Stability of international climate treaties : The importance of heterogeneity
Bibliographic record
Abstract
Climate change presents serious global risks that require global collective action in response; see for example IPCC (2007) and the Stern Review (2006). The challenge is to make global cooperation work. Climate change is therefore a central topic in international politics today, and has been for the last two decades. Nevertheless, little is happening on a global scale to reduce emissions and stabilise the concentration of greenhouse gases in the atmosphere. \n\nFrom a theoretical point of view this is not surprising. Existing literature on international climate treaties indicates that international cooperation will not succeed in solving the problem of climate change. A main reason for this is that it is much more profitable for each individual country to stay out of an agreement and let the other countries do all the work. Everybody wants to be a free-rider. \n\nThis thesis, however, presents a somewhat optimistic view. I will use a linear benefit/quadratic cost model to analyse possible coalitions between 16 independent countries. My approach is based on an analysis by Bjart Holtsmark (2013), where he uses a set of spreadsheets containing a calculation procedure of his own design to look at the stability properties of all possible coalitions between 16 parties (to be specified below). Holtsmark (2013, p. 3) bases his calculations “on non-cooperative game theory and the concept of stable coalitions as originally conceived by d’Aspremont et al. (1983)”. He examines possible coalitions between 16 world regions, where the parameter values were estimated by Osmani and Tol (2009).\n\nAs Holtsmark (2013) mentions, single countries represented by their government are the relevant parties in international negotiations. A model of international climate treaties should therefore have individual countries, not groups of countries, as units. When there are 16 potential parties to a treaty, there are 65 519 possible coalitions with at least two members. With a larger number of parties, the number of possible coalitions is also much larger. Ideally the model should include all countries that are affected by emissions of climate gases, i.e. all the countries in the world. The task of examining all possible coalitions with at least two members for every country in the world is, however, too massive for this thesis.\n\nI have applied Holtsmark’s spreadsheets as basis for the results in this thesis. I had to choose 16 countries to include in the model and then estimate the necessary parameters based on the estimations of Osmani and Tol. These parameter values allow me to get numerical results on the possible coalitions from Holtsmark’s spreadsheets. \n\nThe countries that have the highest emissions are also the countries that can contribute most to total reduction in emissions on a global scale. I have therefore in this thesis decided to include the European Union in addition to the 15 countries with the highest emissions of CO2 (in 2005 (the World Bank, 2005a)). Those countries are: China, the United States of America, the Russian Federation, India, Japan, Canada, the Republic of Korea, the Islamic Republic of Iran, Mexico, South Africa, Australia, Saudi Arabia, Brazil, Ukraine and Indonesia.\n\nThe European Union is a union of several individual countries. However, since the European Union can make decisions that are binding for its member states (Barrett, 2003) and the union negotiate internationally as a single unit, it is natural to include EU as a single party in the model. \n\nIn chapter 2 I will briefly present the parts of environmental economic theory that are of relevance to understanding the model I will use in this thesis. The model itself will be presented in chapter 3, where I will solve it first for two identical countries, then for 16 identical countries. I finally introduce the model with 16 heterogeneous countries in section 3.3. \n\nIn chapter 4 I go through the methodological and theoretical foundation for my own parameter calculations. These are, as previously noted, based on parameters presented in the paper “Toward Farsightedly Stable International Environmental Agreements” by Osmani and Tol (2009). The choice of the FUND model and the modification of these parameters are inspired by Holtsmark (2013). At the end of the chapter I present my final parameter calculations. \n\nIn chapter 5 I present the numerical results from using my parameters in Holtsmark’s spreadsheets (2013) and discuss some key coalitions. The spreadsheets are based on the model formulation presented in section 3.3 and calculates the payoffs and abatement level for each country in each of the 65 520 possible coalitions (including the outcome where no countries cooperate). The spreadsheets also calculate the global payoff and the global abatement for each coalition and rate the coalitions according to global payoff and global abatement – relative to global payoff and global abatement in the two extremes where no one cooperates and where everyone cooperates. \n\nThis is a brief summary of my findings from chapter 5:\n\nThe spreadsheets calculate which coalitions are internally stable and which coalitions are potentially internally stable. As expected, there are relatively few internally stable coalitions. Some internally stable coalitions do, however, have a large number of members. These large coalitions unfortunately do not perform well in terms of relative global abatement (or in terms of relative global payoff). \n\nThe majority of the 65 520 possible outcomes are potentially internally stable when side payments are introduced. While not even the best performing internally stable coalitions have high relative global abatement, a large number of the potentially internally stable do. By allowing for side payments in this thesis, we massively increase the gains of potential cooperation. The main conclusion of this thesis is that when we have a model with heterogeneous countries and allow for side payments we can get quite close to the social optimum (as defined later in the thesis). \n\nWhile a coalition also needs to be externally stable in order to be self-enforcing (according to the definition of self-enforceability introduced later in this thesis) I do not calculate external stability. This is mainly because calculating this is outside the scope of this thesis. I could also argue that internal stability is the most interesting concept; while external stability ensures that no outsider wants to be part of the treaty, internal stability ensures that no member wants to leave the treaty. Forcing a sovereign state to stay on as a member of a treaty is not possible according to international law. However, forming an exclusive treaty with restrictive membership is possible.\n\nChapter 6 is the conclusion. In addition to briefly commenting on the main results from chapter 4 and 5, I also point out some interesting questions I have not had time to answer in this thesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".