Decision support for international climate policy – The PRIMAP emission module

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Abstract

Sound decisions in international climate policy depend on comprehensive and reliable emission data as well as accurate analysis and comparisons of policy proposals. In this context, the emission module of the Potsdam Real-time Integrated Model for the probabilistic Assessment of emission Paths (PRIMAP) has been developed. This article describes its design and functionality. The emission module allows for the flexible combination of data sources contained in its custom-built database into composite datasets, and the calculation of national, regional and global emission pathways following various emission allocation schemes. The resulting emission pathways can further be used to determine atmospheric CO2 concentrations and temperature probability distributions using the PRIMAP climate module, which currently incorporates the reduced complexity climate and carbon-cycle model MAGICC. In addition to the calculation of emission pathways, the PRIMAP emission module supports analysis of policy options, like the quantification of different land use, land-use change and forestry (LULUCF) accounting provisions for Annex I countries. We discuss three applications of the PRIMAP emission module. In a bottom-up approach, we implement the pledges from the Copenhagen Accord, the announced developed country emission targets, in which provisions from LULUCF are taken into account. For the derivation of developing country emission pathways two different approaches are applied: capped per capita emissions and equal cumulative per capita emissions at average developed country levels. As a third example we implement a top-down approach, which equalises cumulative per capita emissions in a global emission pathway to achieve the 2 °C target with a likely (greater than 66%) probability.

Highlights

► Emission allocation tool supporting international climate policy analysis. ► Emission pathways can be assembled and their climatic impacts assessed. ► The tool can flexibly combine and compare data of diverse sources. ► Results show that cumulative per capita caps require deep cuts for Annex I countries. ► Annex I country pledges leave a narrow margin to prevent dangerous climate change.

Introduction

Since the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) (IPCC, 2007), anthropogenic causes of climate change have been described as very likely. In order to keep the expected negative impacts to a minimum, nations are aiming to curb still-growing global greenhouse gas (GHG) emissions to achieve a downward trajectory. Countries associated with the Copenhagen Accord agreed on a ‘below 2 °C’ limit (UNFCCC, 2009c) in order to prevent dangerous climate change. Governments of the G8 agreed to limit the global temperature increase to below 2 °C relative to pre-industrial levels (G8, 2009). The Alliance of Small Island States and the Least Developed Countries (AOSIS, 2008) call for a 1.5 °C limit to global temperature increase. It is possible to estimate global emission budgets (Allen et al., 2009, Matthews et al., 2009, Meinshausen et al., 2009, Zickfeld et al., 2009) which meet such temperature targets with a certain probability. However, the implied certainty with which the target should be achieved, as well as the allocation of national or regional emission allowances, are political questions explicitly or implicitly discussed and negotiated under the United Nations Framework Convention on Climate Change (UNFCCC). The capacity to make real-time assessments of the resulting country reductions and of upcoming policy options, as well as of their implications on the global emission pathway, is important for decision makers and climate policy analysts. Examples of policy options to be assessed include the accounting for emissions from land use, land-use change and forestry (LULUCF), and the banking of surplus emission allowances.

There are several different emission allocation schemes on the table; for an overview see, for example, den Elzen and Höhne (2010) or Hof et al. (2009). Top-down allocation schemes use various equity, fairness and capability criteria, to assign national emission allowances, possibly by dividing a specified global emission budget (these include per capita emissions, historical responsibilities or the capacity to pay). Alternatively, in a bottom-up approach, the pledges by individual countries are added up in order to quantify the global emission level. This aggregation can then be used to determine the gap between the projected and an ‘allowed’ global emission budget.

In this complex setting, where both approaches – bottom-up and top-down – are of relevance, a decision support system for climate policy analysts should assist in answering the following questions:

  • How should a global emission pathway be constructed, to comply with a certain temperature or atmospheric CO2 concentration target?

  • What are the top-down country or regional emission pathways resulting from different global allocation proposals?

  • What is the amount of ‘allowed’ emissions remaining for the rest of the world, given certain pledges by a subset of countries and a global emission budget?

  • What is the globally aggregated difference between the most and the least ambitious targets, given that many countries state a range of potential reductions?

  • How to assess a country pledge given various criteria, for example, the convergence of emission intensity (CO2 per GDP) or per capita emissions?

  • What are the effects of specific policy options – like accounting for LULUCF – and how do they influence country targets and bottom-up constructed global emission pathways?

To answer these questions, a comprehensive emission inventory is necessary. There has already been wide interest in emission inventories for specific sectors (Hurtt et al., 2006, Shevliakova et al., 2009, Pham et al., 2010, Poupkou et al., 2010), as well as expert systems to build, aggregate and store emission inventories at high spatial and temporal (e.g. hourly) resolution, for example, as basis for atmospheric chemistry-transport models (Winiwarter and Schimak, 2005, Samaali et al., 2007). For the application outlined in this paper, specific problems concerning emission inventories are the often sparse nature and ambiguity of emission data. Different institutions and countries may rely on different data sources and assume various emission pathways for the future. The allocation of ‘allowed’ emissions often depends on these hypothetical reference scenarios. A decision support system needs to be able to deal with various datasets, allow for flexible data combinations, a fast update cycle and should feature comparisons between different reference emission pathways.

There are already various tools that analyse aspects of climate policy proposals, such as EVOC (Evolution of Commitments tool) (Höhne et al., 2007, Ekholm et al., 2009), FAIR (Framework to Assess International Regimes for the differentiation of commitments) (den Elzen and Lucas, 2005, den Elzen et al., 2011), EPPA (Emissions Prediction and Policy Analysis) (Jacoby et al., 2008), WITCH (World Induced Technical Change Hybrid model) (Bosetti et al., 2006), C-Roads (Fiddaman et al., 2011), and the on-line tool JCM (java climate model) (see http://jcm.chooseclimate.org/). The EVOC tool, for example, allows for the application of different top-down approaches on a provided dataset with country resolution composed from various sources (Höhne et al., 2007, Ekholm et al., 2009). The EPPA model was used to calculate different emission allocation or cost compensation scenarios for individual countries and regional groups (Jacoby et al., 2008). FAIR, a very comprehensive tool, includes most of the top-down approaches discussed in the literature. With FAIR, emission pathways for 26 world regions (den Elzen and Lucas, 2005, den Elzen et al., 2011) as well as on the country level (den Elzen et al., 2007a) can be determined for various datasets. Like EPPA, FAIR comprises a cost module with which regional abatement costs and derivations of cost efficient emission pathways can be calculated. Moreover, combination with the climate and optimisation module SiMCaP enables the optimisation of emission pathways to attain a specific climate target (den Elzen et al., 2007b).

The emission module described in this paper has been in development since 2008 as a flexible and extendable module of the Potsdam Real-time Integrated Model for the probabilistic Assessment of emission Paths (PRIMAP) (Fig. 1). In contrast to the tools mentioned above, the PRIMAP emission module features several functions that enable climate policy analysts to compose merged datasets on the basis of the most recent available data, with adaptable country resolution and regions definable at runtime. In addition to flexibility in data usage, further key design aspects of the emission module were the possibility to incorporate new algorithms to define bottom-up emission pathways as well as algorithms to split-up global emission budgets in a top-down manner. At present, the PRIMAP emission module does not contain a mitigation-cost or energy-system model, as opposed to models like FAIR or EPPA. Unique elements of the PRIMAP emission module are the inclusion of further functionalities to assess policy options like the treatment of emissions from LULUCF or banked surplus assigned amount units, as well as the impacts of such options on bottom-up determined emission paths. Furthermore, it is one of the core-modules in the larger PRIMAP model framework, which provides an analytical chain from emission allocation decisions to probabilistic regional climate impacts. As explained above and documented below, the PRIMAP model is a unique policy tool due to its comprehensiveness and focus on synthesising multi-gas and multi-sector emission data sources, in combination with a flexible approach, to apply multiple effortsharing mechanisms and probabilistic regional climate projections. PRIMAP underlies the work of Rogelj et al., 2009, Rogelj et al., 2010a, Rogelj et al., 2010b and has been used in negotiation advice and presentations by the Alliance of the Small Island States, the European Union and others (e.g. in Cancún, COP16).

In Section 2 of this article an overview of the implemented functionalities of the PRIMAP emission module is given as well as an outline of how it links to the PRIMAP climate module. In Section 3 a top-down and two different bottom-up application examples are provided. In Section 4 we discuss the limitations of the current functionalities of the PRIMAP emission module and Section 5 concludes.

Section snippets

The PRIMAP emission module

The PRIMAP emission module comprises a database (the so-called PRIMAPDB) with import and export routines, as well as various components for data processing and an emission pathway calculation framework which are introduced below. The PRIMAP emission module is programmed in MATLABⓇ.

Application examples

Three illustrative application examples using the PRIMAP emission module and the PRIMAP climate module are described in this section. The examples employ the two complementary ways to obtain a global emission pathway, as described above (Section 1). The first two follow a bottom-up principle for all developed countries, combined with two different options to calculate the emission paths of the developing countries (Scenario 1 and Scenario 2). The third example implements a top-down approach

Limitations

The PRIMAP emission module does not come with a complete ready-to-use dataset, but supports the use of different datasets and facilitates the aggregation of multiple data sources into new composite datasets of varying geographical, source and multi-gas resolution. As pointed out in Section 2.3, data requirements for a specific analysis often exceed the level of detail that a single data source can provide. Furthermore, overriding priority can demand the compilation of different sources, for

Conclusion

We have presented a comprehensive and powerful tool for analysing proposed policies for emission reductions, using a wide variety of data sources. The PRIMAPDB together with the PRIMAP emission module function library allows for quick access to and combination of many types of information, from emission data for various gases and categories to socioeconomic data like population and GDP, for selectable countries and years. A composite dataset is often necessary for the calculation of emission

Acknowledgements

The suggestions by the three anonymous reviewers are gratefully acknowledged for helping to improve the manuscript. We thank a number of people that were crucial in developing this work, namely Bill Hare and Michiel Schaeffer for joint discussions on the conception of this work and valuable comments on earlier drafts, Kirsten Macey for input in particular on the LULUCF accounting option calculator, as well as Katja Frieler and Mario Parade for assistance with programming and data import

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