Alexander Otto

Operationalizing climate targets under learning: An application of cost-risk analysis

Cost-Effectiveness Analysis (CEA) determines climate policies that reach a given climate target at minimum welfare losses. However, when applied to temperature targets under climate sensitivity uncertainty, decision-makers might be confronted with normatively unappealing negative expected values of future climate information or even infeasible solutions. To tackle these issues, Cost-Risk Analysis (CRA), that trades-off the costs for mitigating climate change against the risk of exceeding climate targets, has been proposed as an extension of CEA under uncertainty. Here we build on this proposition and develop an axiomatically sound CRA for the context of uncertainty and future learning. The main contributions of this paper are: (i) we show, that a risk-penalty function has to be non-concave to avoid counter-intuitive preferences, (ii) we introduce a universally applicable calibration of the cost-risk trade-off, and (iii) we implement the first application of CRA to a numerical integrated assessment model. We find that for a 2°-target in combination with a 66 % compliance level, the expected value of information in 2015 vs. 2075 is between 0.15 % and 0.66 % of consumption every year, and can reduce expected mitigation costs by about one third. (iv) Finally, we find that the relative importance of the economic over the risk-related contribution increases with the target probability of compliance.

Model structure in observational constraints on transient climate response

The transient climate response (TCR) is a highly policy-relevant quantity in climate science. We show that recent revisions to TCR in the IPCC 5th Assessment Report have more impact on projections over the next century than revisions to the equilibrium climate sensitivity (ECS). While it is well known that upper bounds on ECS are dependent on model structure, here we show that the same applies to TCR. Our results use observations of the planetary energy budget, updated radiative forcing estimates and a number of simple climate models. We also investigate the ratio TCR:ECS, or realised warming fraction (RWF), a highly policy-relevant quantity. We show that global climate models (GCMs) don’t sample a region of low TCR and high RWF consistent with observed climate change under all simple models considered. Whether the additional constraints from GCMs are sufficient to rule out these low climate responses is a matter for further research.

The role of infrastructure in macroeconomic growth theories

Investment in infrastructure is widely regarded as promoting economic growth. Moreover, infrastructure has a critical and often irreversible role in locking in patterns of development. Therefore, it is surprising that macroeconomic growth theories do not explicitly incorporate the notion of infrastructure systems. This lacuna is studied here, exploring the mechanisms via which infrastructure can impact an economys growth rate and how these mechanisms may be represented in existing macroeconomic growth theories. We confirm the important role of transport and digital communications infrastructure in reducing the cost of trade, thereby facilitating economies of scale as well as knowledge accumulation. We also consider the limiting effect that inadequate infrastructure can have on economic growth. Some, but not all, economic functions of infrastructure can be represented in current macroeconomic models due to their inherently non-spatial nature: Romer models include knowledge accumulation and energy economics tackles resource flows in the economy. New economic geography with growth, finally, allows for a representation of transport infrastructure due to a more spatial approach. Alterations to the standard investment feedback cycle, allowing for hybridisation of current theories, are identified. These provide the basis for a future programme of work which will include explicit implementations of infrastructure in macroeconomic growth theories.

A Quantified System-of-Systems Modeling Framework for Robust National Infrastructure Planning

National infrastructure (NI) systems (i.e., energy, transport, water, waste, and information and communications technology) provide essential services to the economy and contribute to human well-being. These systems have evolved over centuries, being planned and implemented piecewise, and are mostly managed in isolation from one another. Here, we argue that the growing interconnection between these systems and the convergent challenges ahead (i.e., demographic, technological, and climate change) call for an integrated “system-of-systems” approach to managing NI. Toward that end, we propose a modeling framework for the long-term (to 2100) simulation of NI system performance in a highly uncertain future. The approach is based on the assessment of the performance of infrastructure services in a wide range of possible future conditions. This robust optimization is used to identify cross-sectoral strategies that ensure satisfactory infrastructure performance. We demonstrate the framework using Great Britains NI as an example.

Climate system properties determining the social cost of carbon

The choice of an appropriate scientific target to guide global mitigation efforts is complicated by uncertainties in the temperature response to greenhouse gas emissions. Much climate policy discourse has been based on the equilibrium global mean temperature increase following a concentration stabilization scenario. This is determined by the equilibrium climate sensitivity (ECS) which, in many studies, shows persistent, fat-tailed uncertainty. However, for many purposes, the equilibrium response is less relevant than the transient response. Here, we show that one prominent policy variable, the social cost of carbon (SCC), is generally better constrained by the transient climate response (TCR) than by the ECS. Simple analytic expressions show the SCC to be directly proportional to the TCR under idealized assumptions when the rate at which we discount future damage equals 2.8%. Using ensemble simulations of a simple climate model we find that knowing the true value of the TCR can reduce the relative uncertainty in the SCC substantially more, up to a factor of 3, than knowing the ECS under typical discounting assumptions. We conclude that the TCR, which is better constrained by observations, less subject to fat-tailed uncertainty and more directly related to the SCC, is generally preferable to the ECS as a single proxy for the climate response in SCC calculations.

A National Model for Strategic Planning of Infrastructure Systems

Governments worldwide are paying increasing attention to the role of infrastructure systems in promoting economic growth and environmental sustainability. However, infrastructure planning and provision tend to be addressed in sector-specific silos which overlook the interdependencies between sectors and focuses upon the provision of projects rather than the performance of systems. In this paper, the authors report on the development of the National Infrastructure System Model (NISMOD) family of models and, in particular, the NISMOD-LP model, which is a national model of the long-term performance of infrastructure systems. NISMOD-LP is driven by high-resolution demographic projects and regional multi-sectoral economic scenarios. These provide the basis for scenarios of future demand for infrastructure services. Separate modules simulate the future capacity and performance of energy, transport, water, waste water and solid waste sectors. These different perspectives are integrated through a common architecture for sampling uncertainties, construction of cross-sectoral policy responses and visualisation of future infrastructure performance.