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Robust abatement pathways to tolerable climate futures require immediate global action

DOI:10.1038/s41558-019-0426-8
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Jonathan Lamontagne at Cornell University
Jonathan Lamontagne
Patrick Reed at Cornell University
Patrick Reed
G. Marangoni
G. Marangoni
G. Marangoni
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Klaus Keller at Dartmouth College
Klaus Keller
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Abstract and Figures

Disentangling the relative importance of climate change abatement policies from the human–Earth system (HES) uncertainties that determine their performance is challenging because the two are inexorably linked, and the nature of this linkage is dynamic, interactive and metric specific ¹ . Here, we demonstrate an approach to quantify the individual and joint roles that diverse HES uncertainties and our choices in abatement policy play in determining future climate and economic conditions, as simulated by an improved version of the Dynamic Integrated model of Climate and the Economy 2,3 . Despite wide-ranging HES uncertainties, the growth rate of global abatement (a societal choice) is the primary driver of long-term warming. It is not a question of whether we can limit warming but whether we choose to do so. Our results elucidate important long-term HES dynamics that are often masked by common time-aggregated metrics. Aggressive near-term abatement will be very costly and do little to impact near-term warming. Conversely, the warming that will be experienced by future generations will mostly be driven by earlier abatement actions. We quantify probabilistic abatement pathways to tolerable climate/economic outcomes 4,5 , conditional on the climate sensitivity to the atmospheric CO 2 concentration. Even under optimistic assumptions about the climate sensitivity, pathways to a tolerable climate/economic future are rapidly narrowing. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.
Joint sampling of HES uncertainties and the policy space a, Sampled global abatement trajectories, where colour corresponds to the year in which full abatement is reached (that is, level equal to one). DICE optimal, DICE business as usual (BAU) and a reliable 2 °C trajectory² are plotted for reference. b, Trade-off among 2100 warming, present value abatement costs and present value climate damages. c, Trade-off between present value abatement costs and 2100 warming, with the tolerable abatement-warming window highlighted. d, Trade-off between present value climate damages and 2100 warming, with the tolerable damages-warming window highlighted. Present value climate damages in b,d exclude 22 observations above 40% of the gross world product (GWP), but all observations are included in all computations.
Joint sampling of HES uncertainties and the policy space a, Sampled global abatement trajectories, where colour corresponds to the year in which full abatement is reached (that is, level equal to one). DICE optimal, DICE business as usual (BAU) and a reliable 2 °C trajectory² are plotted for reference. b, Trade-off among 2100 warming, present value abatement costs and present value climate damages. c, Trade-off between present value abatement costs and 2100 warming, with the tolerable abatement-warming window highlighted. d, Trade-off between present value climate damages and 2100 warming, with the tolerable damages-warming window highlighted. Present value climate damages in b,d exclude 22 observations above 40% of the gross world product (GWP), but all observations are included in all computations.
… 
Time-aggregated and time-varying sensitivities for climate damages, abatement costs and warming a, The fraction of variability in present value (PV) damages, present value abatement costs and 2100 warming explained by dominant sources of uncertainty. b–d, The evolution of the fraction of variability described by HES uncertainties, abatement actions and their interactions over the period 2020–2150: time varying control of climate damage (b), abatement cost (c) and warming (d). The ‘Climate damage function’ label refers to the sum of the individual effects of the three damage function parameters. The hatched areas 1 and 2 in c correspond to the combined effects of the two total factor productivity parameters and two production carbon intensity parameters, respectively. All parameters were sampled independently.
Time-aggregated and time-varying sensitivities for climate damages, abatement costs and warming a, The fraction of variability in present value (PV) damages, present value abatement costs and 2100 warming explained by dominant sources of uncertainty. b–d, The evolution of the fraction of variability described by HES uncertainties, abatement actions and their interactions over the period 2020–2150: time varying control of climate damage (b), abatement cost (c) and warming (d). The ‘Climate damage function’ label refers to the sum of the individual effects of the three damage function parameters. The hatched areas 1 and 2 in c correspond to the combined effects of the two total factor productivity parameters and two production carbon intensity parameters, respectively. All parameters were sampled independently.
… 
Abatement pathways with associated probabilities of achieving tolerable climate/economic conditions for an assumed climate sensitivity a–c, Plots of the pathways for very low (1.94 K), low (2.51 K) and medium (3.00 K) climate sensitivities. The colour within each pathway indicates the probability of successfully achieving a tolerable climate/economic future, conditional on the climate sensitivity. The last year by which full abatement must be reached to ensure at least a 50% chance of success is also reported for each climate sensitivity. DICE optimal, DICE business as usual and a reliable 2 °C trajectory² are also plotted for reference.
Abatement pathways with associated probabilities of achieving tolerable climate/economic conditions for an assumed climate sensitivity a–c, Plots of the pathways for very low (1.94 K), low (2.51 K) and medium (3.00 K) climate sensitivities. The colour within each pathway indicates the probability of successfully achieving a tolerable climate/economic future, conditional on the climate sensitivity. The last year by which full abatement must be reached to ensure at least a 50% chance of success is also reported for each climate sensitivity. DICE optimal, DICE business as usual and a reliable 2 °C trajectory² are also plotted for reference.
… 
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Letters
https://doi.org/10.1038/s41558-019-0426-8
1Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA. 2School of Civil and Environmental Engineering, Cornell
University, Ithaca, NY, USA. 3Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, USA. 4Department of
Geosciences, The Pennsylvania State University, University Park, PA, USA. 5Department of Earth and Planetary Sciences, Rutgers University, Piscataway,
NJ, USA. 6Present address: Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy.
*e-mail: jonathan.lamontagne@tufts.edu
Disentangling the relative importance of climate change
abatement policies from the human–Earth system (HES)
uncertainties that determine their performance is challeng-
ing because the two are inexorably linked, and the nature of
this linkage is dynamic, interactive and metric specific1. Here,
we demonstrate an approach to quantify the individual and
joint roles that diverse HES uncertainties and our choices in
abatement policy play in determining future climate and eco-
nomic conditions, as simulated by an improved version of the
Dynamic Integrated model of Climate and the Economy2,3.
Despite wide-ranging HES uncertainties, the growth rate of
global abatement (a societal choice) is the primary driver of
long-term warming. It is not a question of whether we can
limit warming but whether we choose to do so. Our results
elucidate important long-term HES dynamics that are often
masked by common time-aggregated metrics. Aggressive
near-term abatement will be very costly and do little to
impact near-term warming. Conversely, the warming that will
be experienced by future generations will mostly be driven by
earlier abatement actions. We quantify probabilistic abate-
ment pathways to tolerable climate/economic outcomes4,5,
conditional on the climate sensitivity to the atmospheric CO2
concentration. Even under optimistic assumptions about the
climate sensitivity, pathways to a tolerable climate/economic
future are rapidly narrowing.
Policy discussions of climate change mitigation and adaptation
benefit from estimates of the potential extent and timing of future
climate change, and the potential damages and costs this change
might induce. Providing decision-makers with these estimates is
exceedingly difficult because the scientific community lacks a com-
plete understanding of the complex interactions between human
systems and the climate6. Integrated assessment models (IAMs)
approximate these interactions in a computational framework7.
IAMs have been used to create successive generations of global
change scenarios that are ubiquitous in planning and evaluation
applications across many disciplines, including the representa-
tive concentration pathways8 and shared socioeconomc pathways9.
Recent work has considered the sensitivity of IAM results to HES
uncertainties1 and structural model uncertainty across IAMs10,
but little attention has been paid to the interaction between HES
uncertainties and abatement policy11. Here we contribute a unified
framework to evaluate the joint impact of HES uncertainties and
abatement actions on climate–economic outcomes. By considering
the evolution of sensitivities and impacts over century-scale peri-
ods, our framework elucidates important long-term HES dynamics,
and identifies robust abatement pathways to achieve tolerable
climate/economic futures12 (that is, a tolerable policy window).
We utilize an improved version of the Dynamic Integrated
model of Climate and the Economy (DICE)3. DICE is a simple,
though widely used13, IAM that links a global representation of the
economy with a simplified climate emulator. We adopt DICE as a
parsimonious and archetypal surrogate for more complex IAMs
that represent multi-sector and regional dynamics, and are used to
generate shared scenarios such as the representative concentration
pathways and shared socioeconomic pathways (see Weyant14 for a
review). Following previous work2,15 we improve the climate module
in DICE by replacing it with the DOEClim model16. DOEClim is an
energy-balance model that has representation of land, the tropo-
sphere and ocean dynamics, with shallow mixing and deep diffu-
sivity layers. We convert the DICE–DOEClim model from a policy
optimization to a policy simulation model to enable simultaneous
exploration of the full abatement strategy space and parametric
uncertainties2 (see Methods for details). Of course, our findings are
subject to the ability of the DICE–DOEClim model to capture the
key dynamics of the integrated HES and will inevitably be subject
to the limitations of that model. Despite recent advances17, exten-
sive sensitivity and uncertainty analyses of more complex IAMs that
give a better representation of regional and multi-sector dynamics
can pose intractable computational challenges. Thus, we adopt the
parsimonious, globally aggregated DICE model.
As an illustrative example, we assume the global CO2 emissions
abatement level grows exponentially over time, until full abate-
ment is achieved. This is a special case of a more flexible functional
form used in earlier work11. We sample the exponential growth
rate to generate abatement trajectories that achieve full abatement
uniformly between 2030 and 2150 (Fig. 1a and see Methods). We
assume that the year 2030 is the earliest opportunity for the energy
system to become fully carbon neutral11 with a maximum relative
abatement growth rate of 26.3% per year, whereas 2150 is assumed
to be the first year of negative emissions3. The resulting emissions
trajectories are broadly consistent with the representative concen-
tration pathway and shared socioeconomic pathway trajectories
(see Supplementary Figs 2–6).
We sampled 24 different sources of HES uncertainty, as repre-
sented in DICE–DOEClim, along with the growth rate of global
abatement to generate 5,200,000 alternative states of the world
(see Methods for details). The 24 HES uncertainties cover most
of the parameters of the DICE–DOEClim model, including those
that define population growth, total factor productivity, economic
impacts of climate change, the cost of carbon-free technologies, the
Robust abatement pathways to tolerable climate
futures require immediate global action
J.R.Lamontagne 1*, P.M.Reed2, G.Marangoni 3,6, K.Keller 3,4 and G.G.Garner 5
Corrected: Publisher Correction
NATURE CLIMATE CHANGE | VOL 9 | APRIL 2019 | 290–294 | www.nature.com/natureclimatechange
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Article
  • Dec 2014
This paper reviews applications of benefit-cost analysis (BCA) in climate policy assessment at the US national and global scales. Two different but related major application types are addressed. First there are global-scale analyses that focus on calculating optimal global carbon emissions trajectories and carbon prices that maximize global welfare. The second application is the use of the same tools to compute the social cost of carbon (SCC) for use in US regulatory processes. The SCC is defined as the climate damages attributable to an increase of one metric ton of carbon dioxide emissions above a baseline emissions trajectory that assumes no new climate policies. The paper describes the three main quantitative models that have been used in the optimal carbon policy and SCC calculations and then summarizes the range of results that have been produced using them. The results span an extremely broad range (up to an order of magnitude) across modeling platforms as well as across the plausible ranges of input assumptions to a single model. This broad range of results sets the stage for a discussion of the five key challenges that face BCA practitioners participating in the national and global climate change policy analysis arenas: (1) including the possibility of catastrophic outcomes; (2) factoring in equity and income distribution considerations; (3) addressing intertemporal discounting and intergenerational equity; (4) projecting baseline demographics, technological change, and policies inside and outside the energy sector; and (5) characterizing the full set of uncertainties to be dealt with and designing a decision-making process that updates and adapts new scientific and economic information into that process in a timely and productive manner. The paper closes by describing how the BCA models have been useful in climate policy discussions to date despite the uncertainties that pervade the results that have been produced.
Exploring How Changing Monsoonal Dynamics and Human Pressures Challenge Multi-Reservoir Management for Flood Protection, Hydropower Production and Agricultural Water Supply
Article
  • Jun 2018
Multireservoir systems require robust and adaptive control policies capable of managing hydroclimatic variability and human demands across a range of time scales. This is especially true for river basins with high intraannual and interannual variability, such as monsoonal systems that need to buffer against seasonal droughts while also managing extreme floods. Moreover, the timing, intensity, duration, and frequency of these hydrologic extremes may evolve with deeply uncertain changes in socioeconomic and climatic pressures. This study contributes an innovative method for exploring how possible changes in the timing and magnitude of the monsoonal cycle impact the robustness of reservoir operating policies designed assuming stationary hydrologic and socioeconomic conditions. We illustrate this analysis on the Red River basin in Vietnam, where reservoirs and dams serve as important sources of hydropower production, multisectoral water supply, and flood protection for the capital city of Hanoi. Applying our scenario discovery approach, we find that reservoir operations designed assuming stationarity provide robust hydropower performance in the Red River but that increased mean streamflow, amplification of the within-year monsoonal cycle, and increased interannual variability all threaten their ability to manage flood risk. Additionally, increased agricultural water demands can only be tolerated if they are accompanied by greater mean flow, exacerbating food-flood trade-offs in the basin. These findings highlight the importance of exploring the impacts of a wide range of deeply uncertain socioeconomic and hydrologic factors when evaluating system robustness in monsoonal river basins, considering in particular both lower-order moments of annual streamflow and intraannual monsoonal behavior.
Mitigation gambles: Uncertainty, urgency and the last gamble possible
Article
  • May 2018
A rejection by current generations of more ambitious mitigation of carbon emissions inflicts on future generations inherently objectionable risks about which they have no choice. Any gains through savings from less ambitious mitigation, which are relatively minor, would accrue to current generations, and all losses, which are relatively major, would fall on future generations. This mitigation gamble is especially unjustifiable because it imposes a risk of unlimited losses until carbon emissions cease. Ultimate physical collapses remain possible. Much more ominous is prior social collapse from political struggles over conflicting responses to threatened physical collapse. The two most plausible objections to the thesis that less ambitious mitigation is unjustifiable rely, respectively, on the claim that negative emissions will allow a later recovery from a temporary overshoot in emissions and on the claim that ambitious mitigation is incompatible with poverty alleviation that depends on inexpensive fossil fuels. Neither objection stands up. Reliance on negative emissions later instead of ambitious mitigation now permits the passing of tipping points for irreversible change meanwhile, and non-carbon energy is rapidly becoming price competitive in developing countries like India that are committed to poverty alleviation. This article is part of the themed issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels’.
Open discussion of negative emissions is urgently needed
Article
Although nearly all 2 °C scenarios use negative CO2 emission technologies, only relatively small investments are being made in them, and concerns are being raised regarding their large-scale use. If no explicit policy decisions are taken soon, however, their use will simply be forced on us to meet the Paris climate targets.
Less Than 2 °C Warming by 2100 Unlikely
Article
  • Jul 2017
The recently published Intergovernmental Panel on Climate Change (IPCC) projections to 2100 give likely ranges of global temperature increase in four scenarios for population, economic growth and carbon use. However, these projections are not based on a fully statistical approach. Here we use a country-specific version of Kaya's identity to develop a statistically based probabilistic forecast of CO 2 emissions and temperature change to 2100. Using data for 1960-2010, including the UN's probabilistic population projections for all countries, we develop a joint Bayesian hierarchical model for Gross Domestic Product (GDP) per capita and carbon intensity. We find that the 90% interval for cumulative CO 2 emissions includes the IPCC's two middle scenarios but not the extreme ones. The likely range of global temperature increase is 2.0-4.9 °C, with median 3.2 °C and a 5% (1%) chance that it will be less than 2 °C (1.5 °C). Population growth is not a major contributing factor. Our model is not a business as usual' scenario, but rather is based on data which already show the effect of emission mitigation policies. Achieving the goal of less than 1.5 °C warming will require carbon intensity to decline much faster than in the recent past.