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International and intranational equity in sharing climate change mitigation burdens

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Abstract

Is inequality within countries relevant for global climate policy? Most burden-sharing proposals for climate mitigation treat states as homogenous agents, even those that aim to protect individual rights. This can lead to free riders in some large emerging economies and expose the poor to mitigation burdens in others. Proposals that incorporate an exemption for the poor can avoid these outcomes, but do not account for the role of internal policies on the poor’s actual emissions and mitigation burdens. This will create moral hazards in the design of such agreements and risk the misallocation of mitigation costs when implemented. To ensure equitable outcomes at the individual level, international agreements would need to build in additional provisions to encourage benefiting states to reduce emissions and target exemptions to the poor. But such agreements will face political conflicts over sovereignty and the burdensomeness of such provisions.

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Notes

  1. For reviews of this literature, see Gardiner (2004) and Gardiner et al. (2010).

  2. For reviews of burden-sharing proposals, see Claussen and McNeilly (1998), den Elzen and Höhne (2008), and Klinsky and Dowlatabadi (2009).

  3. Data from the Climate Analysis Indicators Tool (CAIT) of the World Resources Institute.

  4. Poverty data from the World Bank development indicators.

  5. China Statistical Yearbook (2009) indicates that every 100 urban households in China own 133 color TVs, 100 air conditioners, 94 refrigerators, 59 computers, and 55 microwaves.

  6. The estimates are based on a lognormal distribution of income. See Sect. 4. India’s estimate uses a GDP growth rate of 7% (which was India’s average rate between 2000 and 2010) and a Gini coefficient of 0.37, reducing to 0.42 (about the rate, it increased from 2000 to 2010) by 2020.

  7. The estimate is based on a Gini coefficient of 0.59 for Brazil in 2006, from the World Development Indicators.

  8. This estimate is based on a Gini coefficient of 0.45 for China in 2006, from the World Development Indicators.

  9. Note that the choice of metric—emissions or income—can have a significant impact on countries’ mitigation obligations. The two are not necessarily correlated. This issue is not material here, because the arguments pertain to the notion of a threshold, regardless of how it is operationalized.

  10. World Bank Poverty database.

  11. Other types of uncertainty that matter for monitoring and verification, but are not addressed here, include the validity of metrics, the accuracy of data, and of future predictions. See Page (2008) for some discussion on this.

  12. This is the approach used in Greenhouse Development Rights (Baer et al. 2008).

  13. GDR (Baer et al. 2008) uses a threshold of ~$20/day, while Chakravarty et al. (2009) use a 1 tCO2 per capita, which varies by country in income terms, but was in the range of $5/day in 2003.

  14. The low GDP growth rate is the average growth rate in the previous two decades, while the high represents the “aspirational” growth rate in government projections. See (Ministry of Environment and Forests; Government of India 2009). The chosen increase/decrease for the Gini coefficient closely matches the actual increase (~.33 to ~.37) from 2000 to 2007, according to World Development Indicators, World Bank. The high population growth rate represents the highest rate between 2000 and 2009. The same gap between the BAU and high was applied to yield the low rate.

  15. The mitigation cost is based on McKinsey’s abatement cost curve for India. See (McKinsey and Company 2009). Global income above the poverty threshold has been calculated based on a global Gini coefficient of 0.60 (Sutcliffe 2004).

  16. The financial impact of uncertainty does not vary proportionately with changes in the threshold itself. Indeed, the elasticity of poverty to growth and inequality may depend on the chosen threshold. The larger the gap between the exemption threshold and average income (as is the case with the US poverty line), the less elastic is poverty reduction to changes in growth and inequality.

  17. A classic example is of the putatively “negative cost” energy efficiency measures that economists often cite but that continue to remain unexploited. See McKinsey Global GHG Abatement Cost Curve, Project Catalyst, 2009. http://www.project-catalyst.info.

  18. IPCC, Sustainable Development, Working Group III Contribution to the Fourth Assessment Report: 12.

  19. This issue has been raised before by some scholars. Caney (2009b) and Singer (2002) argue, in principle, that the duty to avoid harm extends to individuals.

  20. In Pogge’s view, an institution (in this case, the mitigation agreement) may be implicated even if it causes harm through the actions of another institution (i.e., the benefiting state).

  21. The Doha Declaration, paragraph 7.

  22. “China and U.S. Hit Strident Impasse at Climate Talks,” New York Times, December 14, 2009.

Abbreviations

BASIC:

Brazil, South Africa, India, and China

BAU:

Business as usual

CDM:

Clean Development Mechanism

GDR:

Greenhouse Development Rights

GDP:

Gross domestic product

IPCC:

Intergovernmental Panel on Climate Change

NAMA:

Nationally Appropriate Mitigation Action

PPP:

Purchasing power parity

REDD:

Reducing Emissions from Deforestation and Forest Degradation

TRIPS:

Trade-related aspects of Intellectual Property Rights

WTO:

World Trade Organization

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Acknowledgments

I am grateful to Joshua Cohen, Debra Satz, David Victor, and Larry Goulder for their invaluable guidance. I also thank Paul Baer and anonymous reviewers for their valuable comments.

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  1. International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, 2361, Laxenburg, Austria

    Narasimha D. Rao

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Rao, N.D. International and intranational equity in sharing climate change mitigation burdens. Int Environ Agreements 14, 129–146 (2014). https://doi.org/10.1007/s10784-013-9212-7

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