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Equity, Emissions Allowance Trading and the Paris Agreement on Climate Change

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Abstract

The Conference of the Parties to the UN Framework Convention on Climate Change (COP21), held in Paris in December 2015, resulted in voluntary greenhouse gas (GHG) reduction pledges, independent of each other, by 195 countries. The purpose of this paper is to analyze the equity implications of this “bottom-up” approach to climate change negotiations in two major ways. First we analyze the GHG reduction targets specified in the COP21 agreement prior to any emissions trading in terms of the Gini Coefficient equity metric. We also compare the pledges with specific equity principles proposed by developing countries for many years, such as the Egalitarian and Ability to Pay equity. Second, we analyze the equity outcomes after emissions trading takes place for a sample of countries/regions. We find that international policy coordination through a system of tradable GHG emissions allowances can greatly lower the cost to all participants of reducing atmospheric concentrations of greenhouse gases. An emissions trading system involving all countries and regions that made unconditional pledges at COP21 could reduce total GHG mitigation costs from an estimated $1.71 trillion to $0.4 trillion (in 2015 dollars), a savings of 77%. The ensuing allowance sales revenues would greatly enhance the capability of lower-income countries to meet their COP21 pledges. Moreover, the cost-savings in high-income countries would facilitate contributions to the promised fund of $100 billion per year to assist climate policy implementation in low-income countries.

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Notes

  1. Some analysts, such as Barrett (2012) and Andresen (2015), label “top-down” as a misnomer because of an absence of central authority to distribute and enforce GHG reduction commitments. However, we follow the standard convention of utilizing the term to characterize the predominant approach to international climate negotiations until around 2009.

  2. The U.S. pointed to the lack of developing country commitments as the justification for not making international commitments of its own.

  3. According to the OECD (2015b), in 2013–2014 flows of $57 billion went to climate financing in developing countries, with projections to be on track for the $100 billion needed by 2020. These figures have been called in to question, however (see, e.g., a summary of the criticisms by Upton 2015) due to the inclusion of market rate loans and funding for projects that critics believe do not qualify as being climate relevant (Roberts and Weikmans 2015).

  4. Some have argued that equity principles have been more of a focus of academic discourse than actual policy-making. However, we take issue with this. Yes, several countries have proposed equity principles of their own with regard to the decisions of others, even before the bottom-up approach came into vogue, while others proposed reduction commitments without reference to equity principles, but the preponderance of negotiations have in fact revolved around equity. Some point to the Kyoto commitments being couched in terms of “common but differentiated responsibilities,” but Rose et al. (1998) and others have made a strong case that the pleading of special considerations by signatory countries, such as Australia (high dependence on coal exports) and Russia (transitioning economy), for example, were merely an appeal to fairness in terms of economic hardships.

  5. Rose (1992) makes the distinction between equity principles and “reference bases,” metrics by which to gauge the equity principle. For some, there is a one-to-one correspondence such as the Egalitarian principle always being associated with population. For others, such as Horizontal equity, various reference bases might be applied, such as emissions or energy use. Utilizing historical or future emissions is referred to as a “dynamic reference base.”

  6. The European Union elected to submit a single comprehensive pledge for its 28 countries. For simplicity, when referring to INDCs submitted for COP21, our use of the term “countries” includes the EU as a single entity.

  7. Once a country has officially registered its INDC with the UNFCCC, it is referred to as an NDC. However, since not all of the signatory countries in our dataset have done so, we continue to use INDC for the group.

  8. We acknowledge the limitations of a one-parameter characterization of an entire distribution (see, Braun 1988).

  9. We omit Andorra and Lichtenstein despite their positive reduction pledges because of their unusually low populations and high per capita incomes (which comprise only 0.02% of total emissions), so as not to skew the analysis. We also omit all countries with a negative reduction commitment, e.g., India and Russia, because of the difficulties negative numbers pose for constructing Lorenz Curves and calculating Gini coefficients. Note that although this reduces the percentage of world emissions covered to 75% (from 88%), we ran sensitivity tests on the results and found that these restrictions on our sample affected the Gini coefficients by only a few percent.

  10. The cumulative share of emissions reductions is calculated by first ordering the countries by per capita GDP in ascending order, then taking the emissions reduction percentage in each country, summing them, and finally rescaling to 100. The same approach is used in Figure 5a, with relevant reordering.

  11. Note that the Lorenz curve in Figure 2a is monotonically increasing but not at a monotonically increasing rate (a non-convexity). This might cause some to be suspicious of a Gini coefficient derived from it. As a counterpart to Figure 2a, we also specified a Lorenz curve in which countries are ordered by the ratio of the emission reduction and per capita GDP in ascending order. The Gini coefficient associated with this specification is 0.613, indicating a similar significant departure from Ability to Pay equity.

  12. One of the most prominent examples of an international linkage is that between California and Quebec C&T systems. Although it has led to relatively few transactions between the two entities, this has more to do with the similar marginal abatement costs in both regions than any issues inherent to a linked system. Rather, the linkage is proof that linking is possible. Moving forward, if Ontario’s recently established system, or Oregon’s proposed system were to join the link, we would expect to observe an increase in trade as more variation in marginal abatement costs are introduced to the system.

  13. For our analysis, we further disaggregate to 15 regions. See Appendix B for an explanation of the methodology.

  14. This is close to the 59.3 GtCO2e in 2030 for the countries we include in our analysis.

  15. Of the 90 countries in our analysis, 67 explicitly state in their INDC the intention to include the LULUCF sector in their emissions calculations, and we have used their provided data that reflects as much. However, we note that for the majority of high-income (and high emissions) countries this simply amounts to a fixed reduction in their annual emissions, as land use is relatively static, and forest preserves and national parks act as carbon sinks. Brazil is the biggest exception to this, the exclusion of LULUCF cuts their projected emissions by a third, though we note that their INDC explicitly mentions the inclusion of managed lands and forests in their emissions accounting.

  16. Some people view emissions trading as a way for the rich to buy their way out of responsibility entirely. However, in effect, those who purchase allowances (primarily high-income countries) are still paying for mitigation, though elsewhere in the world. Moreover, these payments fund the installation of new technology in countries that sell allowances (typically LMI countries).

  17. As done previously in relation to Fig. 4a, we adjust for countries attaining negative costs post-trading by scaling up all countries’ costs, so that no country has a negative cost. The difference in the approach we used as opposed to Fig. 4a is that we scale by the ratio of Cost/GDP rather than simple cost.

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Acknowledgements

The authors are, respectively, Research Professor, Research Assistant Professor, and Graduate Research Assistant, in the Sol Price School of Public Policy, University of Southern California; Scientific / Technical Support Officer, European Commission Joint Research Centre, Seville. We wish to thank Joshua Banks for his valuable assistance in compiling the data, and to Antonio Bento and Christian Flachsland for their helpful comments on earlier versions of this paper. We also acknowledge the valuable comments of Editor Stephane Hallegatte and two anonymous reviewers. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission or the University of Southern California.

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  1. Sol Price School of Public Policy and Schwarzenegger Institute for State and Global Policy, University of Southern California, Los Angeles, CA, USA

    Adam Rose, Dan Wei & Noah Miller

  2. Joint Research Centre, European Commission, Seville, Spain

    Toon Vandyck

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Correspondence to Adam Rose.

Appendices

Appendix A. Basic Data

Table 6 presents the GHG emission reduction commitments for Year 2030 for 90 countries that made unconditional commitments at COP21 that could be quantified in terms of percentage emission reductions. Commitments for several counties needed to be translated from other base years (e.g., 15% below Year 2005 levels) and adjusted to Year 2030 mission projections.

Table 6 Unconditional GHG mitigation pledges, 2030
Full size table
Table 7. Mapping of 90 Countries into 15 Regions
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Table 8 Parameter estimates for 15 regional MAC curves
Full size table

When calculating each country’s pledge to reduce emissions, data were first collected from their Intended Nationally Determined Contribution (INDC) as submitted for the COP21 proceedings. This included their total GHG emissions in the Base year, projected business-as-usual (BAU) emissions, and any amount of pledged reduction. The pledges were then standardized as both a total and percentage reduction from a 2030 BAU scenario. Conditional and unconditional pledges were calculated separately as indicated in the country’s INDC. In Table 6, we present only the unconditional pledge data that are used in this analysis.

Given that not all countries conformed to the same baseline or projected year for their BAU emissions, nor the same way of measuring emissions or reductions, we first standardized each county to a 2030 BAU projection from which we then calculated their emission reduction target in both absolute and percentage terms. For example, if a country measured their emissions intensity (mtCO2e/GDP) or emissions per capita, we used current and 2030 projected GDP and population numbers to translate these into absolute and percentage terms. In some cases, the GDP and population projections were included in the INDC; in cases where they were not, projections were adopted from the U.S. Census Bureau (2015) and Live Population (2016) for population, and the U.S. Department of Agriculture’s Economic Research Service (2015) for GDP.

Additionally, if a country provided its information in a year pre-2030, we assume that the pledge would hold as-is through 2030. If, however, a country provided their information in a post-2030 year, we linearly extrapolated backwards using either their current baseline emissions, or, if provided, an intermediate goal set forth in their INDC. For example, some countries offered both a final 2035 goal and an intermediate 2025 goal, which were averaged to calculate their approximate 2030 goal. Further complicating the issue, a few countries’ INDC’s did not provide exact numerical data, only graphs of their emissions and reduction levels. In these cases we used our best judgment to infer values from the graphs when they were drawn to-scale. If no numbers or graphs were reported for baseline or BAU emissions, values were adopted from the Climate Equity Reference Project (CERP 2016).

Appendix B. Marginal Abatement Cost Curves

The base GEM-E3 model contains 13 regions, with the Rest of World (ROW) region comprised of some very different countries with respect to GHG emissions and access to mitigation technologies. To account for this, we separate out two additional regions from ROW: Singapore and South Korea (SSK), and Mexico and South America (MSA). SSK’s MAC curve is approximated by a 50/50 split of the AUZ and JPN curves, while MSA’s MAC curve is approximated at 80% of the BRA curve. The new ROW curve is then re-approximated by pulling out the emissions-weighted SSK and MSA curves from the original ROW curve. Table 8 lists each region and its respective MAC curve parameter estimates.

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Rose, A., Wei, D., Miller, N. et al. Equity, Emissions Allowance Trading and the Paris Agreement on Climate Change. EconDisCliCha 1, 203–232 (2017). https://doi.org/10.1007/s41885-017-0012-3

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