Induced Technological Change in a Limited Foresight Optimization Model

The threat of global warming calls for a major transformation of the energy system the coming century. Modeling technological change is an important factor in energy systems modeling. Technological change may be treated as induced by climate policy or as exogenous. We investigate the importance of induced technological change (ITC) in GET-LFL, an iterative optimization model with limited foresight that includes learning-by-doing. Scenarios for stabilization of atmospheric CO2 concentrations at 400, 450, 500 and 550 ppm are studied. We find that the introduction of ITC reduces the total net present value of the abatement cost over this century by 3-9% compared to a case where technological learning is exogenous. Technology specific polices which force the introduction of fuel cell cars and solar PV in combination with ITC reduce the costs further by 4-7% and lead to significantly different technological solutions in different sectors, primarily in the transport sector.Energy system model, Limited foresight, Climate policy, Endougenous learning, Technological lock-in

DICE and the Carbon Budget for Ambitious Climate Targets

The Dynamic Integrated Climate-Economy (DICE) model is one of the most influential Integrated Assessment Models available. Its founder Professor William Nordhaus was recently awarded Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel due to his pioneering work on the economics of climate change. In a recent paper in American Economic Journal: Economic Policy, Nordhaus uses the model to conclude that a 2.5\ub0C target is almost out of reach. In this paper, we update DICE 2016 R2 with state-of-the-art models of the carbon cycle, heat uptake into the oceans, and the role of non-CO2 forcers. We find that the allowable remaining carbon budget (over the period 2015–2100) to meet a 2.5\ub0C target to be 2,360 GtCO2 whereas the estimate obtained using DICE 2016 R2 is about 460 GtCO2. Nordhaus\u27s estimate of the remaining carbon budget for this target is hence five times lower than estimates made by our updated DICE. We also compare our results with estimates by the Intergovernmental Panel on Climate Change (IPCC), and find our results to be in line with the carbon budgets presented in IPCC SR 1.5. We explain the reasons behind the difference between our result and that of Nordhaus and propose that an updated climate module in DICE is warranted

Financing the Transition Toward Carbon Neutrality—an Agent-Based Approach to Modeling Investment Decisions in the Electricity System

Transitioning to a low-carbon electricity system requires investments on a very large scale. These investments require access to capital, but that access can be challenging to obtain. Most energy system models do not (explicitly) model investment financing and thereby fail to take this challenge into account. In this study, we develop an agent-based model, where we explicitly include power sector investment financing. We find that different levels of financing constraints and capital availabilities noticeably impact companies\u27 investment choices and economic performances and that this, in turn, impacts the development of the electricity capacity mix and the pace at which CO2 emissions are reduced. Limited access to capital can delay investments in low-carbon technologies. However, if the financing constraint is too relaxed, the risk of going bankrupt can increase. In general, companies that anticipate carbon prices too high above or too far below the actual development, along with those that use a low hurdle rate, are the ones that are more likely to go bankrupt. Emissions are cut more rapidly when the carbon tax grows faster, but there is overall a greater tendency for agents to go bankrupt when the tax grows faster. Our energy transition model may be particularly useful in the context of the least financially developed markets

Modelling the Transition towards a Carbon-Neutral Electricity System—Investment Decisions and Heterogeneity

To achieve the climate goals of the Paris Agreement, greenhouse gas emissions from the electricity sector must be substantially reduced. We develop an agent-based model of the electricity system with heterogeneous agents who invest in power generating capacity under uncertainty. The heterogeneity is characterised by the hurdle rates the agents employ (to manage risk) and by their expectations of the future carbon prices. We analyse the impact of the heterogeneity on the transition to a low carbon electricity system. Results show that under an increasing CO2\ua0tax scenario, the agents start investing heavily in wind, followed by nuclear and to some extent in natural gas fired power plants both with and without carbon capture and storage as well as biogas fired power plants. However, the degree to which different technologies are used depend strongly on the carbon tax expectations and the hurdle rate employed by the agents. Comparing to the case with homogeneous agents, the introduction of heterogeneity among the agents leads to a faster CO2\ua0reduction. We also estimate the so called “cannibalisation effect” for wind and find that the absolute value of wind does not drop in response to higher deployment levels, but the relative value does decline

Rio Isn’t All Lost

Carbon emissions have increased by 50 percent since the first Earth Summit meeting in Rio de Janeiro, but the rapid development of wind and solar energy offers hope. Published in the New York Times on June 18th, 2012

Meeting global temperature targets-the role of bioenergy with carbon capture and storage

In order to meet stringent temperature targets, active removal of CO2 from the atmosphere may be required in the long run. Such negative emissions can be materialized when well-performing bioenergy systems are combined with carbon capture and storage (BECCS). Here, we develop an integrated global energy system and climate model to evaluate the role of BECCS in reaching ambitious temperature targets. We present emission, concentration and temperature pathways towards 1.5 and 2 degrees C targets. Our model results demonstrate that BECCS makes it feasible to reach temperature targets that are otherwise out of reach, provided that a temporary overshoot of the target is accepted. Additionally, stringent temperature targets can be met at considerably lower cost if BECCS is available. However, the economic benefit of BECCS nearly vanishes if an overshoot of the temperature target is not allowed. Finally, the least-cost emission pathway over the next 50 years towards a 1.5 degrees C overshoot target with BECCS is almost identical to a pathway leading to a 2 degrees C ceiling target

A Virtualized Infrastructure for IVR Applications as Services

Interactive Voice Response (IVR) applications are ubiquitous nowadays. Automated attendant, bank teller and automated surveys are a few of many applications requiring IVR capabilities. Cloud computing is a paradigm gaining a lot of momentum. It has three major service models: Infrastructure as a service – IaaS, Platform as a service – PaaS, and Software as a Service – SaaS. It offers also several inherent benefits such as scalability, resource efficiency and easy introduction of new functionality. However, very few, if any, IVR applications are offered today in cloud-based settings despite of all its potential benefits. This thesis deals with IaaS. Accordingly, we propose a novel architecture for a virtualized IVR infrastructure that relies on RESTFul Web services. The architecture proposes IVR substrates that are virtualized, composed, and assembled on the fly to build IVR applications. As a proof of concept, we have implemented an IaaS prototype on which performance measurements have been done to evaluate our architecture concept. In addition, a simple proof of concept PaaS consisting of a graphical user interface (GUI)has been built to enable the development and management of simple IVR services in the SaaS layer

Exploring the competition between variable renewable electricity and a carbon-neutral baseload technology

In this paper we explore the competition between variable renewable energy sources (VRE) and a carbon-neutral baseload technology in the transition to a low-carbon power system. We study a stylized system subject to a gradually increasing carbon tax using an agent-based model where agents are power companies investing in new capacity. The agents make predictions of the profitability of different investment options. Five electricity generating technologies are available in the model: coal, gas, wind, solar PV and a more expensive carbon-neutral baseload technology. We compare the output from our model with a corresponding optimization model. We present two main findings: (1)\ua0installed capacity of VRE initially increases with a carbon tax. However, once the carbon tax has reached a certain level the installed capacity of VRE starts to decline due to competition with the stylized carbon-neutral baseload technology. (2)\ua0With limited foresight we find that the model underinvests (first 25 years) in wind and then overinvests in wind compared to the optimal solution. The reasons for these dynamic phenomena are explained and an extensive sensitivity analysis is carried out

Investment dynamics in the energy sector under carbon price uncertainty and risk aversion

Decarbonizing the electricity system in order to contribute to climate change mitigation is a key policy goal. Yet, uncertain political and economic conditions (e.g., electricity prices) create uncertainty for energy companies. The dynamics of carbon price developments and aversion to uncertainty may have decisive impacts on companies’ investment decisions and thus environmental and distributional outcomes. In this paper, we incorporate a dynamic portfolio approach in a simulation model of investments in the electricity sector to explore and disentangle the impacts of both uncertainty and risk aversion on companies’ investment decisions. We find that policy uncertainty and risk aversion tend to delay the transition to a low-carbon energy system, with higher levels of either factor causing even further delays. However, the mechanism for the delay depends on how risk aversion is modeled, e.g. whether companies are averse to losses, or variances or if they use a higher discount rate in uncertain situations. Employing the loss-averse approach, the company prefers technology with a low likelihood of negative returns for the portfolio; meanwhile, the mean-variance approach indicates an aversion to both positive and negative deviations in returns. With a high discount rate, investors favor less capital-intensive technologies. To account for the impact of risk aversion in policy framework we, therefore, need more empirical work on understanding these behavioral traits of energy companies