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The fiscal implications of climate change and policy responses

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Mitigation and Adaptation Strategies for Global Change Aims and scope Submit manuscript

Abstract

This paper is concerned with the implications of climate change, and government policies to address it, for countries’ fiscal systems at the national level. Given the uncertainties associated with climate change and countries’ responses to it, the article can do no more than review and suggest some of the major issues of likely importance for fiscal sustainability and how they might be addressed. First the paper defines fiscal sustainability and addresses some general issues related to countries’ attempts to adapt to or mitigate climate change. It then works through a number of more specific issues, discussing policies such as the implementation of environmental taxes or other instruments for the mitigation of climate change. The assessment of the impacts of such policies on fiscal sustainability requires the application of sophisticated economic models, and the paper briefly explores the relative advantages of different modeling approaches in relation to the assessment of fiscal sustainability under policies to mitigate climate change. The major research need identified by the paper is for the development of macroeconomic models that will enable countries identify the wider effects of environmental taxes and help them undertake multi-year budgeting processes.

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Notes

  1. Cited at http://www.fmi.ca/uploads/1/Lynda_Gagne_Fiscal_Sustainability.pdf. See also Burnside 2005; World Bank 2010a.

  2. See for example the work of the Economic Policy and Debt Department (PRMED) of the World Bank which developed a toolkit for assessing fiscal sustainability in the context of uncertainty http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTDEBTDEPT/0,,contentMDK:21718166~menuPK:64166739~pagePK:64166689~piPK:64166646~theSitePK:469043,00.html.

  3. Ben Bernanke, the current Chairman of the United States Federal Reserve highlighted in a recent speech the challenge of achieving fiscal sustainability and stated that “One widely accepted criterion for sustainability is that the ratio of federal debt held by the public to national income remain at least stable (or perhaps even decline) in the longer term. This goal can be achieved by bringing spending, excluding interest payments, roughly into line with revenues” (see: http://www.federalreserve.gov/newsevents/speech/bernanke20100427a.htm).

  4. See for a recent paper discussing the modernizing of the fiscal sustainability framework: IMF 2011

  5. An early use of the term ‘business cycle’ was in Burns and Mitchell (1946), where it is used to refer periodic fluctuations in GDP, which may involve expansion and then contraction. The same phenomenon had been referred to as an ‘economic cycle’ for at least a century.

  6. See the website of HM Treasury of the UK Government, http://webarchive.nationalarchives.gov.uk/±/http://www.hm-treasury.gov.uk/fiscal_policy.htm.

    The ‘Golden Rule’ is a guideline for the operation of fiscal policy. The Golden Rule states that over the economic cycle, the Government will borrow only to invest and not to fund current spending. In layman’s terms this means that on average over the ups and downs of an economic cycle the government should only borrow to pay for investment that benefits future generations. Day-to-day spending that benefits today’s taxpayers should be paid for with today’s taxes, not with leveraged investment. Therefore, over the cycle the current budget (ie, net of investment) must balance or be brought into surplus. The core of the ‘golden rule’ framework is that, as a general rule, policy should be designed to maintain a stable allocation of public sector resources over the course of the business cycle (see http://en.wikipedia.org/wiki/Golden_Rule_(fiscal_policy)).

  7. The identification of investment is also not straightforward. Its essence is that it comprises spending that yields a return in a future period. However, ex ante it is often far from clear whether for any given spending (for example, in education or some physical infrastructure) this will in fact be the case.

  8. Burnside 2005 is a fairly standard reference for techniques of fiscal sustainability analysis. The World Bank gives a range of potential sources at http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTDEBTDEPT/0,,contentMDK:21718166~menuPK:4876076~pagePK:64166689~piPK:64166646~theSitePK:469043,00.html. World Bank 2010b discusses fiscal sustainability related to Afghanistan, while for an EU assessment of relevant techniques see European Commission 2011, while European Commission 2012b contains reports on the public finances of EU countries.

  9. See for further information http://www.mca4climate.info/

  10. The scale of potential adaptation expenditures obviously depends on the extent of climate change, the consequent impacts that are expected, on which space precludes any discussion here. The interested reader is referred to the shortly forthcoming report on the impacts of climate change from Working Group 2 of the International Panel on Climate Change (IPCC).

  11. For example, see the US Federal Agency Climate Change Adaptation Planning Implementing Instructions as of March 4, 2011: http://www.whitehouse.gov/sites/default/files/microsites/ceq/ADAPTATION%20FINAL%20IMPLEMENTING%20INSTRUCTIONS%203_3.pdf.

  12. See for an extended discussion of the estimates of the costs of climate change and the potential sources: Chapter 6 of the World Development Report 2010 (World Bank 2010a).

  13. See for an overview of support measures to fossil-fuel production or use granted in OECD countries: http://www.oecd.org/site/tadffss/

  14. IPCC 2007 (p.749) discussed and assessed many of these models and considered that “SAT [surface atmospheric temperature] warming for a doubling of atmospheric carbon dioxide (CO2), or ‘equilibrium climate sensitivity’, is likely to lie in the range 2 °C to 4.5 °C”, but the current emissions trajectory will result in considerably higher atmospheric concentrations. IPCC (2007, p. 763, Table 10.5) suggests that the A2 emissions trajectory, which implies little mitigation effort, will result in a global average mean SAT rise of 3.13 °C from 1980 to 1999 levels by 2100. This is about 4 °C above pre-industrial levels. See http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter10.pdf.

  15. See, for example, the MARKAL/TIMES energy system model used by the International Energy Agency in its Energy Technology Perspective reports, e.g. IEA 2012b.

  16. Available under restricted access at http://www.glocaf.org/.

  17. For a discussion of the purpose and process of setting such frameworks see http://ec.europa.eu/budget/documents/multiannual_framework_en.htm.

  18. This produces a range of economic analyses and forecasts to provide input into government budgeting; see http://budgetresponsibility.independent.gov.uk/.

  19. See for example the OECD ENV-Linkage Model (Burniaux and Chateau 2010), and the PRIMES model used by the European Commission, http://www.e3mlab.ntua.gr/e3mlab/index.php?option=com_content&view=category&id=35%3Aprimes&Itemid=80&layout=default&lang=en and the G-Cubed Model as discussed in IMF 2008a.

  20. See UNEP, Modeling global green investment scenarios Supporting the transition to a global green economy (advance copy online release); a recent application of a system dynamics model can be found in UNEP’s green economy project (UNEP 2011a, b) – another example of a system dynamic model: RAINS/GAINS model http://www.iiasa.ac.at/rains/gains.html. See for a further discussion: Kupers and Mangalagiu 2010.

  21. See IMF 2008a, b and in particular the references given to the relevant IAM models ‘IGSM’and ‘MiniCA’ respectively Paltsev et al. 2005; Brenkert et al. 2003.

  22. See for a description of two decades of MARKAL model developments and selected applications; http://www.etsap.org/Tools.asp for a general description of MARKAL software; and http://webarchive.nationalarchives.gov.uk/±/http://www.berr.gov.uk/files/file38979.pdf for an application of the UK MARKAL- Macro model which aims to explore the technological and macroeconomic implications of reducing UK domestic carbon emissions.

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Acknowledgments

The authors would like to express their thanks to Şerban Scrieciu for helpful input into an earlier version of this paper, particularly during the inception of the MCA4climate UNEP project, as well as the current draft, to Zaid Chalabi, and to various anonymous reviewers. Any remaining errors or omissions are of course the responsibility of the authors. Please note that the views expressed in this article are those of the authors and may not in any circumstances be regarded as stating an official position of the European Environment Agency or its Management Board, or of any other organization.

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  1. UCL Institute for Sustainable Resources, University College London, Central House, 14 Upper Woburn Place, London, WC1H 0NN, UK

    Paul Ekins

  2. European Environment Agency, Kongens Nytorv 6, 1050, Copenhagen, Denmark

    Stefan Speck

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Correspondence to Paul Ekins.

Appendix. Modeling for energy-environment—economy impacts

Appendix. Modeling for energy-environment—economy impacts

The main modeling techniques used in modeling the various economic impacts of environmental change, and such attempts to avoid it as through the mitigation of climate change, are listed in Table 2 below. A detailed discussion of the underlying modeling frameworks is beyond the scope of this report and therefore only some highlights are discussed here, drawing heavily, and sometimes verbatim, on material in the following reports: Cambridge Econometrics and SERI 2010; EEA 2008; IMF 2008a, b.

Table 2 Principal analytical techniques
Full size table

“Computable General Equilibrium (CGE) models draw heavily on neoclassical economic theory and provide a consistent long-run macroeconomic framework for economic analysis that may be extended into other areas. This approach integrates microeconomic mechanisms and institutional features with clear feedback mechanisms between equations and between sectors. All behavioral equations (demand and supply) are derived from microeconomic principles (for example utility maximizing individuals, profit-maximizing enterprises)” (CE and SERI 2010, p.47). In a CGE model, most of the parameters are typically calibrated so that the theory that underpins the model is consistent with the data available for the base year. This reduces the resource requirements for the model but means the model is heavily dependent on the relationships in this year holding for other time periodsFootnote 20.

“Econometric models are based on empirical relationships and are developed using large-scale (usually time-series) data sets. The parameters of the equations are estimated with formal econometric methods which are integrated into a framework based on the national accounts and also often extended into other areas. Depending on the econometric specification, econometric models are also suitable for short-term analysis. The main assumption is that the historical behavioral relationships remain valid in forward-looking projections” (CE and SERI 2010, p. 47).

Agent-based modeling is a relatively new approach and, so far, there is no fully specified agent-based representation of the macro economy. However, the agent-based approach potentially offers an assessment of large-scale transitions, which the more established approaches (eg when validated on historical trends) are less well equipped to study. As an area of on-going development that has been applied in partial analyses it is therefore important to consider this methodology. Although the recent general recognition of the economy as a complex system has led to a rapid development in agent-based modeling approaches, as yet there is no general macroeconomic specification. There are instances, however, where an agent-based approach could provide a partial assessment (Cioffi-Revilla et al. 2010).

Dynamic system models (sometimes also labeled as system dynamic, system theory or systems models) are based on sets of equations, and can be based on either linear or non-linear programming techniques. The equations are usually based on causality (although the causality may be derived from an empirical analysis of data). Generally speaking, these equations are used to express levels of stock variables and rates as a measure of change in the stock variables. System dynamic (SD) models analyze the relationship between the structure and behavior of complex, dynamic system. “In SD models, causal relationships are analyzed, verified and formalized into models of differential equations, and their behavior is simulated and analyzed via simulation software. The method uses a stock and flow representation of systems and is well suited to jointly represent the economic, social, and environmental aspects of the development process” (UNEP 2011b, p. 505)Footnote 21.

Integrated Assessment Models (IAM) seek to combine major socioeconomic and physical processes and systems that characterize the human influence on, and interactions with, the global climate. “Their strength in the present context is a relatively detailed modeling of energy use and mitigation opportunities” (IMF 2008b, p.44). They are less well-suited than inter-temporal general equilibrium models to modeling investment, savings, and balance of payment effectsFootnote 22.

Energy simulation models, such as the MARKAL model family (MARKAL stands for MARKet ALlocation) and “is a widely applied bottom-up, dynamic, originally and mostly a linear programming (LP) model developed by the Energy Technology Systems Analysis Programmed (ETSAP) of the International Energy Agency (IEA). MARKAL depicts both the energy supply and demand side of the energy system. MARKAL provides policy makers and planners in the public and private sector with extensive detail on energy producing and consuming technologies, and it can provide an understanding of the interplay between the macro-economy and energy use” (Seebregts et al. no date given, p.1)Footnote 23.

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Ekins, P., Speck, S. The fiscal implications of climate change and policy responses. Mitig Adapt Strateg Glob Change 19, 355–374 (2014). https://doi.org/10.1007/s11027-013-9533-4

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