An Oligopoly Game of CDR Strategy Deployment in a Steady-State Net-Zero Emission Climate Regime

In this paper, we propose a simple oligopoly game model to represent the interactions between coalitions of countries in deploying carbon dioxide removal (CDR) strategies in a steady-state net-zero emission climate regime that could take place by the end of the twenty-first century. The emission quotas and CDR activities obtained in the solution of this steady-state model could then be used as a target for end-of-period conditions in a dynamic integrated assessment analysis studying the transition to 2100. More precisely, we analyze a steady-state situation where m coalitions exist and behave as m players in a game of supplying emission rights on an international emission trading system. The quotas supplied by a coalition must correspond to the amount of CO₂ captured through CDR activities in the corresponding world region. We use an extension of the computable general equilibrium model GEMINI-E3 to calibrate the payoff functions and compute an equilibrium solution in the noncooperative game.

     

Robust Energy Portfolios Under Climate Policy and Socioeconomic Uncertainty

Concerning the stabilization of greenhouse gases, the UNFCCC prescribes measures to anticipate, prevent, or minimize the causes of climate change and mitigate their adverse effects. Such measures should be cost-effective and scientific uncertainty should not be used as a reason for postponing them. However, in the light of uncertainty about climate sensitivity and other underlying parameters, it is difficult to assess the importance of different technologies in achieving robust long-term climate risk mitigation. One example currently debated in this context is biomass energy, which can be used to produce both carbon-neutral energy carriers, e.g., electricity, and at the same time offer a permanent CO₂ sink by capturing carbon from the biomass at the conversion facility and permanently storing it. We use the GGI Scenario Database IIASA [3] as a point of departure for deriving optimal technology portfolios across different socioeconomic scenarios for a range of stabilization targets, focusing, in particular, on new, low-emission scenarios. More precisely, the dynamics underlying technology adoption and operational decisions are analyzed in a real options model, the output of which then informs the portfolio optimization. In this way, we determine the importance of different energy technologies in meeting specific stabilization targets under different circumstances (i.e., under different socioeconomic scenarios), providing valuable insight to policymakers about the incentive mechanisms needed to achieve robust long-term climate risk mitigation.     

Vector-Borne Diseases, Development & Climate Change

Vector-borne diseases are feared to extend their range in a future where global warming has occurred. There is considerable concern about scourges such as malaria re-invading currently temperate regions and reaching into higher altitudes in Africa. In this paper we examine the various factors thought to determine potential infectivity of malaria, and its actual outbreak in the context of a dynamic integrated assessment model. We quantify: (i) the role of demographics in placing a larger population in harms way; (ii) the role of climate change in increasing the potential geographic range and severity of the risk of infection; and (iii) the role of economic and social development in limiting the occurrence of malaria. We then explore the climate and economic implications of various climate policies in their effectiveness to limit potential infectivity of malaria. In illustration of these issues we present the climate-related and economics-related impacts of unilateral CO₂ control by OECD on incidence of malaria in non-OECD nations. The model presented here, although highly stylized in its representation of socio-economic factors, provides strong evidence of the role of socio-economic factors in determination of malaria incidence. The case study offers insights into unintended adverse consequences of well-meaning climate policies. This revised version was published online in July 2006 with corrections to the Cover Date.     

Regional impact analysis of climate change on natural and managed forests in the Federal State of Brandenburg, Germany

A methodology for regional application of forest simulation models has been developed as part of an assessment of possible climate change impacts in the Federal state of Brandenburg (Germany). Here we report on the application of a forest gap model to analyse the impacts of climate change on species composition and productivity of natural and managed forests in Brandenburg using a statistical method for the development of climate scenarios. The forest model was linked to a GIS that includes soil and groundwater table maps, as well as gridded climate data with a resolution of 10 × 10 km and simulated a steady-state species composition which was classified into forest types based on the biomass distribution between species. Different climate scenarios were used to assess the sensitivity of species composition to climate change. The simulated forest distribution patterns for current climate were compared with a map of Potential Natural Vegetation (PNV) of Brandenburg.In order to analyse the possible consequences of climate change on forest management, we used forest inventory data to initialize the model with representative forest stands. Simulation experiments with two different management strategies indicated how forest management could respond to the projected impacts of climate change. The combination of regional analysis of natural forest dynamics under climate change with simulation experiments for managed forests outlines possible trends for the forest resources. The implications of the results are discussed, emphasizing the regional differences in environmental risks and the adaptation potentials of forestry in Brandenburg.     

The evolution of city-scale GHG emissions inventory methods: A systematic review

The capacity of cities to act on climate change mitigation is essential to fulfil the Paris Agreement target. In order to do so, cities should establish an effective climate policy which requires, as a first step, a complete greenhouse gas (GHG) emissions inventory. The accurate city-scale GHG inventory enables cities to develop, implement and track climate solution measures, mainly those related to transportation. The compilation of a city-scale GHG inventory requires a standardized method and up-to-date activity data. This systematic review critically examines 40 articles over the past 20 years to (1) identify city-scale GHG inventory methods being applied worldwide, (2) evaluate how these methods are evolving, (3) elaborate how emissions from transport sector are being estimated, and (4) determine what data types and sources of transport-related data are being used. The review was limited to articles that addressed the process of compilation of a GHG inventory. The results demonstrate that city-scale GHG inventory methods evolved from the Intergovernmental Panel on Climate Change (IPCC) Guidelines to a variety of GHG accounting methods that offer levels of complexity to estimate city-scale emissions. Although GHG inventory methods for city-scale have advanced over the years, almost one third of the articles reviewed were focused on the proposal of a GHG inventory framework, adjusting current methods to each city's reality or proposing new ones. The majority of the cities analysed lack local transport-related data to measure GHG emissions based on the bottom-up approach. Yet, more than 40% of the articles managed to deliver the bottom-up inventory using a diversity of data types and sources. This review shows that there is still a path to achieve a globally compatible method. This would require a joint effort between researchers and city authorities to make international protocols more compliant to each city's data availability along with the improvement of cities data collection.     

The Use of Measurement Programmes and Models to Assess Concentrations Next to Major Roads in Urban Areas

This paper shows that monitoring campaigns in association withmodelling, particularly detailed roadside modelling, are auseful and cost effective method of assessing air quality. Itproposes that an approach based on detailed monitoringassociated with concurrent meteorological data can be used incombination with a roadside model to assess concentrations at other locations near a road and in other weather conditions. Plans for improving air quality can also be assessed in simpleways. The need for complex models based on detailed emissioninventories is avoided.Two examples from streets in London and Paris are presented inwhich measurements of CO, NO_x and volatile organic compoundshave been made. The measurements are interpreted usingcorrelations between pollutant concentrations and with windspeed, and by using the USEPA CALINE4 model. It is demonstratedthat air quality measurements can be used to assess the validityof road traffic emission factors.     

Sensitizing Land Uses to Control Motor Vehicular Air Pollution: a Case Study of Lahore in Connection with Paris

Several factors influence air pollution, but effect of land uses is pivotal and has direct consequences on growth and development of a city. In order to assess consequent effects of land uses on air pollution, it is important to transform a set of variables into significant components by using principal component analysis. These components help to develop an effective model between air pollution and land uses by application of regression analysis. Lahore, titled as “Paris of the East,” has some featured commonalities with Paris and was jointly selected for research to adopt common policies and measures to combat air pollution. A two-step cluster analysis has been applied to testify resemblance of Paris to towns of Lahore as a cluster. This paper is trying to develop a calibrated model which helps to estimate about air pollution at a significant level with only one independent variable. Furthermore, it helps to control air pollution through sensitization of land uses in Lahore.     

“Emission game”: some applications of the theory of games to the problem of CO2 emission

If there are no doubts that we must reduce the total emission of carbon dioxide, then the problem of how much different countries should be allowed to contribute to this amount remains a serious one. We suggest this problem to be considered as a non-antagonistic game (in Germeier's sense). A game of this kind is called an “emission game”. Suppose that there are n independent actors (countries or regions), each of them releasing a certain amount of CO₂ per year (in carbon units) into the atmosphere, and that the emission would be reduced by each actor. Each actor has his own aim: to minimise the loss in the Gross Domestic Product (GDP) caused by the reduction of emissions. On the other hand, taking into account that it is impossible to estimate more or less precisely the impact of the climate change on GDP for each country today, a common strategy will be to reduce the climate change. Since one of the main leading factors in global warming is the greenhouse effect, then the common aim will be to reduce the sum of emissions. This is a typical conflict situation. How to resolve it? We can weigh the “egoistic” and “altruistic” criteria for each actor introducing the so-called “coefficients of egoism”. This coefficient is very large, if the actor uses a very egoistic strategy, and conversely, if the actor is a “super-altruist”, then the corresponding coefficient is very small. Using these coefficients we get the general solution of the game in a form of some Pareto's equilibrium. The solution is stable and efficient.     

Symmetric Renegotiation-Proof Climate Agreements

Environmental Modeling & Assessment - In this paper, we study the impacts and effects of the number of countries and green technology on climate agreements. In a repeated climate game...     

Climate change decision-support and the tolerable windows approach

The Tolerable Windows Approach (TWA) to Integrated Assessments (IA) of global warming is based on external normative specifications of tolerable sets of climate impacts as well as proposed emission quotas and policy instruments for implementation. In a subsequent step, the complete set of admissible climate protection strategies which are compatible with these normative inputs is determined by scientific analysis. In doing so, minimum requirements concerning global and national greenhouse gas emission paths can be determined. In this paper we present the basic methodological elements of TWA, discuss its relation to more conventional approaches to IA like cost–benefit analyses, and present some preliminary results obtained by a reduced-form climate model.     

Climatic Implications of Revised IPCC Emissions Scenarios, the Kyoto Protocol and Quantification of Uncertainties

In December 1997, the United Nations Framework Convention on Climate Change (FCCC) adopted the Kyoto Protocol. This paper describes a framework that models the climatic implications of this international agreement, using Monte Carlo simulations and the preliminary Intergovernmental Panel on Climate Change emissions scenarios (SRES). Emissions scenarios (including intervention scenarios), climate sensitivity, and terrestrial carbon sink are the key sampled model parameters. This framework gives prior probability distributions to these parameters and, using a simple climate model, posterior distributions of global temperature change are determined for the future. Our exercise showed that the Kyoto Protocol's effectiveness will be mostly dependent upon which SRES world evolves. In some worlds the Protocol decreases the warming considerably but in others it is almost irrelevant. We exemplified this approach with a current FCCC issue, namely “hot air”. This modelling framework provides a probabilistic assessment of climate policies, which can be useful for decision-makers involved in global climate change management. This revised version was published online in July 2006 with corrections to the Cover Date.     

Oracle-based optimization applied to climate model calibration

In this paper, we show how oracle-based optimization can be effectively used for the calibration of an intermediate complexity climate model. In a fully developed example, we estimate the 12 principal parameters of the C-GOLDSTEIN climate model by using an oracle-based optimization tool, Proximal-ACCPM. The oracle is a procedure that finds, for each query point, a value for the goodness-of-fit function and an evaluation of its gradient. The difficulty in the model calibration problem stems from the need to undertake costly calculations for each simulation and also from the fact that the error function used to assess the goodness-of-fit is not convex. The method converges to a ‘best fit’ estimate over 10 times faster than a comparable test using the ensemble Kalman filter. The approach is simple to implement and potentially useful in calibrating computationally demanding models based on temporal integration (simulation), for which functional derivative information is not readily available.     

Modelling of Materials Policies

This paper discusses the REAP1 model and its application for the analysis of CO₂ reduction and waste management policies for Japanese petrochemicals. The pros and cons of this modelling approach in comparison to other tools is elaborated. This is followed by a discussion of the model code and the modelling results. The results show that CO₂ policies can have significant impacts on waste flows and waste policies can have significant CO₂ benefits. As a consequence both effects must be considered in policy assessment. Pricing instruments are recommended instead of regulations because of the complex physical relations in the materials life cycle that extend beyond sector boundaries. A taxation approach is superior to a subsidy approach because rebound effects can be avoided.     

Integrated Assessment Modeling for Global Climate Change: An Infinite Horizon Optimization Viewpoint

In this paper one uses an infinite time horizon optimal control paradigm to deal with three important issues in global climate change integrated assessment, namely the equitable treatment of all the generations involved, the representation of technical progress, and the uncertainty concerning technological progress and climate change processes. The notion of economic sustainability is associated with the concept of turnpike in infinite horizon optimization models. The issue of deciding on the proper discount rate is addressed in this context and a method is proposed to reconcile long term sustainability and short term time preference for current generations. One also formulates a model where environmental damage and/or technical progress are represented as stochastic jump processes. One calls this random evolution a mode switching process. In this context, sustainability is represented by a family of turnpikes, and the economy will be driven from one steady state to the other as the modes switch. These concepts are illustrated on the DICE model and their possible implementation in other types of integrated assessment models is discussed.     

Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment

In this paper, we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in a harmonized way stochastic programming in a large-scale bottom-up (BU) model and Monte Carlo simulation in a large-scale top-down (TD) model. The BU model we use is the TIMES Integrated Assessment Model, which is run in a stochastic programming version to provide a hedging emission policy to cope with the uncertainty characterizing climate sensitivity. The TD model we use is the computable general equilibrium model GEMINI-E3. Through Monte Carlo simulations of randomly generated uncertain parameter values, one provides a stochastic micro- and macro-economic analysis. Through statistical analysis of the simulation results, we analyse the impact of the uncertainties on the policy assessment.     

CO2 Capture and Storage with Leakage in an Energy-Climate Model

Geological CO₂ capture and storage (CCS) is among the main near-term contenders for addressing the problem of global climate change. Even in a baseline scenario, with no comprehensive international climate policy, a moderate level of CCS technology is expected to be deployed, given the economic benefits associated with enhanced oil and gas recovery. With stringent climate change control, CCS technologies will probably be installed on an industrial scale. Geologically stored CO₂, however, may leak back to the atmosphere, which could render CCS ineffective as climate change reduction option. This article presents a long-term energy scenario study for Europe, in which we assess the significance for climate policy making of leakage of CO₂ artificially stored in underground geological formations. A detailed sensitivity analysis is performed for the CO₂ leakage rate with the bottom-up energy systems model MARKAL, enriched for this purpose with a large set of CO₂ capture technologies (in the power sector, industry, and for the production of hydrogen) and storage options (among which enhanced oil and gas recovery, enhanced coal bed methane recovery, depleted fossil fuel fields, and aquifers). Through a series of model runs, we confirm that a leakage rate of 0.1%/year seems acceptable for CCS to constitute a meaningful climate change mitigation option, whereas one of 1%/year is not. CCS is essentially no option to achieve CO₂ emission reductions when the leakage rate is as high as 1%/year, so more reductions need to be achieved through the use of renewables or nuclear power, or in sectors like industry and transport. We calculate that under strict climate control policy, the cumulative captured and geologically stored CO₂ by 2100 in the electricity sector, when the leakage rate is 0.1%/year, amounts to about 45,000 MtCO₂. Only a little over 10,000 MtCO₂ cumulative power-generation-related emissions are captured and stored underground by the end of the century when the leakage rate is 1%/year. Overall marginal CO₂ abatement costs increase from a few €/tCO₂ today to well over 150 €/tCO₂ in 2100, under an atmospheric CO₂ concentration constraint of 550 ppmv. Carbon costs in 2100 turn out to be about 40 €/tCO₂ higher when the annual leakage rate is 1%/year in comparison to when there is no CO₂ leakage. Irrespective of whether CCS deployment is affected by gradual CO₂ seepage, the annual welfare loss in Europe induced by the implementation of policies preventing “dangerous anthropogenic interference with the climate system” (under our assumption, implying a climate stabilisation target of 550 ppmv CO₂ concentration) remains below 0.5% of GDP during the entire century.

Global Emission Ceiling Versus International Cap and Trade: What is the Most Efficient System to Solve the Climate Change Issue?

We model the climate change issue as a pollution control game with the purpose of comparing two possible departures from the business as usual (BAU) where countries noncooperatively choose their emission levels. In the first scenario, players have to agree on a global emission cap (GEC) that is enforced by a uniform taxation scheme. They still behave strategically when choosing emission levels but are now subject to the coupled constraint imposed by the cap. The second scenario consists of the implementation of an international cap and trade (ICT) system. In this case, players decide on their emission quotas, and emission trading is allowed. A three heterogenous player quadratic game serves as a basis for the analysis. When the cap is binding, among all the coupled constraints Nash equilibria, we select a particular normalized equilibrium by solving a variational inequality. Comparing the normalized equilibrium with the Nash equilibria of the BAU and the ICT, we first show that if the cap is appropriately chosen, then the GEC system improves all players’ payoffs, relative to the BAU. The GEC system may thus be unanimously approved whereas the ICT is not, because moving from the BAU to the ICT is costly for one player. Second, for some values of the cap, all players get a higher payoff under the GEC than under the ICT. Therefore, the GEC outperforms the ICT both in terms of feasibility and efficiency.     

Optimisation analysis and integrated models of the enhanced greenhouse effect

The issues surrounding the anticipated impacts of the enhanced greenhouse effect are likely to form a significant part of the research activities in the coming years. In this paper we have adopted a control theoretic approach to the analysis of one of the world's best known integrated models of the enhanced greenhouse effect: the Dutch IMAGE 1.0 (Integrated Model to Assess the Greenhouse Effect) model. The paper demonstrates that optimisation methodologies can be applied to integrated models to enhance their interpretative power. This is accomplished by providing a mechanism whereby optimal emission allocation strategies can be formulated from existing models. One result of particular interest is that the analysis confirms that the earlier human input into the climate system can be stabilised, the higher the levels of CO₂ emissions can be permitted and still achieve specific long-term environmental target.     

A Stochastic Dynamic Game of Carbon Emissions Trading

This paper proposes a computable stochastic equilibrium model to represent the possible competition between Russia and China on the international market of carbon emissions permits. The model includes a representation of the uncertainty concerning the date of entry of developing countries (e.g., China) on this market in the form of an event tree. Assuming that this date of entry is an uncontrolled event, we model the competition as a dynamic game played on an event tree and we look for a solution called S-adapted equilibrium. We compare the solution obtained from realistic data describing the demand curves for permits and the marginal abatement cost curves in different countries, under different market and information structures: (i) Russia's monopoly, (ii) Russia–China competition in a deterministic framework, (iii) Russia–China competition in a stochastic framework. The results show the possible impact of this competition on the pricing of emissions permits and on the effectiveness of Kyoto and post-Kyoto agreements, without a US participation.     

Coupling Climate Damages and GHG Abatement Costs in a Linear Programming Framework

The paper discusses the coupling of non-linear non-convex damage costs due to climate change with a cost-efficiency analysis based on a technical-economic linear programming model like MARKAL and studies the implications for the computation of cooperative and non-cooperative solutions. Our empirical analysis of climate damages based on different world emissions levels and paths prove (a) that the dependency of damages on the trajectory of emissions may be neglected, so that the only relevant variables are the cumulative emissions in each country, and (b) that a linear relationship links regional damages and cumulative global emissions. Based on these results, cooperative and non-cooperative equilibria can be much more easily calculated by solving local optimization problems in a case where international trade effects of GHG policies are neglected: given the linearity of damage functions, each country chooses its non-cooperative strategy by considering only the part of its own damage cost due to its own emissions; in the cooperative case, each country takes into account its contribution to the damages done to all countries. Of course, any cost-benefit conclusion that will be produced by this approach is fully dependent on the damage functions. Also, this approach may be extended to the case where trade effects are modeled.