Generación de Modelos Tridimensionales

A further issue that arises with respect to links to decision makers is the communication of the results. Overall, because urban level IA analyses are complex, it is necessary to have a simple, graphical representation of the vision of a plan, which can then be supported by more detailed results.

A further important point is that city authorities are often highly sensitive to comparisons with city which they perceive as competitors in the region and often have an international perspective. Many larger (EU) cities have the resources to consider practices in other cities and try and identify best practices, even at a global level. In the case of Helsinki, the main competitor cities are perceived to be Stockholm and Oslo, while the pathway of development is considered to be strongly influenced by developments in Tallinn and particularly St. Petersburg. Therefore, regional planning is often

influenced by international relationships, which must then be considered as one of the necessary scales of analysis.

5.2.1 A diversity of dimensions

Because an integrated assessment has several (or many) different dimensions and perspectives, the analyses often generate a complex set of results. A clear example of this are the Vitoria case, which in theory terms is not so complex, but generates 50 indicators of urban sustainability. A further example is the London case study, which combines several separate models in an integrated assessment system: a regional macro-econometric Input-Output model, a district (ward) accessibility based transport demand model, a flood risk model etc. These assessments therefore produce a large volume of complicated data, which cannot be just reported as one set of results.

One possibility is to communicate the assumptions and a sub-set of the results e.g. the flood risk maps for different scenarios in the London case. This is what is usually done. However, this obscures the linkages between the different sub-systems in the integrated assessment, which provide the real added value of this type of analysis.

For assessments such as the transport assessments in Helsinki-Lahti, Debrecen or Kaunas, the results are organized around a single two dimensional problem, spatial development and transport.

Detailed data can be provided, but because it only has to address these two aspects, which are geographical problems and can therefore be effectively represented by two dimensional mappings, the results can be quite effectively communicated through figures and maps. This also applies to the Novi Sad case. The Luxembourg case considers energy and emissions, but also produces maps of e.g. atmospheric ozone over the region analysed, so this is an intermediate case. The Linz case took the prime energy demand of the building as a reference. The links between the multi-sectoral energy technology analysis and the regional maps of atmosphere effects are generally difficult to explain.

The LEAQ model for Luxembourg however connects the primary emissions from economic sectors (transportation, industrial, residential and commercial) to daily air quality levels via a software link and optimization program (Zachary et al., 2011; Reis et al, 2013).

5.2.2 The results are difficult to explain

Most operational conclusions of the models involve complex results, which are difficult to communicate effectively to stakeholders. This is illustrated in Figure 2.8 for the Paris case, where two-dimensional representations of land use constraints and transport times and costs are used to determine properties of the housing market and housing stock. The results are presented in 3 dimensional images for a set of output variables, which are difficult to interpret without a dialogue of explanation. However, in the Paris, London Linz and Helsinki cases, the models were developed with policy stakeholders who did understand the model and accept the results, with a resulting input into the policy decisions – an improvement over the situation in the past.

Figure 2.8 Description of NEDUM-2D model.

Another issue that arises is the communication of the uncertainty of the projections, especially the uncertainty surrounding social science related projections. In building integrated assessment model systems as in the Paris, London and Linz cases, it becomes very difficult to include feedbacks into the endogenous scenario generation where multiple models are involved. An important aspect of this is that it is in principle difficult to include social feedbacks in models – i.e. policy changes due to e.g. climate impacts or local air pollution.

These policy feedbacks themselves involve a complex set of social and political processes which cannot be realistically modelled, given the complexities of the social structures and the difficulty of quantifying them. The overall result is that integrated assessment modelling systems tend to have a single direction of causality.

In the London case, this chain consists of data (economic, social, transport) through transport activity models and economic activity models to generated patterns of transport and settlement activity which generated e.g. heat island effects and flood risk. Changes through changes in policy decisions have to be input as exogenous scenarios. This means that in principle, the integrated assessment has to be run iteratively, with different policy scenarios being tried in the assessment system to see how they contribute toward sustainability goals. However, this feature can be used to advantage in the decision making process, if the stakeholders are able to choose the scenarios and change them given the assessment outcomes.

As a conclusion, further research is required in our opinion on how to communicate these links or sensitivities effectively. Visualisation is vital for the communication of such complex sets of results (e.g. IEHIAS, 2005), with maps as shown in the case studies a minimum level of communication, which requires improvement. In some cases the information can be simplified by aggregation into summary indicators, where this is meaningful e.g. average temperature change over a city, total GHG emissions from a city, total employment in a city. The Kaunas study also provides an interesting example of how to summarise two complex spatial data into summary indicators showing an overall structure (the fractal indices in this case). However, it is often necessary to provide spatial data such as for transport flows in map form, usually through GIS database software.