Editing IPCC:AR6/WGIII/Chapter-1 (section)

=== 1.7.1 Aggregated Approaches: Economic Efficiency and Global Dynamics of Mitigation ===

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Some of the most established and influential approaches to understanding the ''aggregate'' causes and consequences of climate change and mitigation across societies, draw upon economic theories and modelling to generate global emission pathways in the absence of climate policies and to study alternative mitigation pathways (described in detail in [[IPCC:Wg3:Chapter:Chapter-3#3.2.5|Section 3.2.5]] , and Appendix 3). The underlying economic concepts aggregate wealth or other measures of welfare based on utilitarian ethical foundations, and in most applications, a number of additional assumptions detailed in AR5 (Chapters 2 and 3).

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==== 1.7.1.1 Cost-benefit Analysis and Cost-effectiveness Analysis ====

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Such global aggregate economic studies coalesce around two main questions. One, as pioneered by Nordhaus (1992, 2008) attempts to monetise overall climate damages and mitigation costs so as to strike a ‘cost-benefit optimum’ pathway. More detailed and empirically-grounded ‘cost-effectiveness analysis’ explores pathways that would minimise mitigation costs ( [[#Ekholm--2014|Ekholm 2014]] ; [[#IPCC--2014a|IPCC 2014a]] [[IPCC:Wg3:Chapter:Chapter-2#2.5|Section 2.5]] ; [[#Weyant--2017|Weyant 2017]] ) for given targets (e.g., as agreed in international negotiations, see [[IPCC:Wg3:Chapter:Chapter-3#3.2|Section 3.2]] in Chapter 3). Bothapproaches recognise that resources are limited and climate change competes with other priorities in government policymaking, and are generally examined with some form of Integrated Assessment Model (IAM) ( [[#1.5|Section 1.5]] and Appendix III). Depending on the regional disaggregation of the modelling tools used and on the scope of the analyses, these studies may or may not address distributional aspects within and across nations associated with climate policies ( [[#Bauer--2020|Bauer et al. 2020]] ).

For at least 10 to 15 years after the first computed global cost-benefit estimate ( [[#Nordhaus--1992|Nordhaus 1992]] ), the dominant conclusions from these different approaches seemed to yield very different recommendations, with cost-benefit studies suggesting lenient mitigation compared to the climate targets typically recommended from scientific risk assessments ( [[#Weyant--2017|Weyant 2017]] ). Over the past 10 to 15 years, literature has made important strides towards reconciling these two approaches, both in the analytic methods and the conclusions arising.

'''Damages and risks.''' Incorporating impacts which may be extremely severe but are uncertain (known as ‘fat tails’ ( [[#Weitzman--2009|Weitzman 2009]] , 2011)), strengthens the economic case for ambitious action to avoid risks of extreme climate impacts ( [[#Ackerman--2010|Ackerman et al. 2010]] ; [[#Fankhauser--2013|Fankhauser et al. 2013]] ; [[#Dietz--2015|Dietz and]] [[#Stern--2015|Stern 2015]] ). The salience of risks has also been amplified by improved understanding of climate ‘tipping points’ ( [[#Lontzek--2015|Lontzek et al. 2015]] ; [[#Lenton--2019|Lenton et al. 2019]] ); valuations should reflect that cutting emissions reduces not only average expected damages, but also the risk of catastrophic events ( [[#IWG--2021|IWG 2021]] ).

'''Discounting.''' The role of time discounting in weighting future climate change impacts against today’s costs of mitigating emissions has been long recognised ( [[#Weitzman--1994|Weitzman 1994]] , 2001; [[#Nordhaus--2007|Nordhaus 2007]] ; [[#Stern--2007|Stern 2007]] ; [[#Dasgupta--2008|Dasgupta 2008]] ). Its importance is underlined in analytical Integrated Assessment Models (IAMs) ( [[#Golosov--2014|Golosov et al. 2014]] ; [[#van%20den%20Bijgaart--2016|van den Bijgaart et al. 2016]] ; [[#van%20der%20Ploeg--2019|van der Ploeg and Rezai 2019]] ) (Annex III). Economic literature suggests applying risk-free, public, and long-term interest rates when evaluating overall climate strategy ( [[#Weitzman--2001|Weitzman 2001]] ; [[#Dasgupta--2008|Dasgupta 2008]] ; [[#Arrow--2013|Arrow et al. 2013]] ; [[#Groom--2017|Groom and Hepburn 2017]] ). Expert elicitations indicate values around 2% (majority) to 3% ( [[#Drupp--2018|Drupp et al. 2018]] ). This is lower than in many of the studies reviewed in earlier IPCC assessments, and many IAM studies since, and by increasing the weight accorded to the future would increase current ‘optimal effort’. The US Interagency Working Group on the Social Cost of Carbon used 3% as its central value ( [[#IAWG--2016|IAWG 2016]] ; [[#Li--2018|Li and Pizer 2018]] ; [[#Adler--2017|Adler et al. 2017]] ). Individual projects may require specific risk adjustments.

'''Distribution of impacts.''' The economic damages from climate change at the nationally aggregated and sub-national level are very diverse ( [[#Moore--2017|Moore et al. 2017]] ; [[#Ricke--2018|Ricke et al. 2018]] ; [[#Carleton--2020|Carleton et al. 2020]] ). A ‘global damage function’ necessarily implies aggregating impacts across people and countries with different levels of income, and over generations, a process which obscures the strategic considerations that drive climate policymaking ( [[#Keohane--2016|Keohane and Oppenheimer 2016]] ). Economics acknowledges there is no single, objectively defined ‘social welfare function’ ( [[#IPCC--1995|IPCC 1995]] , 2014a). This applies also to the distribution of responses: both underline the relevance of equity (next section) and global negotiations to determine national and collective objectives.

Obvious limitations arise from these multiple difficulties in assessing an objective, globally acceptable single estimate of climate change damages (e.g., [[#Arrow--2013|Arrow et al. 2013]] ; [[#Pindyck--2013|Pindyck 2013]] ; [[#Auffhammer--2018|Auffhammer 2018]] ; [[#Stern--2021|Stern et al. 2021]] ), with some arguing that agreement on a specific value can never be expected ( [[#Rosen--2015|Rosen and Guenther 2015]] ; [[#Pezzey--2018|Pezzey 2018]] ). A new generation of cost-benefits analysis, based on projections of actual observed damages, results in stronger mitigation efforts as optimal ( [[#Glanemann--2020|Glanemann et al. 2020]] ; [[#Hänsel--2020|Hänsel et al. 2020]] ). Overall, the combination of improved damage functions with the wider consensus on low discount rates (as well as lower mitigation costs due to innovation) has increasingly yielded ‘optimal’ results from benefit-cost studies in line with the range established in the Paris Agreement (Cross-Working Group Box 1 in Chapter 3).

'''Hybrid cost-benefit approaches''' that extend the objective of the optimisation beyond traditional welfare, adding some form of temperature targets as in [[#Llavador--2015|Llavador et al. (2015)]] and [[#Held--2019|Held (2019)]] also represent a step in bridging the gap between the two approaches and result in proposed strategies much more in line with those coming from the cost-effectiveness literature. Approaching from the opposite side, cost-effectiveness studies have looked into incorporating benefits from avoided climate damages, to improve the assessment of net costs ( [[#Drouet--2021|Drouet et al. 2021]] ).

Cost-benefit IAMs utilise damage functions to derive a social cost of CO 2 emissions’ (SCC – the additional cost to society of a pulse of CO 2 emissions). One review considered that ‘the best estimate’ of the optimal (near-term) level ‘still ranges from a few tens to a few hundreds of dollars per ton of carbon’ ( [[#Tol--2018|Tol 2018]] ), with various recent studies in the hundreds, taking account of risks (Taconet et al. 2019), learning ( [[#Ekholm--2018|Ekholm 2018]] ) and distribution ( [[#Ricke--2018|Ricke et al. 2018]] ). In addition to the importance of uncertainty/risk, aggregation, and realistic damage functions as noted, on which some progress has been made, some reviews additionally critique how IAMs represent abatement costs in terms of energy efficiency and innovation (e.g., [[#Farmer--2015|Farmer et al. 2015]] ; [[#Rosen--2015|Rosen and Guenther 2015]] ; [[#Keen--2021|Keen 2021]] ) (Sections 1.7.3 and 1.7.4). IAMs may better reflect associated ‘rebound’ at system level ( [[#Saunders--2021|Saunders et al. 2021]] ), and inefficient implementation would raise mitigation costs ( [[#Homma--2019|Homma et al. 2019]] ); conversely, ''co-benefits'' – most extensively estimated for air quality, valued at a few tens of USD per tCO 2 -eq across 16 studies ( [[#Karlsson--2020|Karlsson et al. 2020]] ) – complement global with additional local benefits (Table 1.2).

Whereas many of these factors affect primarily cost-benefit evaluation, discounting also determines the cost-effective trajectory: [[#Emmerling--2019|Emmerling et al. (2019)]] find that, for a remaining budget of 1000 GtCO 2 , reducing the discount rate from 5% to 2% would more than double current efforts, limit ‘overshoot’, greatly reduce a late rush to negative emissions, and improve intergenerational justice by more evenly distributing policy costs across the 21st century.

'''Table 1.2 | Potential for net co-benefits arising from synergies and trade-offs, opportuni''' '''ties and risks.'''

{| class="wikitable"
|-
!
! '''Positives'''

! '''Negatives'''

|-
| Broadly known (e.g., air pollution, distributional).

| Synergies

| Trade-offs

|-
| Deep uncertainties (e.g., radical innovations).

| Opportunities

| Risks

|-
|
|-
|
| Select options with maximum synergies, and foster and exploit opportunities.

| Ameliorate trade-offs (e.g., revenue redistribution), and minimise or allocate risks appropriately.

|-
| rowspan="2"|
| colspan="2"|
|-
| colspan="2"| '''Net co-benefits from appropriate mit''' '''igation choices'''

|}

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==== 1.7.1.2 Dynamic Efficiency and Uncertainty ====

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Care is required to clarify what is optimised ( [[#Dietz--2019|Dietz and Venmans 2019]] ). Optimising a path towards a given temperature goal ''by a fixed date'' (e.g., 2100) gives time-inconsistent results backloaded to large, last-minute investment in carbon dioxide removal (CDR). ‘Cost-effective’ optimisations generate less initial effort than ''equivalent'' cost-benefit models ( [[#Dietz--2019|Dietz and Venmans 2019]] ; [[#Gollier--2021|Gollier 2021]] ) as they do not incorporate benefits of reducing impacts earlier.

‘Efficient pathways’ are affected by inertia and innovation. Inertia implies amplifying action on long-lived investments and infrastructure that could otherwise lock-in emissions for many decades ( [[#Vogt-Schilb--2018|Vogt-Schilb et al. 2018]] ; [[#Baldwin--2020|Baldwin et al. 2020]] ). [[IPCC:Wg3:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg3:Chapter:Chapter-3#3.5|Section 3.5]] ) discusses interactions between near-, medium- and long-term actions in global pathways, particularly vis-à-vis inertia. Also, to the extent that early action induces low-carbon innovation, it ‘multiplies’ the optimal effort (for given damage assumptions), because it facilitates subsequent cheaper abatement. For example, a ‘learning-by-doing’ analysis concludes that early deployment of expensive PV was of net global economic benefit, due to induced innovation ( [[#Newbery--2018|Newbery 2018]] ).

Research thus increasingly emphasises the need to understand climate transformation in terms of dynamic, rather than static, efficiency ( [[#Gillingham--2018|Gillingham and Stock 2018]] ). This means taking account of inertia, learning and various additional sources of ‘path-dependence’. Including induced innovation in stylised IAMs can radically change the outlook ( [[#Acemoglu--2012|Acemoglu et al. 2012]] , 2016), albeit with limitations ( [[#Pottier--2014|Pottier et al. 2014]] ); many more detailed-process IAMs now do include endogenous technical change (as reviewed in [[#Yang--2018|Yang et al. 2018]] and [[#Grubb--2021b|Grubb et al. 2021b]] ) (Annex III).

These dynamic and uncertainty effects typically justify greater upfront effort ( [[#Kalkuhl--2012|Kalkuhl et al. 2012]] ; [[#Bertram--2015|Bertram et al. 2015]] ), including accelerated international diffusion ( [[#Schultes--2018|Schultes et al. 2018]] ), and strengthen optimal initial effort in cost-benefit models ( [[#Baldwin--2020|Baldwin et al. 2020]] ; [[#Grubb--2021b|Grubb et al. 2021b]] ). Approaches to risk premia common in finance would similarly amplify the initial mitigation effort, declining as uncertainties reduce ( [[#Daniel--2019|Daniel et al. 2019]] ).

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==== 1.7.1.3 Disequilibrium, Complex Systems and Evolutionary Approaches ====

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Other approaches to aggregate evaluation draw on various branches of intrinsically non-equilibrium theories (e.g., [[#Chang--2014|Chang 2014]] ). These including long-standing theories from the 1930s (e.g., Schumpeter 1934; [[#Keynes--1936|Keynes 1936]] ) to understand situations of structurally underemployed resources, potential financial instabilities ( [[#Minsky--1986|Minsky 1986]] ), and related economic approaches which emphasise time dimensions (e.g., recent reviews in [[#Legrand--2017|Legrand and Hagemann 2017]] ; [[#Stern--2018|Stern 2018]] ). More recently developing have been formal economic theories of endogenous growth building on, for example, [[#Romer--1986|Romer (1986)]] , and developments of Schumpeterian creative destruction ( [[#Aghion--2021|Aghion et al. 2021]] ) and evolutionary economic theories which abandon any notion of full or stable resource utilisation even as a reference concept ( [[#Nelson--1982|Nelson and Winter 1982]] ; [[#Freeman--1988|Freeman and Perez 1988]] ; [[#Carlsson--1991|Carlsson and Stankiewicz 1991]] ; Freeman and Louçã 2001; [[#Perez--2001|Perez 2001]] ).

The latter especially are technically grounded in complex system theories (e.g., [[#Arthur--1989|Arthur 1989]] , [[#Arthur--1999|1999]] ; [[#Beinhocker--2007|Beinhocker 2007]] ; [[#Hidalgo--2009|Hidalgo and Hausmann 2009]] ). These take inherently dynamic views of economies as continually evolving systems with continuously unfolding and path-dependent properties, and emphasise uncertainty in contrast to any predictable or default optimality. Such approaches have been variously applied in policy evaluation ( [[#Walton--2014|Walton 2014]] ; Moore et al. 2018), and specifically for global decarbonisation (e.g., [[#Barker--2014|]] [[#Barker--2014|Barker and Crawford-Brown 2014]] ) using global simulation models. Because these have no natural reference ‘least lost’ trajectory, they illustrate varied and divergent pathways and tend to emphasise the diversity of possibilities and relevant policies, particularly linked to innovation and potentially ‘sensitive intervention points’ ( [[#Farmer--2019|Farmer et al. 2019]] ) ( [[#1.7.3|Section 1.7.3]] ). They also illustrate that different representations of innovation and financial markets together can explain why estimated impacts of mitigation on GDP can differ very widely (potentially even in sign), between different model types (Chapter 15, [[#15.6.3|Section 15.6.3]] and Box 15.7).

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