Editing Test:SRCCL/Chapter-1 (section)

== 1.3.5 Economics of land-based mitigation pathways: Costs versus benefits of early action under uncertainty ==

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The overarching societal costs associated with GHG emissions and the potential implications of mitigation activities can be measured by various metrics (cost-benefit analysis, cost effectiveness analysis) at different scales (project, technology, sector or the economy) (IPCC 2018 <sup>[[#fn:r794|794]]</sup> ) (Section 1.4). The social cost of carbon (SCC) measures the total net damages of an extra metric tonne of CO <sub>2</sub>  emissions due to the associated climate change (Nordhaus 2014 <sup>[[#fn:r795|795]]</sup> ; Pizer et al. 2014 <sup>[[#fn:r796|796]]</sup> ). Both negative and positive impacts are monetised and discounted to arrive at the net value of consumption loss. As the SCC depends on discount rate assumptions and value judgements (e.g., relative weight given to current vs future generations), it is not a straightforward policy tool to compare alternative options. At the sectoral level, marginal abatement cost curves (MACCs) are widely used for the assessment  of costs related to GHG emissions reduction. MACCs measure the cost of reducing one more GHG unit and are either expert-based or model-derived and offer a range of approaches and assumptions on discount rates or available abatement technologies (Kesicki 2013 <sup>[[#fn:r797|797]]</sup> ). In land-based sectors, Gillingham and Stock (2018) <sup>[[#fn:r798|798]]</sup> reported short-term static abatement costs for afforestation of between 1 and 10 USD2017 per tCO <sub>2</sub> , soil management at 57 and livestock management at 71 USD2017 per tCO <sub>2</sub> . MACCs are more reliable when used to rank alternative options compared to a baseline (or business as usual) rather than offering absolute numerical measures (Huang et al. 2016 <sup>[[#fn:r799|799]]</sup> ). The economics of land-based mitigation options encompass also the “costs of inaction” that arise either from the economic damages due to continued accumulation of GHGs in the atmosphere and from the diminution in value of ecosystem services or the cost of their restoration where feasible (Rodriguez-Labajos 2013 <sup>[[#fn:r800|800]]</sup> ; Ricke et al. 2018 <sup>[[#fn:r801|801]]</sup> ). Overall, it remains challenging to estimate the costs of alternative mitigation options owing to the context – and scale-specific interplay between multiple drivers (technological, economic, and socio-cultural) and enabling policies and institutions (IPCC 2018 <sup>[[#fn:r802|802]]</sup> ) (Section 1.4).

The costs associated with mitigation (both project-linked such as capital costs or land rental rates, or sometimes social costs) generally increase with stringent mitigation targets and over time. Sources of uncertainty include the future availability, cost and performance of technologies (Rosen and Guenther 2015 <sup>[[#fn:r803|803]]</sup> ; Chen et al. 2016 <sup>[[#fn:r804|804]]</sup> ) or lags in decision-making, which have been demonstrated by the uptake of land use and land utilisation policies (Alexander et al. 2013 <sup>[[#fn:r805|805]]</sup> ; Hull et al. 2015 <sup>[[#fn:r806|806]]</sup> ; Brown et al. 2018b <sup>[[#fn:r807|807]]</sup> ). There is growing evidence of significant mitigation gains through conservation, restoration and improved land management practices (Griscom et al. 2017 <sup>[[#fn:r808|808]]</sup> ; Kindermann et al. 2008 <sup>[[#fn:r809|809]]</sup> ; Golub et al. 2013 <sup>[[#fn:r810|810]]</sup> ; Favero et al. 2017 <sup>[[#fn:r811|811]]</sup> ) (Chapters 4 and 6), but the mitigation cost efficiency can vary according to region and specific ecosystem (Albanito et al. 2016 <sup>[[#fn:r812|812]]</sup> ). Recent model developments that treat process-based, human–environment interactions have recognised feedbacks that reinforce or dampen the original stimulus for land-use change (Robinson et al. 2017 <sup>[[#fn:r813|813]]</sup> ; Walters and Scholes 2017 <sup>[[#fn:r814|814]]</sup> ). For instance, land mitigation interventions that rely on large-scale, land-use change (e.g., afforestation) would need to account for the rebound effect (which dampens initial impacts due to feedbacks) in which raising land prices also raises the cost of land-based mitigation (Vivanco et al. 2016 <sup>[[#fn:r815|815]]</sup> ). Although there are few direct estimates, indirect assessments strongly point to much higher costs if action is delayed or limited in scope ( ''medium confidence'' ). Quicker response options are also needed to avoid loss of high-carbon ecosystems and other vital ecosystem services that provide multiple services that are difficult to replace (peatlands, wetlands, mangroves, forests) (Yirdaw et al. 2017 <sup>[[#fn:r816|816]]</sup> ; Pedrozo-Acuña et al. 2015 <sup>[[#fn:r817|817]]</sup> ). Delayed action would raise relative costs in the future or could make response options less feasible ( ''medium confidence'' ) (Goldstein et al. 2019 <sup>[[#fn:r818|818]]</sup> ; Butler et al. 2014 <sup>[[#fn:r819|819]]</sup> ).

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