [[#Chateau--2018|Chateau et al. (2018)]] and [[#OECD--2017a|OECD (2017a)]] find that the implementation of green policies globally (defined broadly as policies that internalise environmental externalities through taxes and other tools, shifting profitability from polluting to green sectors) need not harm total employment, and that the broad skill composition (low, high- and medium-skilled jobs) of emerging and contracting sectors is very similar, with the largest shares of job creation and destruction at the lowest skill level. To smoothen the labour market transition, they conclude that it may be important to reduce labour taxes, to compensate vulnerable households, and to provide education and training programs, the latter making it easier for labour to move to new jobs. Consistent with this, other studies that simulate the impact of scenarios with more or less ambitious mitigation policies (including 100% reliance on renewable energy by 2050) find relatively small (positive or negative) impacts on aggregate global employment that are more positive if labour taxes are reduced but encompass substantial losses for sectors and regions that today are heavily dependent on fossil fuels (Arndt et al. 2013; [[#Huang--2019|Huang et al. 2019]] ; [[#Vandyck--2016|Vandyck et al. 2016]] ; [[#Jacobson--2019|Jacobson et al. 2019]] ). Among worker categories, low-skilled workers tend to suffer wage losses as they are more likely to have to reallocate, something that can come at a cost in the form of a wage cut (assuming that workers who relocate are initially less productive than those who already work in the sector). The results for alternative carbon revenue recycling schemes point to trade-offs: a reduction in labour taxes often leads to the most positive employment outcomes while lump-sum (uniform per-capita) transfers to households irrespective of income yield a more egalitarian outcome.

The results from country-level studies using CGE models tend be similar to those at global level. Aggregate employment impacts are small and may be positive especially if labour taxes are cut, see for example, [[#Telaye--2019|Telaye et al. (2019)]] for Ethiopia,( [[#Kolsuz--2017|Kolsuz and Yeldan (2017)]] for Turkey, [[#Fragkos--2017|Fragkos et al. (2017)]] for the EU, and [[#Mu--2018b|Mu et al. (2018b)]] for China. On the other hand, sectoral reallocations away from fossil-dependent sectors may be substantial, see for example, Alton et al. (2014) for South Africa or [[#Huang--2019|Huang et al. (2019)]] for China. Targeting of investment to labour-intensive green sectors may generate the strongest employment gains, see, for example, [[#Perrier--2018|Perrier and Quirion (2018)]] for France, [[#van%20Meijl--2018|van Meijl et al. (2018)]] for the Netherlands, and Patrizio et al. 2(018) for the USA. Changes in skill requirements between emerging and declining sectors appear to be quite similar, involving smaller transitions than during the IT revolution ( [[#Bowen--2018|Bowen et al. 2018]] ).

In sum, the literature suggests that the employment impact of mitigation policies tends to be limited on aggregate, but can be significant at the sectoral level ( ''medium evidence'' , ''medium agreement'' ) and that cutting labour taxes may limit adverse effects on employment ( ''limited evidence'' , ''medium agreement'' ). Labour market impacts, including job losses in certain sectors, can be mitigated by equipping workers for job changes via education and training, and by reducing labour taxes to boost overall labour demand ( [[#Stiglitz--2017|Stiglitz et al. 2017]] ) ( [[#4.5|Section 4.5]] ).

Like most of the literature on climate change, the above studies do not address gender aspects. These may be significant since the employment shares for men and women vary across sectors and countries.

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==== 4.2.6.4 Mitigation and Equity in the Near and Mid-term ====

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Climate mitigation may exacerbate socio-economic pressures on poorer households ( [[#Jakob--2014|Jakob et al. 2014]] ). First, the price increase in energy-intensive goods and services – including food ( [[#Hasegawa--2018|Hasegawa et al. 2018]] ) – associated with mitigation may affect poorer households disproportionally (Bento 2013), and increase the number of energy-poor (Berry 2019). Second, the mitigation may disproportionally affect low-skilled workers (see previous section). Distributional issues have been identified not only with explicit price measures (carbon tax, emission permits system, subsidy removal), but also with subsidies for renewables ( [[#Borenstein--2016|Borenstein and Davis 2016]] ), and efficiency and emissions standards ( [[#Davis--2019|Davis and Knittel 2019]] ; [[#Bruegge--2019|Bruegge et al. 2019]] ; [[#Levinson--2019|Levinson 2019]] ; [[#Fullerton--2019|Fullerton and Muehlegger 2019]] ).

Distributional implications, however, are context specific, depending on consumption patterns (initially and ease of adjusting them in response to price changes) and asset ownership (see for example analysis of energy prices in Indonesia by Renner et al. 2019). In an analysis of the distributional impact of carbon pricing based on household expenditure data for 87 low- and middle-income countries, [[#Dorband--2019|Dorband et al. (2019)]] find that, in countries with a per-capita income of up to USD15,000 per capita (purchasing power parity (PPP) adjusted), carbon pricing has a progressive impact on income distribution and that there may be an inversely U-shaped relationship between energy expenditure shares and per-capita income, rendering carbon pricing regressive in high-income countries, in other words, in countries where the capacity to pursue compensatory policies tends to be relatively strong.

The literature finds that the detailed design of mitigation policies is critical for their distributional impacts ( ''robust evidence'' , ''high agreement'' ). For example, [[#Vogt-Schilb--2019|Vogt-Schilb et al. (2019)]] suggest to turn to cash transfer programs, established as some of the most efficient tools for poverty reduction in developing countries. In an analysis of Latin America and the Caribbean, they find that allocation of 30% of carbon revenues would suffice to compensate poor and vulnerable households on average, leaving the rest for other uses. This policy tool is not only available in countries with relatively high per-capita incomes: in Sub-Saharan Africa, where per-capita incomes are relatively low, cash transfer programs have been implemented in almost all countries (Beegle et al. 2018, p. 57), and are found central to the success of energy subsidy reforms ( [[#Rentschler--2017|Rentschler and Bazilian 2017]] ). In the same vein, Böhringer et al. (2021) finds that recycling of revenues from emissions pricing in equal amounts to every household appeals as an attractive strategy to mitigate regressive effects and thereby make stringent climate policy more acceptable on societal fairness grounds. However, distributional gains from such recycling may come at the opportunity cost of not reaping efficiency gains from reductions in the taxes that are most distortionary (Goulder et al. 2019).

Distributional concerns related to climate mitigation are also prevalent in developed countries, as demonstrated, for instance, by France’s recent yellow-vest movement, which was ignited by an increase in carbon taxes. It exemplifies the fact that, when analysing the distributional effects of carbon pricing, it is not sufficient to consider vertical redistribution (i.e., redistribution between households at different incomes levels but also horizontal redistribution (i.e., redistribution between households at similar incomes which is due to differences in terms of spending shares and elasticities for fuel consumption). Compared to vertical redistribution, it is more difficult to devise policies that effectively address horizontal redistribution (Cronin et al. 2019; [[#Pizer--2019|Pizer and Sexton 2019]] ; [[#Douenne--2020|Douenne 2020]] ). However, it has been shown ex post that transfer schemes considering income levels and location could have protected or even improved the purchasing power of the bottom half of the population ( [[#Bureau--2019|Bureau et al. 2019]] ). Investments in public transportation may reduce horizontal redistribution if it makes it easier for households to reduce fossil fuel consumption when prices increase (see Sections 4.4.1.5 and 4.4.1.9). Similarly, in relation to energy use in housing, policies that encourage investments that raise energy efficiency for low-income households may complement or be an alternative to taxes and subsidies as a means of simultaneously mitigating and reducing fuel poverty ( [[#Charlier--2019|Charlier et al. 2019]] ). From a different angle, public acceptance of the French increase in the carbon tax could also have been enhanced via a public information campaign could have raised public acceptance of the carbon tax increase ( [[#Douenne--2020|Douenne and Fabre 2020]] ). (See [[#4.4.1.8|Section 4.4.1.8]] for a discussion of this and other factors that influence public support for carbon taxation.)

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