Will Adaptation Delay the Transition to Clean Energy Systems? An Analysis with AD-MERGE.

• Author: Bahn, Olivier

1. INTRODUCTION

   Climate change is one of the greatest environmental challenges facing our planet in the foreseeable future. According to the Intergovernmental Panel on Climate Change (IPCC), climate change is expected to impact both ecosystems and the environmental services they provide (e.g., biodiversity) and human societies (e.g., affecting human health). This is expected to result in economic damage of approximately 2% (1) of GDP per year for a temperature increase of 2.5[degrees]C (Arent et al., 2014).

   To address this issue, one policy option is the mitigation approach, which aims to reduce anthropogenic greenhouse gas (GHG) emissions. To be effective, such a strategy needs to be implemented globally by the major emitters. The Kyoto Protocol to the United Nations Framework Convention on Climate Change (United Nations, 1997) set emission reduction targets for the developed countries. More recently, the Paris Agreement has recognized the importance of drastically reducing GHG emissions to limit the global temperature rise to 2[degrees]C, where nations submit their own emission-reduction targets named Individually Nationally Determined Contributions (INDCs). These INDCs, however, are voluntary and not binding and are expected to lead to significantly higher climate change than the proposed 2[degrees]C target (Rogelj et al., 2016). Furthermore, the withdrawal of the US will likely decrease the effectiveness of the agreement. All in all, global GHG emissions continue to increase, adding to atmospheric GHG concentrations (Victor et al, 2014).

   An additional policy option is the use of adaptation. Adaptation measures adjust economic or social structures to limit the impact of climate change at a given level of temperature, i.e. without limiting climate change itself. They can be implemented in an array of sectors and can take on many forms. Examples include crop modifications in agriculture, the building of sea walls, and medical precautions against pandemics. In the literature a common distinction is made of two types of adaptation strategies (Smit et al, 2000; Lecocq and Shalizi, 2007). Reactive strategies (or 'flow' adaptation) (2) are measures implemented in reaction to climate change stimuli. Proactive strategies (or 'stock' adaptation) (3) are preventive measures that must be taken in advance through the build up of adaptation stock. A further description of these two forms of adaptation will be given in the next section. Certain characteristics of adaptation are favourable as compared to mitigation. First, many adaptation measures have immediate benefits or benefits in the short term, whereas most mitigation benefits occur after several decades. Second, mitigation needs global cooperation to be effective, whereas adaptation can generally be implemented regionally. Adaptation also has some less favourable characteristics. For higher increases in temperature, the uncertainty range of the expected climate change damage is larger (Adger et al., 2005). Mitigation limits climate change and hence limits the uncertainty associated with living in an unkown climate, whereas adaptation shields us from the impact of climate change without affecting temperature. Furthermore, adaptation itself is likely less effective at higher temperatures, where the severe change in climate makes it harder to adapt.

   Until recently, the focus in the climate-change literature and policy arena has been on mitigation. Adaptation is now attracting more attention, both in the scientific community with increased research and in the policy arena where funding has been made available (Pielke et al., 2007). Prominent examples are the IPCC's Fifth Assessment Report, which has four chapters that analyze adaptation, including (Chambwera et al, 2014), and the Adaptation Fund (4), which supports adaptation projects in developing countries. Also the recent Paris Agreement includes funding for adaptation (and mitigation) in developing regions. Though both adaptation and mitigation by themselves can reduce climate change impacts, addressing climate change most effectively will require a combination of both. The question then arises how much resources should be allotted to consumption, investments in capital, investments in adaptation capital, adaptation costs and mitigation efforts. One way to find the optimal balance of the mitigation and adaptation, is to use an integrated assessment approach that combines social economic elements with geophysical and environmental elements. Due to among others the large uncertainties involved in the issue of climate change, these models have important limitations, which should be considered when interpreting their results. Integrated assessment model (IAM) results should be viewed as thought experiments, where the precision of their numerical magnitudes should not be overestimated. They are aggregate models that simplify reality and do not include many important details. Given these drawbacks, IAM results remain relevant in creating a better understanding of the future impacts of climate change and the interactions between climate policies.

   Examples of IAMs include DICE (Nordhaus, 1994, 2014), FUND (Anthoff and Tol, 2013), MERGE (Manne and Richels, 1992; Manne et al., 1995; Manne and Richels, 2005), RICE (Nordhaus and Yang, 1996; Nordhaus, 2011), and WITCH (Bosetti et al., 2006). Mitigation policies have been widely studied with IAMs, but adaptation strategies have only recently been explored. The first model to include adaptation was the PAGE model (Hope et al., 1993; Hope, 2006). PAGE modeled adaptation in a simplistic manner: for a small adaptation fee 90% of the climate-change damage could be eliminated. Later models have included a more comprehensive approach to adaptation. We distinguish these models based on the type of adaptation they include. Models include either reactive adaptation or proactive adaptation or both. Reactive adaptation refers to adaptation taken in reaction to climate change such as farmers changing their harvesting time. Proactive adaptation refers to adaptation taken in anticipation of future climate change, such as e.g. the building of seawalls for future protection against sea-level rise. Several models include only reactive adaptation, such as early versions of AD-DICE (de Bruin et al., 2009b) and AD-RICE (de Bruin et al., 2009a). FEEM-RICE (Bosello, 2008) and the first version of Ada-BaHaMa (Bahn et al., 2012) include only proactive adaptation. Other models include both reactive and proactive adaptation, such as later versions of AD-DICE (de Bruin and Dellink, 2011), AD-RICE (de Bruin, 2011, 2014), and Ada-BaHaMa (Bahn et al., 2015), as well as AD-WITCH (Bosello et al., 2010, 2013). AD-WITCH also includes adaptive capacity, where GDP growth enhances a region's capacity to adapt. The FUND model also includes sector-specific adaptation options, which depending on the sector are either proactive or reactive.

   The aim of this paper is twofold. First, we contribute to the adaptation modeling literature (5), which relies on a limited number of IAMs, by introducing in the MERGE model both reactive and proactive adaptation. In the process, we also recalibrate the MERGE damage function. Second, we use the resulting model (AD-MERGE) to study in detail the impact of adaptation on the implementation of mitigation measures in the energy sector. Such analyses are possible because MERGE includes a distinct energy module that details different technological options to curb energy-related GHG emissions and for this reason was chosen for this analysis. In terms of mitigation modeling, AD-MERGE provides a more detailed representation of mitigation options than existing IAMs (with adaptation) provide. In the DICE/RICE approach, energy use and the corresponding emissions are directly derived from economic production, and mitigation options are aggregated into a single mitigation cost function. Ada-BaHaMa distinguishes between a 'carbon' sector and a 'carbon-free' sector, where mitigation consists of replacing the former sector with the latter. AD-WITCH includes a bottom-up representation of the energy sector that distinguishes among seven energy technologies, whereas AD-MERGE has a more detailed representation with close to 40 technologies. In terms of adaptation modeling, AD-MERGE includes the latest developments in the literature. AD-MERGE thus enables a more comprehensive analysis (6) of the impact of adaptation on specific mitigation technologies.

   One reason why studying the interactions between adaptation and mitigation in an IAM framework is important is to ensure mitigation results are not biased. Generally, IAMs assume adaptation strategies will be applied at their optimal level. However, examining the real-world adaptation shows that adaptation is not applied at its optimal level. There are many restrictions to adaptation, such as lack of knowledge or lack of funding, that lead to a lower level of adaptation than what is optimal (de Bruin and Dellink, 2011). IAM suggested optimal mitigation strategies should account for this and present mitigation results for varying adaptation assumptions and not simply rely on optimal adaptation. Not accounting for the interactions of adaptation and mitigation can lead to biased results, specifically when the underlying adaptation assumptions are not communicated.

   The remainder of this paper is organized as follows. In Section 2, we describe the main characteristics of the MERGE model and describe the damage module; we discuss the adaptation options and the calibration of the module. Section 3 presents the numerical results, and Section 4 provides a sensitivity analysis. Section 5 gives a discussion and a comparison with existing studies, and Section 6 presents concluding remarks.

2. MODEL DESCRIPTION

   In this section we will describe the AD-MERGE model applied in this paper. This model includes various regions, where a single decision maker for...