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Are studies available on future impacts?
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Are studies available on future impacts?

You have entered the Pathfinder's decision tree for impact analytical tasks.
Impact-analytical methods address the task of analysing what are or will be the potential impacts of climate change. A variety of different tasks and methods are relevant and are described in more detail in this section, including impact modeling, impacts projection, trend detection, impact attribution and others.

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You have entered the Pathfinder's decision tree for impact analytical tasks.

Impact-analytical methods address the question of what are or will be the impacts of climate change. A variety of different approaches are relevant for analysing impacts. This part of the Pathfinder presents the decision tree for choosing impact-analytical approaches. The respective approaches are described in more detail in the respective sections. For an overview see Tables 2.3 and 2.4.

The first entry point to consider is whether studies of future impacts relevant for your location and/or sector have been carried out and are readily available.
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Table 2.3: Impact-analytical methods (1).

Method Type Trend detection Impact attribution Vulnerability indication
Task Trend detection in time series data. Explaining observed changes in study unit through (combination of) variables. Indicating how climate change may impact study unit based on (combination of) variables.
Characteristics of AS Time-series data is available on the study unit. Data on explanatory variables is available.
Data on observed impacts on the study unit is available. 		Data on indicating variables is available.
Data on observed impacts is NOT available.
Future impacts cannot be reliably simulated using computational models.
Theoretical assumptions Explanation of observed impacts through climate or socio-economic variables.
Steps taken
  1. Selection of variables of interest.
  2. Application of statistical methods.
  1. Selection of potential explanatory variables based on literature and theory.
  2. Application of statistical methods.
  1. Selection of potential indicating variables based on the literature.
  2. Aggregation of indicating variables based on normative or theoretical arguments (Hinkel 2011).
Results Statistical significant trend in data. Statistical model explaining observed impacts. A function that maps the current state of the entity to a measure of possible future impacts. The measure is often called adaptive capacity.
Example cases Emanual (2005) develops an index of accumulated annual power-dissipation from tropical storms in 5 ocean basins. The index is based on measures of wind-speed and precipitation in the storms. Using statistical methods an upward trend in the index is observed over the period since the 1970s.
Pielke et al. (2008) find no trend in the annual hurricane damage in the US normalised for inflation, population and wealth. 		Checkley et al., (2000), for example, explain changes in daily hospital admissions in Lima through the stimuli variables temperature, humidity and rainfall.
Singh et al., (2001) explain observed incidences of diarrhoea in Fiji based on variations in temperature and rainfall.
Tol and Yohe (2007) address the question whether national level socio-economic variables can explain observed impact data found in the EM-DAT database. An initial list of 34 variables was selected based on the IPCC's eight determinants of adaptive capacity. Six alternative indicators such as number of people affected by natural disasters, infant mortality and life expectancy were selected for which data was available in the EMDAT database. 24 of the 34 indicating variables were found to be statistically not significant. Amongst the statistical significant ones, different ones were found significant for different hazards. They conclude that there are no universal explanations; mechanisms that cause impacts vary from case to case and hazard to hazard. 		Hahn et al. (2009) develop a Livelihood Vulnerability Index based on surveying 220 household in Mozambique. The indicating variables describing aspects such as demographics, social networks, resource availability and past exposure to climate variability were selected based on the literature and then aggregated using equal weights.
Issues involved A general issue for the complex social-ecological systems considered in CCVIA is that the amount of possible explanatory variables is thus very large and not conducive to building statistical models. Second, most impact data has only begun to be collected with respect to slow-onset changes, most impact data is on extreme events
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Table 2.4: Impact-analytical methods (2).
Method Type Impact projection
Subtype Potential Impact Projection (PIP) Residual Impact Projection (RIP)
Task Project future impacts of climate change.
Characteristics of AS Interaction between the drivers and the study unit can be formally represented as a computational model. Given a scenario impacts can be computed.
Theoretical assumptions People affected do not adapt. People affected adapt.
Adaptation can be formally represented by a computational model
Steps taken
  1. Selection of climate and socio-economic scenarios
  2. Computation of the potential impacts of those scenarios
  3. Evaluation of impacts using impact indicators
  1. Selection of climate and socio-economic scenarios
  2. Selection of adaptation options and strategies
  3. Computation of the impacts of the scenarios and the adaptation strategies
  4. Evaluation of impacts using impact indicators
Results achieved A list of propositions that map each scenario to an impact. Each proposition is interpreted in the following way: "When the world evolves according to scenario e and people don't adapt, the impact on will be i". A list of propositions that map each scenario to a residual impact. Each proposition is interpreted: "When the world evolves according to scenario e and one adapts according to strategy a, the impact on the vulnerable system will be i."
Example cases Dasgupta et al. (2007) address the question of what the impacts of sea-level rise are on developing countries are. Impacts are projected for sea-level rise scenarios of 1 to 5 meter by overlaying data on land, population, agriculture, urban extent, wetlands and GDP with the inundation zones of the sealevel rise scenarios. They find that tens of millions of people will be displaced and economic damages will be severe but limited to a couple of countries. Hinkel et al. (2010) address the question of what will be both the potential and the residual impacts of sea-level rise on coastal countries of the EU27. The authors use the DIVA model to project the impacts of various sea-level rise and socio-economic scenarios on the countries first without any adaptation (potential impacts) and then with an adaptation strategy (residual impacts) that raises dikes to protect against coastal flooding and nourishes beach to protect against coastal erosion. It is found that while the potential impacts are substantial, adaptation reduces these impacts significantly by one or two orders of magnitude.
Issues involved Rarely understood that potential impacts will almost certainly not occur because adaptation will take place. E.g., people living in the coastal zone are likely to move away before experiencing permanent flooding. How to model adaptation? Model of adaptation (e.g. dumb, typical, smart and clairvoyant farmer) used has a significant indication on the results produced.

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This section is based on the UNEP PROVIA guidance document

Criteria checklist

1. You want to assess vulnerability.
2. Your focus is on impacts.
3. As a next step you are faced with the question whether there are studies available on future impacts for the region or sector of interest.