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In this paper, we present a global and multi-timescale approach for assessing energy tran- sition policies aiming at fully renewable generation in power systems of non-interconnected areas, typically islands or isolated regions. The approach links together three dynamic mod- els:(i) a capacity expansion model, ETEM-SG, proposes an investment and production plan for typical days, at the horizon 2030; (ii) a simplified dispatch model is used to validate the production plan for a full year data of weather and demand variations and; (iii) a static & dynamic power system analysis is used to assess the stability of the new power network for fast occurring events like, e.g., a sudden reduction of renewable production. The proposed three-stage approach generates a least-cost long-term investment planning that ensures a supply-demand balance at an hourly time-step and power network statibility at a few mil- lisecond time scale. The presentaion is based on a case study fully described in a report [1] made with ADEME, the French Agency for ecological transition, for the French island La R ́eunion. It shows how a reliable 100% renewable power supply is reachable by 2030, in this area.

In this paper, we propose an approach for coupling a power network dispatch model, which is part of a long-term multi-energy model, with Wardrop or Mean-Field-Game (MFG) equilibrium models that represent the demand response of a large population of small “prosumers” connected at the various nodes of the electricity network. In a deterministic setting, the problem is akin to an optimization problem with equilibrium constraints taking the form of variational inequalities or nonlinear complementarity conditions. In a stochastic setting, the problem is formulated as a robust optimization with uncertainty sets informed by the probability distributions resulting from an MFG equilibrium solution. Preliminary numerical experimentations, using heuristics mimicking standard price adjustment techniques, are presented for both the deterministic and stochastic cases.

This paper proposes an assessment of long-term climate strategies for oil- and gas-producing countries—in particular, the Gulf Cooperation Council (GCC) member states—as regards the Paris Agreement goal of limiting the increase of surface air temperature to 2°C by the end of the twenty-first century. The study evaluates the possible role of carbon dioxide removal (CDR) technologies under an international emissions trading market as a way to mitigate welfare losses. To model the strategic context, one assumes that a global cumulative emissions budget will have been allocated among different coalitions of countries—the GCC being one of them—and the existence of an international emissions trading market. A meta-game model is proposed in which deployment of CDR technologies as well as supply of emission rights are strategic variables and the payoffs are obtained from simulations of a general equilibrium model. The results of the simulations indicate that oil and gas producing countries and especially the GCC countries face a significant welfare loss risk, due to “unburnable oil” if a worldwide climate regime as recommended by the Paris Agreement is put in place. The development of CDR technologies, in particular direct air capture (DAC) alleviates somewhat this risk and offers these countries a new opportunity for exploiting their gas reserves and the carbon storage capacity offered by depleted oil and gas reservoirs. This paper was made possible by NPRP grant number 10-0212-170447 from the Qatar National Research Fund (a member of Qatar Foundation). The first author acknowledges support provided by FONDECYT 1190325 and by ANILLO ACT192094, Chile. The fourth author also received support provided by the IFP School Chair on Carbon Management (CARMA). The last author also received support provided by the H2020 European Commission Project “PARIS REINFORCE” under grant agreement no. 820846.

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.

In the meta-modeling approach, one builds a numerically tractable dynamic optimization or game model in which the parameters are identified through statistical emulation of a detailed large scale numerical simulation model. In this paper, we show how this approach can be used to assess the economic impacts of possible climate policies compatible with the Paris Agreement. One indicates why it is appropriate to assume that an international carbon market, with emission rights given to different groups of countries will exist. One discusses the approach to evaluate correctly abatement costs and welfare losses incurred by different groups of countries when implementing climate policies. Finally, using a recently proposed meta-model of game with a coupled constraint on a cumulative CO2 emissions budget, we assess several new scenarios for possible fair burden sharing in climate policies compatible with the Paris Agreement.

This paper gives a detailed description of the ETEM-SG model, which provides a simulation of the long -term development of a regional multi-energy system in a smart city environment. The originality of the modeling comes from a representation of the power distribution constraints associated with intermittent and volatile renewable energy sources connected at the transmission network like, e.g., wind farms, or the distribution networks like, e.g., roof top PV panels). The model takes into account the options to optimize the power system provided by grid-friendly flexible loads and distributed energy resources, including variable speed drive powered CHP micro-generators, heat pumps, and electric vehicles. One deals with uncertainties in some parameters by implementing robust optimization techniques. A case study, based on the modeling of the energy system of the “Arc Lemanique” region, shows on simulation results, the importance of introducing a representation of power distribution constraints, and options in a regional energy model.

In this paper we propose a very simple steady-state game model that represents schematically interactions between coalitions of countries in achieving a necessary net-zero emission of GHGs in order to stabilize climate over the long term. We start from a situation where m coalitions exist and behave as m players in a game of sharing a global emission budget that can only be maintained by negative emission activities. We compare a fully “cooperative” solution with a Nash equilibrium solution implemented through an international emission trading scheme. We characterize the fully cooperative and Nash equilibrium solutions for this game in a deterministic context.

We use the flexible model coupling technology known as the bespoke framework generator to link established existing modules representing dynamics in the global economy (GEMINI-E3), the energy system (TIAM-WORLD), the global and regional climate system (MAGICC6, PLASIM-ENTS and ClimGEN), the agricultural system, the hydrological system and ecosystems (LPJmL), together in a single integrated assessment modelling (IAM) framework, building on the pre-existing framework of the Community Integrated Assessment System. Next, we demonstrate the application of the framework to produce policy-relevant scientific information. We use it to show that when using carbon price mechanisms to induce a transition from a high-carbon to a low-carbon economy, prices can be minimised if policy action is taken early, if burden sharing regimes are used, and if agriculture is intensified. The approach has created long-lasting coupled models available for use in research for the future at a secure and user-friendly web portal.

A linear programming framework is proposed to model distribution network characteristics, and market clearing processes for flexible load and distributed energy resources providing reserve and reactive power compensation. One shows that the Nash equilibrium solution representing the interaction between utility and customers for demand response and distributed reserve transactions can be approximated by a linear program when the players (i.e. the customers) are numerous and tend to become infinitesimal. Then a linear program is shown to reveal the market prices, corresponding to the marginal cost for the utility. The goal in developing this model is to provide a new module for a regional long term model of development of smart energy systems. This module will then introduce in the modeling of energy transition, the new options and constraints that are provided by a penetration of renewables with the possibility of implementing distributed markets for demand response and system services permitted by the development of the cyber-physical layer. A case study of a potential smart urban distribution network in Europe is carried out and provides numerical results that illustrate the proposed framework.

This paper deals with the modeling of a regional energy system, based on the example of Qatar, over a time horizon of 35 years or more, taking into account the possible transition toward a smart city paradigm of the Doha region and the probable climate regime that could result from COP21 and the coming rounds of international climate negotiations. The impact on the energy system of a transition toward smart city could take many forms: (i) by providing two-way communications between consumers and electricity producers, the smart city will foster demand response and distributed energy resource management; (ii) new forms of demand for energy services will materialize in the transport, housing and service sectors that could contribute to lower the environmental footprint of the energy sector; (iii) the smart-grid connection of distributed energy resources and the possibility to provide secondary reserve through grid storage and distributed system service, like e.g. reactive power compensation, will facilitate the penetration of variable renewable energy in the energy system. Qatar is the world-class leader in the export of liquefied natural gas (LNG). It has also access to crude oil reserves. As the economy of Qatar developed, as in other Gulf countries, the energy system expanded assuming an unlimited supply of very low cost fossil fuels. However, the very economic success of the gas and oil exporting countries in the Gulf region is starting to impose a limit on this form of development of the energy system. The share of gas that is used in the region and thus not available for export is increasing sharply. The local pollution due to the burning of fossil fuels in transport, residential, service and industry sectors, has to be curbed. Finally, at COP21 in Paris, more than 160 nations, including Qatar, have agreed to reduce GHG emissions in order to reach a goal of limiting the temperature change at the end of 21st century at 1.5oC. At COP21 Qatar has made a commitment to reduce its per-capita emission level, which is one of the highest in the world. To deal with these challenges, the Gulf countries count on a substitution from fossil fuel source to variable renewable ones (as well as some extent to nuclear power generation in UAE, KSA and Iran). This is exemplified by the location of IRENA headquarters[1] in Abu Dhabi, inaugurated in May 2015. Indeed, harnessing wind and solar energy sources seems promising in the region, with some caveats due to intermittency of wind blowing and sand storms reducing the efficiency of solar panels. There is another game changing phenomenon represented by the drive toward smart cities. The cities in Gulf countries are very modern and they are ready to embrace the smart city paradigm and concept. Abu Dhabi and more generally the UAE, Qatar and Saudi Arabia have announced pilot projects for fully integrated “smart-city” districts. The development of the “internet of things”, which characterizes smart cities, will translate for the energy system into a development of smart-grid connected, distributed energy resources and this should help tremendously the penetration of variable renewable energy sources. The model ETEM-SC[2] used in this research has been designed to help in assessing the policies that could be used to define, up to the horizon 2050, an environmentally friendly energy system in Qatar. ETEM-SC is a technology rich cost-efficiency model, which permits an exploration of the future of the regional energy system under different environmental and economic constraints. The model is derived from the shell-model ETEM (Energy-Technology-Environment-Model), developed at ORDECSYS[3]. This shell has a structure of a linear program, as in MARKAL, MESSAGE or TIMES, with an extensive description of technology and energy choices in the region into consideration. The new additions that have been made for our purpose (SC for Smart Cities) concern: (i) the modeling of constraints and options at the power distribution level, due to the introduction of smart grids, distributed energy resource and demand response; (ii) the consideration of the impact on demand for energy services of the development of a smart city environment, and (iii) the explicit consideration of uncertainty in the scenario building, through the use of stochastic programming and robust optimization techniques. Some technical aspects of these modeling advances have been described in two parent papers (Babonneau et al. (2016a) and (2016b)). The present research will focus on the description of the scenarios for Qatar that result from the use of this model. These scenarios are based on publicly available data and should not be considered as a representation of the energy policy of the state of Qatar. The purpose of this exercise is to demonstrate the potential offered by ETEM-SC to assess the possible penetration of variable renewable energy in regional energy systems with smart-city development, in order to reduce drastically the GHG emissions. [1] The International Renewable Energy Agency. www.irena.org [2] Energy Technology Environment Model-Smart-Cities. [3] See www.ordecsys.com for details.

In this paper we propose a non-cooperative meta-game approach to designing and assessing climate agreements among 28 European countries that will be compatible with the EU 2050 climate target. Our proposed game model is identified through statistical emulation of a large set of numerical simulations performed with the computable general equilibrium model GEMINI-E3. In this game, the players are the 28 European countries, the payoffs are related to welfare losses due to abatements and the strategies correspond to the supply of emission rights on the European carbon market. We show it is possible to design a fair burden-sharing rule that equalizes welfare losses between countries to approximately 1.2 % of their discounted household consumption. The associated European CO2 price in 2050 reaches $1100, a figure in line with previous studies. Lastly, the paper discusses various implementation issues of these types of negotiations and evaluates the cost of non-cooperation among EU countries.

Cet article présente le modèle d’optimisation technico-économique ETEM-AR consacré à l’analyse du futur d’un système de production et d’utilisation de l’énergie à l’échelle régionale. Le modèle prend en compte aussi bien l’atténuation de l’émission de gaz à effet de serre que l’adaptation aux impacts du changement climatique. L’article montre qu’il est possible de combiner l’analyse des politiques d’atténuation et celle des politiques d’adaptation dans des modèles technico-économiques du système énergétique. Le recours à la simulation de scénarios contrastés, à la programmation stochastique et à l’optimisation robuste, permet de prendre en compte les incertitudes associées aux caractéristiques du système énergétique et aux impacts futurs du changement climatique. Le modèle est illustré par une application à la région française Midi-Pyrénées.

In this paper, we propose a differential game model with a coupled constraint to represent the possible effects of climate agreements between industrialized, emerging and developing countries. Each group of countries is represented by an economic growth model where two different types of economies, called, respectively, ‘low-carbon’ and ‘carbon,’ can co-exist, each of which having different productivities of capital and of emissions due to energy use. We assume that each group of countries participating in the negotiations has identified a damage function, which determines a loss of GDP due to warming and has also a possibility to invest in a capital permitting adaptation to climate changes. The climate agreements we consider have two main components: (1) They define a global emission budget for a commitment period and impose it as a limit on cumulative emissions during that period; (2) they distribute this global budget among the different coalitions of countries taking part in the agreement. This implies that the game has now a coupled constraint for all participants in the negotiations. The outcome of the agreement is therefore obtained as a generalized or ‘Rosen’ equilibrium which can be selected among a whole manifold of such solutions. We show that the family of Nash equilibria in the games obtained through a distribution of the total budget among the different parties corresponds to the manifold of normalized equilibria. We then propose an equity criterion to determine a fair division of this total emission budget or equivalently to select a proper weighting for a normalized equilibrium.

This paper deals with a family of dynamic game models that represent schematically the interaction between groups of countries in achieving the necessary limitation of carbon atmospheric emissions in order to control climate change. We start from a situation where m coalitions of countries exist and behave as m players in a game of sharing a global emission budget through the establishment of an international emissions trading system. We characterize the Nash equilibrium solutions for this game in a deterministic context. Through a simple replication schemes, we increase the number of players, each one becoming infinitesimal, and we characterize the limit games thus obtained. A stochastic version is proposed for this class of models, and the limit games are characterized, using the recently defined concept of mean field games.

Consequential Life Cycle Assessment (C-LCA) is a “system modelling approach in which activities in a product system are linked so that activities are included in the product system to the extent that they are expected to change as a consequence of a change in demand”. Hence, C-LCA focuses on micro-economic actions linked to macro-economic consequences, by identifying the (marginal) suppliers and technologies prone to be affected by variable scale changes in the demand of a product. Detecting the direct and indirect environmental effects due to changes in the production system is not an easy task. Hence, researchers have combined the consequential perspective with different econometric models. Therefore, the aim of this study is to assess an increase in biocrops cultivation in Luxembourg using three different consequential modelling approaches to understand the benefits, drawbacks and assumptions linked to each approach as applied to the case study selected. Firstly, a partial equilibrium (PE) model is used to detect changes in land cultivation based on the farmers’ revenue maximisation. Secondly, another PE model is proposed, which considers a different perspective aiming at minimising a total adaptation cost (so-called opportunity cost) to satisfy a given new demand of domestically produced biofuel. Finally, the consequential system delimitation for agricultural LCA approach, as proposed by Schmidt (Int J Life Cycle Assess 13:350–364, 2008), is applied. The two PE models present complex shifts in crop rotation land use changes (LUCs), linked to the optimisation that is performed, while the remaining approach has limited consequential impact on changes in crop patterns since the expert opinion decision tree constitutes a simplification of the ongoing LUCs. However, environmental consequences in the latter were considerably higher due to intercontinental trade assumptions recommended by the experts that were not accounted for in the economic models. Environmental variations between the different scenarios due to LUCs vary based on the different expert- or computational-based assumptions. Finally, environmental consequences as compared with the current state-of-the-art are lame due to the limited impact of the shock within the global trade market. The use of several consequential modelling approaches within the same study may help widen the interpretation of the advantages or risks of applying a specific change to a production system. In fact, different models may not only be good alternatives in terms of comparability of scenarios and assumptions, but there may also be room for complementing these within a unique framework to reduce uncertainties in an integrated way.

In this paper we show how to robustify the computation of equilibria in two integrated assessment models for climate change. Both models deal with the optimal timing of a transition to a ‘clean’ economy where a technology with low emissions but high energy cost can be used in the production process. The game represents the competition between industrialized and developing countries. A cost-benefit approach is implemented with an economic loss factor that represents the damages due to climate change. In the first model one assumes that both technologies, ‘dirty’ and ‘clean’ are available, but the economic loss factor is very uncertain. In the second model one assumes that the ‘clean’ technology is not yet available and some R&D investment must be made to get the technology breakthrough permitting its penetration. In this second model, formulated in continuous time, the jump rate of the controlled stochastic process describing the effect of R&D investment on the probability of breakthrough, is also considered as very uncertain. In both models we introduce a concept of α-robust equilibrium, where the robustification is achieved through the use of ambiguous probability distributions with a Kullback-Leibler divergence cost structure for the worst case choice by Nature.

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.

After several decades of rapid technological advancement and economic growth, levels of pollution and environmental degradation are rising all over the world. Unilateral response on the part of one single country would hardly be effective. Collaborations in pollution abatement, R&D in technology development, emissions reduction, and subsidization for technology transfer offer the best promise to assist the development of an effective means to halt the accelerating trend of environmental deterioration. International cooperative agreements have to be negotiated to deal with the global environmental problem. In these negotiations, intraand inter-generational cost sharing, compensation for technology transfer, gains distribution, and sustainability are among the important issues to be resolved and pose an ensemble of problems that are best addressed through strategic modelling. Applications of operations research methodologies—such as optimization techniques, control theory, and interactive strategic analysis—are crucial to the development of collaborative environmental management paradigms and the formulation of viable solutions. This special volume of the Annals of Operations Research contains a collection of frontier research papers on operations research techniques and practicable solutions in environmental modeling and collaborative management. In the paper of Finus et al., the stability of self-enforcing climate agreements under a sequential coalition formation process of heterogeneous and farsighted players is studied. In particular, players can make proposals that are either accepted or countered by alternative

The theory of systems is presented as an operational theory that can be used to modelize the structure of technical or social systems. Simulation and optimalization techniques can be used to operate this model on a computer for analyzing different scenarios. A recent study of the economy of energy and of the particular role of hydrogen is presented as an example of such a use of the theory of systems in the studies on prospective.

Cet article concerne la modélisation d’une situation de concurrence imparfaite, face à une possibilité de percée technologique induisant des modifications importantes des coûts de production et, par le fait même, déclenchant une phase de domination de la firme détenant l’avantage technologique. Ce modèle utilise le formalisme de la théorie des jeux différentiels déterministes par morceaux. Entre deux instants aléatoires correspondant aux périodes d’occurence de percées technologiques le jeu est totalement déterministe. Au moment d’une percée technologique, le comportement des firmes en présence peut être modifié, le mode de jeu passant de l’équilibre de Cournot à la solution de Stackelberg et vice versa. Les conditions d’équilibre sont établies et une première interprétation économique de ces conditions est donnée. Quelques possibilités de prolongement de ce modèle sont aussi discutées., This paper deals with a duopoly model in a situation where technological breakthroughs are possible. The innovation process is described as a continuous-time Markov chain with jump depending on level of R&D capital accumulated through investment by each firm. When a firm is the first to have access to the innovation it gains a market advantage and becomes a leader à la Stackelberg, until the competing firm gains itself access to its own improved production process. In this paper we show how the recently developed theory of piece wise deterministic differential games permits the analysis of this economic situation. Equilibrium conditions are formulated in the frameworks of dynamic programming and control theory. An economic interpretation of these conditions is provided.

This paper deals with the design of equilibrium solutions with coupled constraints in dynamic games of greenhouse gas (GHG) emissions abatement. Self enforcing International Environmental Agreements (IEA) among different groups of countries call for Nash equilibrium solutions when the abatement strategies of the countries are defined. In this paper we study the effect of having another party, like e.g. the United Nations which would impose to all players a coupled constraint on the total emissions allowed over the 21st century, or on the concentration of carbon reached at the end of the century. We show, using different formulations of environmental game, that the normalized equilibria obtained under a coupled constraint on emissions or concentration is close to Pareto optimality. This gives a clue on the way the post Kyoto negotiations could yield an agreement which could be close to efficiency.

A stochastic control model is proposed as a paradigm for the design of optimal timing of greenhouse gas (GHG) emission abatement. The resolution of uncertainty concerning climate sensitivity and the technological breakthrough providing access to a carbon-free production economy are modeled as controlled stochastic jump processes. The optimal policy is characterized using the dynamic programming solution to a piecewise deterministic optimal control problem. A numerical illustration is developed with a set of parameters calibrated on recently proposed models for integrated assessment of climate policies. The results are interpreted and the insights they provide on the timing issue of climate policy are discussed.

This paper is concerned with a repair vs. replacement problem for a system observed in continuous time and subject to random failure. When the system fails, a decision is taken, based on the observed values of the (random) repair cost and the system's age, to either perform a minimal repair or to replace the system by a new one. At any time, the system may also be replaced preventively, and the aim of the decision maker is to minimize the total expected discounted cost. A dynamic programming approach is proposed to solve this problem. Sufficient conditions under which a repair-limit rule is optimal are given. An efficient algorithm is devised for the computation of such an optimal policy, and a numerical illustration is worked out.

This paper deals with two classes of large-scale Markov decision models which can be used, respectively, for hospital admission scheduling and multicomponent system maintenance. These models are called population processes as they describe a set of individuals represented by a set of independent replicas of the same underlying stochastic core process. Two examples are completely worked out, showing the potential utilization of this modelling approach.

Models of optimal control of two-species ecological systems are considered on an infinite time horizon. Conditions are obtained under which the optimal exploitation of the system is stabilizing. Thus, a stationary state toward which all optimal trajectories must converge is defined. This extends to this class of models, the co-called turnpike property of optimal economic growth models.

This paper proposes a computational game-theoretic model for the international negotiations that should take place at the end of the period covered by the Kyoto protocol. These negotiations could lead to a self-enforcing agreement on a burden sharing scheme given the necessary global emissions limit that will be imposed when the real extent of climate change is known. The model assumes a non-cooperative behavior of the parties except for the fact that they will be collectively committed to reach a target on total cumulative emissions by the year 2050. The concept of normalized equilibrium, introduced by J.B. Rosen for concave games with coupled constraints, is used to characterize a family of dynamic equilibrium solutions in an m-player game where the agents are (groups of) countries and the payoffs are the welfare gains obtained from a Computable General Equilibrium (CGE) model. The model deals with the uncertainty about climate sensitivity by computing an S-adapted equilibrium. These equilibria are computed using an oracle-based method permitting an implicit definition of the payoffs to the different players, obtained through simulations performed with the global CGE model GEMINI-E3.

In order to assess the cooperation between industrialized and developing countries in the design of a comprehensive worldwide climate policy to limit the global long-term temperature increase to 2°C, we developed an iterative procedure linking the global technology-rich optimization energy model TIAM-WORLD and the global general equilibrium model GEMINI-E3. The coupling methodology combines the precise representation of technology choices and their impact on climate change, with a coherent representation of the welfare gains or losses associated with the techno-economic and energy choices, especially in terms of trade effects. The assessment of alternative global, partially cooperative agreements is used to illustrate the interest of such a coupling approach; it appears that an important added value of such a coupling relates to the evaluation of the trade effects of climate policies on energy-intensive products. Furthermore, in order to achieve safe levels of climate change, drastic technology breakthroughs and rigorous implementations are required as soon as possible, especially in large emitting countries, and in all sectors of the economy— focussing only on emission reductions in the power sector is not sufficient. Moreover, some risks of delocalization of gas extraction activities exist in the case of partial agreements, but they result in a limited carbon leakage thanks to the reduction of oil extraction in all cases.

In this two-part paper we evaluate the effect of “endogenizing” technological learning and strategic behavior of agents in economic models used to assess climate change policies. In the first part we show the potential impact of RD in the second case the agents (countries) reply optimally to the actions decided by the other agents by exploiting their strategic advantages. Simulations based on integrated assessment models illustrate the approaches. These studies have been conducted under the Swiss NCCR-Climate program.

In this paper we further explore the properties and computational methods of intergenerational equilibrium solutions for a class of multi-generation stochastic games that are inspired by the intergenerational equity issues typically found in cost-benefit analysis for climate change policies. We first extend previous results by showing that discounting over time and over generations yields to an intergenerational equilibrium which is equivalent to the use of a lower discount rate. We then propose a model that encompasses the different formulations proposed before to represent intergenerational equilibria in the presence of selfishness. We propose a numerical method to compute these equilibria and we test it on an adaptation of the DICE94 model proposed by Nordhaus to do cost-benefit analysis of climate policies.

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 Fbest 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.

We report on reduced-order photo-chemical model (TAPOM-Lite) designed for calculating ozone levels for varying urban scenarios and calibrated for the city of Geneva, Switzerland. The TAPOM-Lite model is an atmospheric-environmental model designed to be incorporated into a larger energy–economy–environment (E3) model and run in an optimization framework. The motivation for the TAPOM-Lite model was the design of a fast ozone calculator needed for the CPU-intensive optimization environment where many iterations are potentially required before a best solution is found. Three principal advances in this model are (1) a simplified chemical scheme for O3 production, (2) linearization of the O3 production function needed for the overseeing linear optimization program, and (3) the extraction of gradient (sensitivity) information calculated from the O3 production function which in turn is needed as “directional” information in the optimization method. Results show that the TAPOM-Lite model is consistent with the ful...

This paper presents the implementation of a large-scale systems analytic method in a model permitting the assessment of air quality policies in urban regions. This method is based on the use of a convex optimization technique called the analytic center cutting plane method. One realizes the coupling of a photochemical model (TAPOM-Lite), used to simulate ozone creation and dispersion in the region under study, and of a technoeconomic model (MARKAL-Lite) that represents the technology and energy uses by different economic sectors in the same region. Although the models correspond to different time and space scales, one realizes the coupling through a series of approximating convex reduced order optimization problems with constraints that are implicitly defined by the photochemical and the technoeconomic model, respectively. The implementation of the method in a case study corresponding to the region of Geneva, Switzerland is described.

We report on a reduced-order photo-chemical model (TAPOM-Lite) designed for calculating ozone levels for varying urban scenarios and calibrated for the city of Geneva, Switzerland. The TAPOM-Lite model is an atmospheric-environmental model designed to be incorporated into a larger energy-economy-environment (E 3) model and run in an optimization framework. The motivation for the TAPOM-Lite model was the design of a fast ozone calculator needed for the CPU intensive optimization environment where many iterations are potentially required before a best solution is found. Three principal advances in this model are (1) a simplified chemical scheme for O 3 production, (2) linearization of the O 3 production function needed for the overseeing linear optimization program, and (3) the extraction of gradient (sensitivity) information calculated from the O 3 production function which in turn is needed as “directional” information in the optimization method. Results show that the TAPOM-Lite model is consistent with t...

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.