Climate finance for developing country mitigation: blessing or curse?

Abstract

Under the United Nations Framework Convention on Climate Change, industrialized countries have agreed to cover the incremental costs of climate change mitigation in developing countries and recent climate negotiations have reaffirmed the central role of climate finance for global mitigation efforts. We use an integrated energy–economy–climate model to assess the potential magnitude of financial transfers to developing countries that can be expected under non-market transfer mechanisms as well as international emission trading with several allocation schemes. Our results indicate that for the latter, depending on international permit allocation rules financial transfers to developing countries could reach almost USD bln 400 per year in 2020, with Sub-Saharan Africa receiving financial inflows of as much as 14.5% of its GDP. Reviewing the literature on natural resource revenues, official development assistance and foreign direct investment, we identify three major channels through which such sizable financial inflows may induce harmful effects for recipients: volatility, Dutch disease, and rent-seeking and corruption. We discuss the relevance of these mechanisms for climate finance and identify institutional arrangements which could help to avoid a ‘climate finance curse’. We conclude that there is no deterministic relationship between financial inflows and adverse consequences, as the most serious problems could be prevented or at least alleviated by appropriately designed policies and governance provisions.

Keywords:

• mitigation scenarios
• developing countries
• multilateral climate policy frameworks
• North–South

Acknowledgements

The authors thank Brigitte Knopf, Ottmar Edenhofer, and three anonymous reviewers for helpful comments and suggestions. Funding from the German Federal Ministry of Education and Research (BMBF) (funding code 01UV1008A, EntDekEn) is gratefully acknowledged.

Notes

1 For instance, for China and India manufacturing output declines by 6–7%, and manufacturing exports by 9–11%.

2 While climate finance may be sourced from both public and private sources, we do not analyse this aspect in more detail as for the potential adverse economic impacts of climate finance identified in this paper the source of transfers is not relevant. Where it might matter for implementing response options (such as conditionality rules), we raise the issue in Section 4.

3 In order to provide an appropriate picture of macro-economic abatement costs, the marginal abatement costs would not only need to include technology costs, but also cost of, e.g. overcoming market barriers (Staub-Kaminski, Zimmer, Jakob, & Marschinski, 2014).

4 In its structure, the ReMIND-R model is comparable to other integrated assessment models, e.g. RICE (Nordhaus & Yang, 1996) or MERGE (Manne, Mendelsohn, & Richels, 1995), but features a detailed resolution of the energy sector.

5 Due to the model's optimizing behaviour and the assumption of perfect foresight, the resulting stabilization scenarios should not be interpreted as forecasts but rather as first-best scenarios regarding a cost-optimal transition towards a low-carbon energy system.

6 Economic damages caused by climate change are not taken into account in this version of ReMIND-R.

7 In the future, significant additional inflows for adaptation and REDD+, which currently account for a rather small share of climate finance, could materialize (Buchner et al., 2013). As the finance needs as well as the associated burden sharing are subject to substantial uncertainty, we refrain from quantification of potential financial flows in these areas.

8 This compares to an aggregated GDP of developed countries (i.e. USA, EU27, Japan, Russia and other Annex I countries) of approximately USD trln 50 in the year 2020, depending on the scenario. For a comparison to financial flows from other sources see also Section 3.

9 Bottom-up studies estimating costs on the level of specific technologies (Olbrisch et al. 2011) usually find incremental investment costs to be higher than mitigation costs due to higher investment but lower operation costs of low-carbon technologies relative to conventional energy sources. As our approach assesses mitigation costs as macro-economic consumption losses that include all general equilibrium changes, no such general relationship can be posited a priori.

10 ReMIND-R projects lower mitigation costs for Sub-Sahara Africa in the more stringent 450 ppm mitigation target in 2020 and 2050 (but not over the second half of the twenty-first century) as compared to the less ambitious 550 ppm scenario. This can mainly be explained by technology spill-overs through learning-by-doing. That is, while more abatement is required in the more ambitious scenario, the marginal abatement cost curve shifts down due to a higher abatement level in other regions. Unlike other regions, for Sub-Saharan Africa the cost reductions associated with the latter effect dominate the rising costs related to the former (but note that in later time steps, mitigation costs in this region are significantly higher in the 450 ppm scenario).

11 With a stricter abatement target, the carbon price will be higher, but the amount of permits allocated to each region and hence the amount of permits sold by net exporters will be lower. Hence, it is ambiguous whether financial inflows will be higher or lower under a more ambitious mitigation scenario.

12 Note that developing countries here refer to the four ReMIND-R model regions IND, AFR, OAS and LAM. Also note that only financial inflows are regarded.

13 For instance, Collier (2003) estimates that for Africa a typical large export shock can decrease GPD by as much as 20% in the long run.

14 This approach elaborates on the carbon contracting idea developed by Helm and Hepburn (2007) for the UK domestic context. It also bears some resemblance with the proposal to invest more public resources in facilitating international donor-receiver bargaining over large-scale mitigation deals suggested by Victor (2011).

15 The ReMIND-R model region ‘Other Asia’ is a composite of South Asian countries except India, South-East Asian countries, the Korean peninsula, Mongolia, Nepal and Afghanistan.