1.4. The challenges of MRE of adaptation

The monitoring and evaluation of adaptation interventions is beset with methodological challenges given the uncertain, non-linear, and long-term nature of climate change. These challenges are not unique to adaptation, although they differ from those faced by mitigation. (Dinshaw 2014)

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The consequences of climate change, and therefore the adaptation steps we might choose to take, are shaped by a web of bio-physical, social and economic interactions (Brown et al. 2011). These interactions occur at multiple scales, thus decisions taken at one level can reverberate throughout the system (Preston & Stafford- Smith, 2009). This complexity can make it difficult to understand the impact of adaptation policies on the resilience of a country. Factors such as long timescales, uncertainty, shifting baselines and contexts and divergent values, perceptions and goals (Bours et al. 2015 – IN PRESS) present a distinct set of challenges to those seeking to undertake MRE of adaptation at national level. These challenges are not necessarily unique to climate adaptation but do present an assortment of issues that have required re-examination of conventional MRE methods, approaches and framings. This has resulted in growing body of literature to support MRE practitioners and policymakers to employ effective MRE approaches for adaptation (Villanueva, 2011; Lamhauge et al. 2012: Bours et al 2014).   Rather than disheartening practitioners, an appreciation of the challenges of MRE of adaptation can support the development of effective and ambitious MRE systems. If carefully planned, MRE presents an exciting opportunity to understand how societies can best adapt to climate change and improve adaptation practice.

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Box 1.2 provides a brief overview of some most pertinent of these challenges to the national level.

Box 1.2: Challenges for MRE policy and practice


Uncertainty:  There are inevitable uncertainties surrounding adaptation. These are often described in terms of our understanding of the climate system. However uncertainties also relate to the social, economic and environmental drivers that influence the extent and nature of climate impacts, where they are experienced and who they affect (See Wilby and Dessai, 2010). Given the dynamic and uncertain nature of these factors it can often be difficult to evaluate the appropriateness of adaptation policies and actions. In section 2.3 of this report we examine the methods currently being used at national-level to undertake MRE in the context of uncertainty.


Long timeframes: Climate change is a long-term process that stretches beyond the span of programme management cycles. Consequently, we may not truly understand if our adaptation decisions were optimal or appropriate for many years.  As societal values and our understanding of bio-physical and social conditions change, what may appear to be a sound adaptation decision today may not in the future.


Establishing baselines: The combination of long timescales, uncertainty and a complex array of climate and non-climate drivers create a dynamic context within which adaptation occurs. This means that the specific points of reference against which adaptation progress might ideally be measured change over time (this is called the ‘shifting baseline’ problem). For example, a catastrophic storm may totally change the general public’s perception of climate risks, meaning that the level of risk that was deemed ‘acceptable’ before the storm may no longer apply. Thus the appropriateness of adaptation actions cannot be measured against the previous point of reference.   


Attribution: Given this uncertainty and long timescales it can be very difficult to attribute changes in resilience to a given intervention or policy. Resilience can be also shaped by a range of factors which do not relate directly to our adaptation efforts. For example, the annual growth rate of development in floodplain areas in the UK decreased during 2008-2011, yet this was a result of adverse economic conditions not policies to address the climate risks.


A lack of a universal objective:  Climate mitigation MRE is characterised by a strong focus on tracking changes in greenhouse gas emissions or in avoided emissions through the protection of carbon sinks.  This interchangeable and quantifiable unit of carbon dioxide equivalents emissions provides common ground for MRE. In contrast, adaptation lacks a transparent and universal objective or indicator; what exactly we should be monitoring, evaluating and reporting is therefore more varied diffuse and subjective. In section 2.3 of this report examines how a combination of methods can be used to help generate a clearer picture of adaptation progress and performance.


Diversity of key concepts and definitions: Adaptation can refer to actions taken the process by which adaptation is reached and the outcome of a process that leads to a reduction in risk (Bours et al 2014 – 12 reasons). It might comprise building adaptive capacity, adaptation planning, adaptation actions or a combination. Sometimes it is framed in terms of increasing resilience, reducing vulnerability or altering risk levels. All of these terms can offer subtlety different frames for viewing adaptation and therefore what we should be seeking to measure and understand. The need to understand the differing drivers and purposes underpinning MRE at national level, and how these can shape MRE systems, is examined in sections 2.1 and 2.3 of this report.


Data availability: Data for indicators should be scalable and applicable in a wide range of areas to allow for comparison. However, data is not always available in the same format, on the same scale or over a coherent timescale.


Resource constraints: The resources available to collect and analyse information are often limited. As with data availability, this means that compromises must be made regarding what can and should be monitored and evaluated, who can be engaged in the process and how, with whom and in what format the findings can be shared. It also emphasises the need to make use of existing data sources, including socio-economic data.

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