Oslo Summer School in Comparative Social Science Studies 2012

Strategic Dynamic Climate Policy

Lecturer: Professor Reyer Gerlagh,
Department of Economics,
Tilburg University, The Netherlands

Main disciplines: Economics, Environmental Policy
Dates: 30 July - 3 August 2012
Course Credits: 10 pts (ECTS)
Limitation: 20 participants


Objectives
To present the basic insights from climate change economics, using numerical tools to assess the substance of various insights derived from theory. The course includes ‘hands on’ tutorials to program dedicated integrated assessment models (IAMs) in GAMS and Matlab. We extend the basic IAMs with uncertainty, overlapping generations, efficiency versus equity, fossil fuel scarcity (green paradox), carbon capture and sequestration, and endogenous innovation.


Requirements
Students are demanded to familiarize themselves with the base models provided through the course internet site, i.e. have full GAMS license for the solvers and run these models before the course starts .Students attending this course need to bring their own laptops with GAMS installed. Students must have run the GAMS models provided on the course website before the start of the lectures. There will also be some base models in Matlab. Working with the Matlab models will be voluntary and supplementary to the work in GAMS.


Lecture Outline
The course will consist of 6 x 2 hour lectures, and 4 x 2 hour tutorials, both in morning sessions. During the tutorials, the students will build their own simple integrated assessment models (IAMs) to quantitatively assess the significance of the theory presented during the lectures. Students can work on their own IAMs at the afternoon, and discuss their progress between the lectures and over lunch.

Students will build their own IAMs, or adjust existing models, to answer specific questions numerically. The tutor provides in advance, through the course web site, some model building blocks. The students will learn to adjust these models for their own purposes. The course will emphasize the relation between crucial assumptions and numerical outcomes by predicting the magnitude of changes in outcomes when different assumptions are followed. That is, the tutorials will not only be practical, but will closely link to the theory lectures and emphasize the need to discuss basic elements of the model set up.

The program language platform used will be GAMS using the MINOS and CONOPT solvers. Students are demanded to familiarize themselves with the base models provided through the course internet site, i.e. run these models before the course starts. Students who are very familiar with Matlab or Mathematica and want to program in these languages are welcome to say so. They are asked to inform the tutor 3 months in advance to discuss alternative model codes.

The literature on integrated assessment models is so vast that it cannot be completely covered during this course. The selection made here reflects a personal choice. Some articles are added as they are considered interesting, even though they will not be used during the lectures or tutorials, but they are considered to offer potential roads for future work of the students.


Monday, Lecture 1: Science of climate change
The science of climate change starts with the discovery of carbon dioxide as a greenhouse gas in the 19th century. The 20th century shows much confusion, when from 1945 to 1975 global cooling seemed more likely then warming. The lecture concludes with an overview of the post-2000 evidence that strongly points to human-induced climate change.

Readings:

  • * Friis-Christensen E. and K.Lassen, 1991, Length of the solar cycle: an indicator of solar activity closely associated with climate change, Science 254, 698-700.
  • Alcamo J., G.J.J.Kreileman, J.C.Bollen et al., 1996, Baseline scenarios of global environmental change, Global environmental change 6, 261-303.
  • Chadwick D.H. and J.Sartore, 1998, Unlocking the climate puzzle, National Geographic 193, No.5, 38-71.
  • Maier-Reimer E. and K.Hasselman (1987), Transport and storage of CO2 in the ocean - an inorganic ocean-circulation carbon cycle model, Climate Dynamics 2, 63-90.
  • * Mann M.E., R.S.Bradley, and M.K.Hughes, 1998, Global-scale temperature patterns and climate forcing over the past six centuries, Nature 392, 779-787.
  • IPCC, Intergovernmental Panel on Climate Change, 2001, Climate Change, the Scientific Basis, Technical Summary, p1-88.
  • ** Weart, S.R., 2003, The Discovery of Global Warming (Harvard University Press, Harvard, MA, USA).  See also www.aip.org/history/climate/co2.htm & http://www.aip.org/history/climate/20ctrend.htm
  • * Hansen J., L.Nazarenko, R.Ruedy et al., 2005, Earth's Energy Imbalance: Confirmation and Implications, Science 308, 1431-1435.


Monday, Lecture 2: Reduced-form economics of climate change
There are various estimates in the literature for climate change damages, and emission reduction (abatement) costs. Together with a simple reduced-form representation for the carbon cycle, we build a basic dynamic climate-economy model. This model informs us how the optimal path for emissions reductions, or carbon prices, and associated temperature rise, develops over time, dependent on the particular assumptions for damages, abatement costs, and discounting. We will see that it might be efficient to follow a modest climate policy, accepting potentially severe consequences of climate change.

Readings:

  • * Nordhaus WD, 1991, To Slow or Not to Slow: The Economics of The Greenhouse Effect, The Economic Journal 101: 920-937.
  • Peck S.C. and T.J.Teisberg, 1992, CETA: a model for carbon emissions trajectory assessment, Energy Journal 13, 55-77.
  • * Schelling T.C., 1992, Some economics of global warming, American Economic Review 82, 1-14.
  • Nordhaus W.D., 1992, An Optimal Transition Path for Controlling Greenhouse Gases, Science 258, 1315-1319.
  • Nordhaus W.D., 1993, Optimal greenhouse-gas reductions and tax policy in the "DICE" model, American Economic Review 83, 313-317.
  • * Nordhaus W.D., 1993, Rolling the 'DICE': An Optimal Transition Path for Controlling Greenhouse Gases, Resource and Energy Economics 15, 27-50. See also http://www.econ.yale.edu/~nordhaus/homepage/web%20table%20of%20contents%20102599.htm
  • Manne A.S. and R.Richels, 1995, The greenhouse debate; economic efficiency, burden sharing and hedging strategies, Energy Journal 16, 1-37.
  • Manne A.S., R.Mendelsohn, and R.Richels, 1995, MERGE, A model for evaluating regional and global effects of GHG reduction policies, Energy Policy 23, 17-34.
  • Schelling T.C., 1995, Intergenerational discounting, Energy Policy 23, 395-401.
  • Tol R.S.J., 1995, The Damage Costs of Climate Change Toward More Comprehensive Calculations, Environmental and Resource Economics 5, 353-374.
  • Tol R.S.J., 1996, The damage costs of climate change towards a dynamic representation, Ecological Economics 19, 67-90.
  • Fankhauser S. and R.S.J.Tol, 1996, Climate change costs, Energy Policy 24, 665-673.
  • Nordhaus W.D. and Z.Yang, 1996, A regional dynamic general equilibrium model of alternative climate-change strategies, American Economic Review 86, 741-765.
  • * Wigley T.M.L., R.Richels, and J.A.Edmonds, 1996, Economic and environmental choices in the stabilization of atmospheric CO2 concentrations, Nature 379, 240-243.
  • Chakravorty U., J.Roumasset, and K.Tse, 1997, Endogenous substitution among energy resources and global warming, Journal of Political Economy 105, 1201-1234.
  • Tol R.S.J., 1999, Spatial and temporal efficiency in climate policy: applications of FUND, Environmental and Resource Economics 14, 33-49.
  • Lise W. and R.S.J.Tol, 2002, Impact of climate on tourist demand, Climatic Change 55, 429-449.
  • Kavuncu Y.O. and S.D.Knabb, 2005, Stabilizing greenhouse gas emissions: Assessing the intergenerational costs and benefits of the Kyoto Protocol, Energy Economics 27, 369-386.
  • Gerlagh R., 2007, The level and distribution of costs and benefits over generations of an emission stabilization program, Energy Economics 29, 126-131.
  • Golosov, M., J. Hassler, P. Krusell, A. Tsyvinski (2011), Optimal taxes on fossil fuel in general equilibrium, NBER WP 17348.
  • Gerlagh, R. and M. Liski (2012), Carbon taxes for the next thousand years, draft presented at annual EAERE conference, Prague


Tuesday, Lecture 3: Intergenerational Equity and Climate Policy
A modest emission reduction policy might be efficient, but is it fair to future generations? The other way around, a strict climate change policy might be fair, but is it efficient? We discuss various concepts of sustainability, their connection to substitutability, and their application to climate change. Discounting and substitutability between man-made and natural goods will play a pivotal role in the discussion.

We will compare the value of the capital stock with the value of future damages associated to past emissions. We will see how the interest rate becomes an endogenous variable in an OLG economy that is separated from long-term preferences.

Readings:

  • Howarth R.B., 1990, Intergenerational Resource Rights, efficiency, and social optimality, Land Economics 66, 1-11.
  • Howarth R.B., 1991, Intertemporal equilibria and exhaustible resources: an overlapping generations approach, Ecological Economics 4, 237-252.
  • Howarth R.B., 1991, Intergenerational competitive equilibria under technological uncertainty and an exhaustible resource constraint, Journal of Environmental Economics and Management 21, 225-243.
  • Howarth R.B. and R.B.Norgaard, 1992, Environmental valuation under sustainable development, American Economic Review 82, 473-477.
  • * Howarth R.B. and R.B.Norgaard, 1993, Intergenerational transfers and the social discount rate, Environmental and Resource Economics 3, 337-358.
  • Jaeger W.K., 1995, Is sustainability optimal? Examining the differences between economists and environmentalists, Ecological Economics 15, 43-57.
  • Azar C. and J.Holmberg, 1995, Defining the Generational Environmental Debt, Ecological Economics 14, 7-19.
  • Azar C. and T.Sterner, 1996, Discounting and distributional considerations in the context of global warming, Ecological Economics 19, 169-184.
  • Howarth R.B. (1996) An overlapping generations model of climate-economy interactions. International Society for Ecological Econmics. Boston.
  • Howarth R.B., 1997, Defining sustainability: an overview, Land Economics 73 (4), 445-447.
  • Nordhaus W.D., 1997, Discounting in economics and climate change, Climatic Change 37, 315-328.
  • Heal G.M., 1997, Discounting and Climate Change, Climatic Change 37, 335-343.
  • Howarth R.B., 1998, An overlapping generations model of climate-economy interactions, Scandinavian Journal of Economics 100, 575-591.
  • Tol R.S.J., 1999, Time discounting and optimal emission reduction: an application of FUND, Climatic Change 41, 351-362.
  • Howarth R.B., 2000, Climate change and the representative agent, Environmental and Resource Economics 15, 135-148.
  • * Gerlagh R. and B.C.C.v.d.Zwaan, 2001, The effects of ageing and an environmental trust fund in an overlapping generations model on carbon emission reductions, Ecological Economics 36, 311-326.
  • * Azar C. and K.Schneider, 2002, Are the economic costs of stabilising the atmosphere prohibitive?, Ecological Economics 42, 73-80.
  • * Gerlagh R. and E.Papyrakis, 2003, Are the economic costs of (non-)stabilising the atmosphere prohibitive? A comment, Ecological Economics 46:325-327
  • Anthoff D. and R.S.J. Tol (2010): On international equity weights and national decision making in climate change, JEEM
  • Anthoff D. (2011): Optimal global dynamic carbon abatement, Job market paper: 
  • Rezai A. 2011, The Opportunity Cost of Climate Policy: A Question of Reference, Scand. J. of Economics 113: 885-903.
  • * Gerlagh, R. and M. Liski (2012), Carbon taxes for the next thousand years, draft presented at annual EAERE conference, Prague

 

Tuesday, Tutorial 1: Efficiency versus Equity
The second tutorial will focus on questions of efficiency and equity in IAMs. The questions that will be addressed, numerically, are:

(i) The climate contract-curve
What is the role of capital versus emission reductions, as investments to transfer wealth to future generations. We will use an IAM to construct a series of contract-curves, that is, combinations of capital and cumulative emissions that are on the efficiency boundary, where the particular coordinates depend on the weight of future versus present generations.

(ii) Overlapping generations
How to model an IAM with overlapping generations? How does discounting and efficient climate policy depend on demographic changes?

(iii) Overlapping generations with altruism
What are optimal carbon prices when investments in capital are determined by private life-cycle savings decisions, while each generation has an additional altruism towards future generations?

(iv) Overlapping generations versus the Solow model
We will compare the results of the previous exercise with a Solow type IAM, that is, a model with one representative agent where the savings rate is an exogenous constant.

(v) Strictly improving the Business as Usual
What are optimal carbon prices when investments in capital are determined by private life-cycle savings decisions, while each generation has an additional altruism towards future generations?


Wednesday, Lecture 4: Carbon Leakage and the Green Paradox
When policy makers with good intentions propose to implement a future carbon tax, or to stimulate the development of low carbon energy sources to (partly) replace fossil fuels in the future, oil markets may anticipate a future reduction in demand and increase current supply. Thus climate change policies may increase current emissions: a ‘Green Paradox’ arises.

The Green Paradox is studied combining the basic exhaustible resource model with a climate-economy model. We also add carbon capture and sequestration (CCS) to the analysis. While carbon-free energy sources may push fossil fuel suppliers to advance their supply, by the threat of a future decline, a CCS policy potentially increases future demand and thereby may reduce current supplies.

Readings:

  • Sinclair P, 1992, High does nothing and rising is worse: carbon taxes should keep declining to cut harmful emissions, The Manchester School 60: 41-52
  • Ulph A, and D. Ulph, 1994, The Optimal Time Path of a Carbon Tax, Oxford Economics Papers 46: 857-868.
  • Hoel M., and S. Kverndokk, 1996, “Depletion of fossil fuels and the impacts of global warming”, in Resource and Energy Economics 18:115-136.
  • Tahvonen O., 1997, Fossil fuels, stock externalities, and backstop technology, in Canadian Journal of Economics 30: 855-874.
  • Babiker M.H., 2001, Subglobal climate-change actions and carbon leakage: the implication of international capital flows, Energy Economics 23, 121-139.
  • Kuik O.J. and R.Gerlagh, 2003, Trade Liberalization and Carbon Leakage, The Energy Journal 24, 97-120.
  • Liski M. and O.Tahvonen, 2004, Can carbon tax eat OPEC's rents?, Journal of Environmental Economics and Management 47, 1-12.
  • Babiker M.H., 2005, Climate change policy, market structure, and carbon leakage, Journal of International Economics 65, 421-445.
  • Strand, J. 2007, Technology Treaties and Fossil-Fuels Extraction, in The Energy Journal 28: 129-142.
  • * Sinn HW, 2008, Public policies against global warming: a supply side approach, International tax and public finance 15: 360-394.
  • Hoel, M., 2008, Bush Meets Hotelling: Effects of Improved Renewable Energy Technology on Greenhouse Gas Emissions, CESifo working paper no. 2492.
  • * Gerlagh R, 2011, Too much oil, CESifo Economic Studies 57: 79-102.
  • Van der Ploeg, F. and C. Withagen, 2010, Is there really a green paradox? OxCarre Research Paper 35, Oxford University.
  • Grafton, R.Q., T. Kompas, and N. van Long 2010, "Biofuels subsidies and the green paradox", CESifo working paper No 2960.
  • * Hoel M. and S. Jensen (2010), Cutting Costs of Catching Carbon; Intertemporal effects under imperfect climate policy, Discussion Papers 639, Statistics Norway.
  • Michielsen T.O. (2011), Brown backstops versus the green paradox, CentER discussion paper 2011-110.


Wednesday, Tutorial 2: Green paradox
The second tutorial will add a dynamically integrated fossil fuel market to the IAM.

(i) Exhaustible fossil fuel supply with constant extraction costs
Adding a constraint on cumulative fossil fuel supplies offers the greatest scope for a green paradox to arise. We assess the change in optimal policy brought about by this addition to the model.

(ii) Adding CCS
We add CCS, following Hoel and Jensen (2010), to see whether optimal policy indeed favours CCS above renewables. That is, whether there is reason for a CCS subsidy on top of the carbon price.

(iii) Adding coal as an inexhaustible resource
Following Gerlagh (2011) and Michielsen (2011), we differentiate between gas and oil versus coal, where the former are exhaustible but the latter is an inexhaustible resource. Some literature claims that the green paradox vanishes in this more realistic setting.


Thursday, Lecture 5: Endogenous Clean Innovation
A major determinant of estimates for costs of emission reductions is the calculated costs of substituting away from fossil fuels to carbon-free energy sources, using existing technologies. But if innovators react to climate policy, we can expect clean energy to become cheaper with increasing stringency, reducing emission reduction costs. At the other hand, the innovators engaged in clean energy cannot engage in other innovative sectors, thus lowering overall productivity improvement. This lecture addresses the question whether endogenous innovation implies higher or lower overall costs, and whether it implies a different timing of policies, and or different instruments.

Readings:

  • Parry I.W.H., 1995, Optimal pollution taxes and endogenous technological progress, Resource and Energy Economics 17, 69-85.
  • Ha-Duong M., M.J.Grubb, and J.C.Hourcade, 1997, Influence of socioeconomic inertia and uncertainty on optimal CO2-emission abatement, Nature 390, 270-273.
  • Goulder L.H. and S.H.Schneider, 1999, Induced technological change and the attractiveness of CO2 abatement policies, Resource and Energy Economics 21, 211-253.
  • Goulder L.H. and K.Mathai, 2000, Optimal CO2 abatement in the presence of induced technological change, Journal of Environmental Economics and Management 39, 1-38.
  • Zwaan B.C.C.v.d., R.Gerlagh, G.A.J.Klaassen et al., 2002, Endogenous technological change in climate change modelling, Energy Economics 24, 1-19.
  • Popp D., 2002, Induced innovation and energy prices, American Economic Review 92, 160-180.
  • Gerlagh R. and B.C.C.v.d.Zwaan, 2003, Gross World Product and Consumption in a Global Warming Model with Endogenous Technological Change, Resource and Energy Economics 25, 35-57.
  • Buonanno P., C.Carraro, and M.Galeotti, 2003, Endogenous induced technical change and the costs of Kyoto, Resource and Energy Economics 25, 11-34.
  • * Popp D., 2004, ENTICE: endogenous technological change in the DICE model of global warming, Journal of Environmental Economics and Management 48, 742-768.
  • Manne A. and R.Richels, 2004, The impact of learning-by-doing on the timing and costs of CO2 abatement, Energy Economics 26, 603-619.
  • Gerlagh R., B.C.C.v.d.Zwaan, M.W.Hofkes et al., 2004, Impacts of CO2 taxes when there are niche markets and learning by doing, Environmental and Resource Economics 28, 367-394.
  • Gerlagh R. and B.C.C.v.d.Zwaan, 2004, A sensitivity analysis on timing and costs of greenhouse gas abatement, calculations with DEMETER, Climatic Change 65, 39-71.
  • Gerlagh R. and W.Lise, 2005, Carbon taxes: A drop in the ocean, or a drop that erodes the stone? The effect of carbon taxes on technological change, Ecological Economics 54, 241-260.
  • Bosetti  V, C Carraro, E Massetti and M Tavoni, 2008, International energy R&D spillovers and the economics of greenhouse gas atmospheric stabilization, Energy Economics 30: 2912-2929.
  • * Gerlagh R, 2008, A climate-change policy induced shift from innovations in carbon-energy production to carbon-energy savings, Energy Economics 30: 425-448.
  • Gerlagh, R., S. Kverndokk, and K.E. Rosendahl 2010, Optimal Timing of Climate Change Policy: Interaction Between Carbon Taxes and Innovation Externalities, ERE 43:369-390.
  • Acemoglu, D., P. Aghion, L. Bursztyn, and D. Hemous (2012), The Environment and Directed Technical Change, AER 102: 131-166.


Thursday, Tutorial 3: Endogenous growth
The third tutorial will add endogenous innovation to the model. We will use a model set up where dirty and clean energy sources compete, each with its own knowledge stock.

(i) Endogenous productivity of clean energy supply
We add endogenous productivity for the clean energy source to the model, and see how this changes the costs of emission reductions. Furthermore, we add a research externality, and see whether this induces a substantial optimal R&D subsidy as part of climate policy.

(ii) Endogenous overall productivity
We also add endogenous technology for the final goods sector, and assume full crowding out between clean energy R&D and final goods R&D. It has been argued that in this context clean R&D deserves less of a stimulus as any improvement in clean energy knowledge goes at the cost of overall income. We will use our IAM to test the hypothesis.


Friday, Lecture 6: Uncertainty
After decades of scientific climate research, the climate sensitivity, that is the steady state global earth surface temperature rise associated with a doubling of atmospheric CO2, still remains highly uncertain. Scientific estimates for the climate sensitivity range from 1 to 8 degrees Celsius. Is the uncertainty reason for extra caution and a more stringent policy compared to a situation with certain climate sensitivity? Similar questions have been asked regarding the uncertainty of future income levels. To what kind of uncertainty is optimal climate policy sensitive?

Readings:

  • Viscusi,W.K. (1996), "Economic Foundation of the Current Regulatory Reform Efforts", Journal of Economic Perspectives 10, 119-134.
  • Lempert R.J., M.E.Schlesinger, and S.C.Bankes, 1996, When we don't know the costs or the benefits: adaptive strategies for abating climate change, Climatic Change 3, 235-274.
  • * Ulph and Ulph (1997), “Global Warming, Irreversibility and Learning”, The Economic Journal 107:636-650.
  • Schneider, Stephen H., B.L. Turner II, H. Morehouse Garriga (1998), "Imaginable surprise in global change science", Journal of Risk Research 1 (2), 165–185.
  • Gjerde J., S.Grepperud, and S.Kverndokk, 1999, Optimal climate policy under the possibility of a catastrophe, Resource and Energy Economics 21, 289-317.
  • Pizer W.A., 1999, The optimal choice of climate change policy in the presence of uncertainty, Resource and Energy Economics  21, 255-287.
  • Newell, R. and W. Pizer (2001), "Discounting the benefits of climate change mitigation. How much does uncertain rates increase valuations?", Pew Center on Global Climate Change.
  • Kelly D.L., .D. Kolstad, . SChlesinger, N.G. Andronova (2000), Learning About Climate Sensitivity From the Instrumental Near-Surface Temperature Record, mimeo (see also JEDC (2009) 23: 491-518)
  • * Bosetti V. and M. Tavoni, 2009, Uncertain R&D, backstop technology and GHGs stabilization, Energy Economics 31: S18-S26.
  • Crost and Traeger (2011): Risk and Aversion in the Integrated Assessment of Climate Change, under sumbission.
  • Traeger (2009): Recent Developments in the Intertemporal Modeling of Uncertainty, Annual Review of Resource Economics.
  • Asheim G. & S. Dietz 2011, Climate Policy under Sustainable Discounted Utilitarianism, CESifo WP 3563.
  • Lemoine and Traeger (2011): Tipping Points and Ambiguity in the Integrated Assessment of Climate Change, under submission
  • Golosov, M., J. Hassler, P. Krusell, A. Tsyvinski (2011), Optimal taxes on fossil fuel in general equilibrium, NBER WP 17348.
  • Traeger: Discounting under Intertemporal Risk Aversion and Ambiguity, under revision (new version out soon he says)


Friday, Tutorial 4: Uncertainty in an Integrated Assessment Model
students can each separately decide which type of uncertainty they want to consider: climate sensitivity, population growth, technological growth, or another choice of their own interest.

We consider this question, numerically, through considering optimal policies as dependent on a mean-preserving spread, comparing the average of an ensemble of perfect-foresight policies with a hedging policy where uncertainty is resolved at some future point in time.

Alternatively, students can model uncertainty with respect to the future global population size, and economic productivity.

Using the GAMS models, we will model uncertainty as a set of possible states that the world can be in, and uncertainty resolving at some point in time. Using the Matlab model, we will add uncertainty to our analysis as a dynamic stochastic variable.

(i) Uncertain climate sensitivity
We add uncertainty with respect to the climate sensitivity in two ways. In our GAMS model, we will compare two possible states: high and low sensitivity, with uncertainty resolving by 2050. We will analyse how pre-2050 policies change as a result of the uncertainty, and how this depends on the parametric forms for production, utility and damages. In the Matlab model, we will model climate sensitivity as a stochastic observable variable that converges to its real value.

(ii) Uncertain economic productivity
We add uncertainty with respect to economic productivity, as persistent shocks. In our GAMS model, we will compare two possible states: high and low productivity, with uncertainty resolving by 2050. We will analyse how pre-2050 policies change as a result of the uncertainty, and how this depends on the parametric forms for production, utility and damages. In the Matlab model, we will model productivity shocks as a stochastic variable.


The integrated assessment models
During the course we will build a series of IAMs. We characterize each IAM by its level of sophistication. E.g. an IAM 2abBL1 has a general constant elasticity of substitution utility function, backstop energy source, a 1-box climate module with temperature adjustment, and an overlapping generations structure, but without ageing.

The numbers 1-5 differentiate with respect to sophistication of the economic model

1

  • Logistic population growth
  • Logarithmic utility
  • Cobb-Douglas production with constant energy extraction costs
  • parameters changing over time exogenously

2

  • a. Constant elasticity utility function
  • b. Backstop energy source available, linear supply

3

  • a. Exhaustible fossil fuel resource
  • b. Exhaustible oil and gas, inexhaustible coal
  • c. CCS with leakage

4

  • a. Endogenous technology for carbon free
  • b. Endogenous technology for final goods and fossil fuels

 

The capitals A,B,C differentiate with respect to the sophistication of the climate cycle model

A

  • One-box atmospheric CO2 stock with constant depreciation
  • Damages proportional to atmospheric CO2 stock

B

  • Temperature adjustment to logarithm of atmospheric CO2
  • Damages proportional to output and to temperature squared

C

  • 4 box model for atmospheric CO2



The capitals L,S,P,U differentiate with respect to specific features of the model

L

  • 1. Overlapping generations
  • 2. With ageing

S

  • 1. Limited substitutability between final goods and climate damages
  • 2. Multiple-consumers with heterogeneous damages

P

  • 1. Time-inconsistent preferences through short- versus long-term discounting
  • 2. Catastrophe as value for maximal temperature change

U

  • 1. Uncertainty for final good productivity
  • 2. Uncertainty for climate temperature sensitivity
  • 3. Uncertainty for long-term population size

 

The lecturer
Reyer Gerlagh is professor of Economics at Tilburg University. He has been associate editor of ERE, member of the editorial board of JEEM, and is still associate editor for Energy Economics, and coordinating lead author of the fifth assessment report of the IPCC, WG III. He has published many articles on climate change policy, technological change, sustainability, natural resources, and the green paradox. Methods applied in his work include econometrics, formal theory analysis, and applied numerical assessment by use of integrated assessment models.

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Tags: Economics, Climate change, Environment and Energy, Environmental policy, Summer School, Energy Politics, PhD
Published Oct. 10, 2012 1:16 PM - Last modified Oct. 10, 2012 1:39 PM