Model predictive control, the economy, and the issue of global warming

This study is motivated by the evidence of global warming, which is caused by human activity but affects the efficiency of the economy. We employ the integrated assessment Nordhaus DICE-2007 model [16]. Generally speaking, the framework is that of dynamic optimization of the discounted inter-temporal utility of consumption, taking into account the economic and the environmental dynamics. The main novelty is that several reasonable types of behavior (policy) of the economic agents, which may be non-optimal from the point of view of the global performance but are reasonable form an individual point of view and exist in reality, are strictly defined and analyzed. These include the concepts of “business as usual”, in which an economic agent ignores her impact on the climate change (although adapting to it), and of “free riding with a perfect foresight”, where some economic agents optimize in an adaptive way their individual performance expecting that the others would perform in a collectively optimal way. These policies are defined in a formal and unified way modifying ideas from the so-called “model predictive control”. The introduced concepts are relevant to many other problems of dynamic optimization, especially in the context of resource economics. However, the numerical analysis in this paper is devoted to the evolution of the world economy and the average temperature in the next 150 years, depending on different scenarios for the behavior of the economic agents. In particular, the results show that the “business as usual”, although adaptive to the change of the atmospheric temperature, may lead within 150 years to increase of temperature by 2°C more than the collectively optimal policy.environmental economics, dynamic optimization, optimal control, global warming, model predictive control, integrated assessment

A semi-empirical representation of the temporal variation of total greenhouse gas levels expressed as equivalent levels of carbon dioxide

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).In order to examine the underlying longer-term trends in greenhouse gases, that are driven for example by anthropogenic emissions or climate change, it is useful to remove the recurring effects of natural cycles and oscillations on the sources and/or sinks of those gases that have strong biological (e.g., CO2, CH4, N2O) and/or photochemical (e.g. CH4) influences on their global atmospheric cycles. We use global observations to calculate monthly estimates of greenhouse gas levels expressed as CO2 equivalents, and then fit these estimates to a semi-empirical model that includes the natural seasonal, QBO, and ENSO variations, as well as a second order polynomial expressing longer-term variations. We find that this model provides a reasonably accurate fit to the observation-based monthly data. We also show that this semiempirical model has some predictive capability; that is it can be used to provide a reasonably reliable estimate of CO2 equivalents at the current time using validated observations that lag real time by a few to several months.This study received support from the MIT Joint Program on the Science and Policy of Global Change, which is funded by a consortium of government, industry and foundation sponsors

Adaptive model-predictive climate policies in a multi-country

The purpose of this paper is to extend the use of integrated assessment models by defining rational policies based on predictive control and adaptive behavior. The paper begins with an review of the main IAMs and their use. Then the concept of Model Predictive Nash Equilibrium (MPNE) is introduced within a general model involving heterogeneous economic agents operating in (and interfering with) a common environment. This concept captures the fact that agents do not have a perfect foresight for several ingredients of the model, including that of the environment. A version of the canonical IAM (DICE) is developed as a benchmark case. The concept of MPNE is then enhanced with adaptive learning about the environmental dynamics and the damages caused by global warming. The approach is illustrated by some numerical experiments in a two-region setting for several scenarios

Strategic interaction and NFNE. The case of co2 concentration

After the first formulation of optimal control problems in the middle of the 20th century, model predictive control (MPC) and nonlinear model predictive control (NMPC) gradually became popular methods in both science and industry. Considering the advantages of NMPC as a practical method to solve dynamic decision problems numerically, this method could attract more attention in economics too. Later, in order to model strategic interactions between different policymakers, NMPC was extended to the NMPC feedback Nash equilibrium (NFNE). With focusing on these novel and practical techniques, the aims of this thesis are considered as follows. First, taking into account the privileges of mentioned techniques (NMPC and NFNE) such as repetitive solution of an optimal control problem in a receding horizon fashion and considering the time horizon, we use them in an environmental topic and assess the effects of different regimes on the climate change. Second, referring to the time horizon as a significant factor in these meth-ods, we evaluate the effect of “Different time horizons” on the results and finally, we extend the current NFNE method in order to have more accurate predictions, specifically where we face more than one state variable in our optimization problems. In this thesis, we consider a significant climate issue as global warming. Since using non-renewable energy and as its result CO2 concentration leads to the negative externalities and affects individual welfare, for evaluating the interactions between different policymakers, we use a canonical growth model augmented with damages in the household’s welfare function. We assess the CO2 concentration level when players operate under different regimes, in this thesis by different regimes, we refer to the cooperative and non-cooperative policies and we consider the period from 2019 to 2100. We start considering one common state variable as CO2 con-centration and one control variable as using non-renewable energy. Our result shows a big difference in the CO2 concentration level in the cooperative and non-cooperative situation. Although, with cooperation between independent policymakers we can reach a lower level of externality but, still it is not the best emission pathway. However, if policymakers find it difficult (e.g., for political reasons) to accept international binding agreements, and prefer to rely on their preferences for consuming non-renewal recourses instead of considering the global warming, the negative externalities and damaging effects may be quite severe. Moreover, along the line of Sims’s idea that agents often make decisions under in-formation constraint, we interpret the finite horizon as a measure of inattention or myopia. When we apply different time horizons for introducing the policymakers’ myopia, we observe that interestingly, less myopic policymakers anticipate much less CO2 concentration above the pre-industrial level. However, results state that also if we find a way to remove policy uncertainty or constraints and have a more precise prediction (i.e., longer time horizon), still the result will not be satisfying by the year 2100. Then, in our extended form of NFNE, we consider the transition from non-renewable to renewable energy as an important way to combat global warming. This transition can also be considered as an additional instrument in cooperative situations. Hence, we suppose two state variables. Along with CO2 concentration which is the common state variable, we take into account the capital stock to produce renewable energy. Also, we have two control variables as the extraction rate of fossil fuels and consumption. But, this extension in our model requires an extension in the method. So, we extend the current NFNE method and build two separate loops as two different games for optimization problems. These loops should be solved independently but simultaneously to find those fixed points that players have no incentive to change that at each point of time. Results show that despite having the renewable resources, since there is not a suitable cooperation between countries/policymakers, we cannot expect to have a transition from non-renewable to renewable resources. But interestingly, if policymakers accept a high degree of cooperation, we will reach really good results in CO2 concentration and eventually temperature

Adaptive Model-Predictive Climate Policies in a Multi-Country Setting

URL des Documents de travail : http://centredeconomiesorbonne.univ-paris1.fr/bandeau-haut/documents-de-travail/Documents de travail du Centre d'Economie de la Sorbonne 2012.29 - ISSN : 1955-611XThe purpose of this paper is to extend the use of integrated assessment models by defining rational policies based on predictive control and adaptive behavior. The paper begins with an review of the main IAMs and their use. Then the concept of Model Predictive Nash Equilibrium (MPNE) is introduced within a general model involving heterogeneous economic agents operating in (and interfering with) a common environment. This concept captures the fact that agents do not have a perfect foresight for several ingredients of the model, including that of the environment. A version of the canonical IAM (DICE) is developed as a benchmark case. The concept of MPNE is then enhanced with adaptive learning about the environmental dynamics and the damages caused by global warming. The approach is illustrated by some numerical experiments in a two-region setting for several scenarios.L'objectif de cet article est d'inclure dans un modèle d'estimation intégrée (IAM) des politiques rationnelles basées sur les théories de contrôle optimal et de comportement adaptatif. L'article commence avec une revue des IAMs les plus importantes et leur utilisation dans la littérature. Par la suite, nous introduisons le concept d'Equilibre de Nash dans le cas d'un Modèle Prédictif (MPNE) et l'intégrons à un modèle général comprenant des agents économiques hétérogènes qui agissent (et qui interfèrent) dans un même milieu. Ce concept reprend des agents qui n'ont pas de "perfect foresight" par rapport à différents ingrédients du modèle, y compris l'environnement. Une version du DICE, le modèle IAM canonique, est développé comme modèle cadre. Le concept de MPNE est alors amélioré à travers un processus d'apprentissage adaptif concernant la dynamique de l'environnement et les dommages induits par le changement climatique. Notre approche est illustrée au moyen de plusieurs simulations numériques dans un cadre à deux régions

Climate change amplifies plant invasion hotspots in Nepal

Aim Climate change has increased the risk of biological invasions, particularly by increasing the climatically suitable regions for invasive alien species. The distribution of many native and invasive species has been predicted to change under future climate. We performed species distribution modelling of invasive alien plants (IAPs) to identify hotspots under current and future climate scenarios in Nepal, a country ranked among the most vulnerable countries to biological invasions and climate change in the world. Location Nepal. Methods We predicted climatically suitable niches of 24 out of the total 26 reported IAPs in Nepal under current and future climate (2050 for RCP 6.0) using an ensemble of species distribution models. We also conducted hotspot analysis to highlight the geographic hotspots for IAPs in different climatic zones, land cover, ecoregions, physiography and federal states. Results Under future climate, climatically suitable regions for 75% of IAPs will expand in contrast to a contraction of the climatically suitable regions for the remaining 25% of the IAPs. A high proportion of the modelled suitable niches of IAPs occurred on agricultural lands followed by forests. In aggregation, both extent and intensity (invasion hotspots) of the climatically suitable regions for IAPs will increase in Nepal under future climate scenarios. The invasion hotspots will expand towards the high‐elevation mountainous regions. In these regions, land use is rapidly transforming due to the development of infrastructure and expansion of tourism and trade. Main conclusions Negative impacts on livelihood, biodiversity and ecosystem services, as well as economic loss caused by IAPs in the future, may be amplified if preventive and control measures are not immediately initiated. Therefore, the management of IAPs in Nepal should account for the vulnerability of climate change‐induced biological invasions into new areas, primarily in the mountains

Greenhouse gas emissions and the energy system: decomposition analysis and the environmental Kuznets curve

This paper discusses to what extent the recent trends in energy consumption and production are compatible with the requirements of sustainable development. For this purpose, starting from a simple identity applied to the energy sector, we use the decomposition analysis to derive a few analytical requirements for the long-term sustainability of the energy system and examine whether they are satisfied on the basis of the currently available data. From the analysis conducted in the paper, it emerges that an Environmental Kuznets Curve in energy intensity and/or carbon intensity may be insufficient to satisfy the sustainability conditions identified in the paper. Moreover, using simple graphical analysis, we show that the decomposition approach and the EKC imply two different relationships between per capita income (y) and carbon intensity (gy) and discuss the relative implications.sustainable development, energy, global warming, environmental Kuznets curve, decomposition analysis, Kaya identity

Modeling reality

Although powerful computers have allowed complex physical and manmade hardware systems to be modeled successfully, we have encountered persistent problems with the reliability of computer models for systems involving human learning, human action, and human organizations. This is not a misfortune; unlike physical and manmade systems, human systems do not operate under a fixed set of laws. The rules governing the actions allowable in the system can be changed without warning at any moment, and can evolve over time. That the governing laws are inherently unpredictable raises serious questions about the reliability of models when applied to human situations. In these domains, computers are better used, not for prediction and planning, but for aiding humans. Examples are systems that help humans speculate about possible futures, offer advice about possible actions in a domain, systems that gather information from the networks, and systems that track and support work flows in organizations

Coping with creeping catastrophes: national political systems and the challenge of slow-moving policy problems

No abstract available