Spatial scenarios for alternative futures

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Although the advantages mentioned above, public participation to analyse and validate future alternatives for land use is yet seen as complex (Asgesen and Dragicevic, 2014), and difficult to apply to planning policies. In Portugal, these approaches at the municipal level are still scarce.

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Figure 3.1¹⁵– Scenarios categories and types (adapted from Borjeson et al. 2006).

Predictive scenarios can be divided into forecasts and what-if. According to Borjeson (2006), projections scenarios answer the question “what will happen to the most likely development unfolds”? And in a what-if type scenario, answers to the question “what will happen, on the condition of some specific events”? The term what if is presented when there are probably effects at different visions (Scearce, 2004).

Explorative scenarios are also divided into two types: external and strategic. External scenarios have external dynamics, beyond the control of the relevant actors. On the other hand, strategic scenarios integrate internal factors, which define a collection of probable consequences for strategic decisions. These scenarios answer the following question: “what can happen if we act in a certain way”? (Höjer et al., 2008).

Finally, normative scenarios are divided into preserving and transforming. Preserving scenarios are established, to find how a target can be reached, within a predominant structure; and, transforming scenarios are used when a trend break is required to achieve a goal.

Scenarios are presented as qualitative and quantitative. In a qualitative way, scenarios are presented descriptively or/and by visual symbols (diagrams). The major disadvantage of qualitative scenarios is related to the absence of numerical information (Alcamo, 2001; Varho and Tapio, 2013). Borjeson et al. (2006) presented the main characteristics of each category summarily, and the type of scenario (Table 3.3).

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Table 3.3 ¹⁰– Scenario types(adapted from Borjeson et al. 2006).

Scenario

(type) Quantitative/ Qualitative Time-frame Predictive – what will happen?

Forecasts more quantitative; less qualitative usually short What-if more quantitative; less qualitative usually short

Explorative – what can happen?

External more qualitative; less quantitative usually long Strategic quantitative and qualitative usually long

Normative – how can a certain target be reached?

Preserving more quantitative usually long

Transforming more qualitative; some quantitative long

One of the most well-known future scenarios as support for policymakers was developed by the Intergovernmental Panel on Climate Change (IPCC) in Special Report on Energy Scenarios (SRES) in 2000 (Nakicenovic and Swart, 2000). This report defines possible future climate change with four narrative storylines (A1, A2, B1, and B2), setting global scenarios for the 21^st century (Table 3.4). These scenarios aimed to mitigate issues such as human intervention to reduce the sources of greenhouse gases, and to better plan human settlements, and infrastructures.

Table 3.4¹¹– SRES (adapted from Nakicenovic and Swart, 2000).

Scenario Description

A1

- Rapid economic growth.

- A global population reaches 9 billion in 2050.

- Extensive social and cultural interactions worldwide

A2

- Self-reliant nations.

- Increasing population.

- Regionally oriented economic development.

B1

- Rapid economic and population growth.

- Clean and resource-efficient technologies.

- Global solutions to economic, social and environmental stability.

B2

- Continuously increasing the population.

- Emphasis on local rather than global solutions.

- Intermediate levels of economic development.

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In the last years, other global models have been developed by OECD Environmental Outlook 2050 (creating bridges between science and policy realms) (OECD, 2012), World Bank (2010), The European Environment State and Outlook 2015 (makes a reflection on European environment) (EEA, 2015), Models IMAGE (Integrated Model to Assess the Global Environment - simulates the environmental consequences of human activities) (The Government Office for Science, 2011), GLOBIOM (to assess competition for land use between agriculture, bioenergy, and forestry – global scale) (IIASA, 2014), and FAO (2013) (aims to create a Climate-Smart Agriculture at regional and local scale). These models have in common the integration of planning measures.

Scenarios have been used in land use science since the 1960s. The first works were a study regarding urban growth in the Detroit area (Doxiadis, 1966), and a study concerning regional land-use planning (METLAND) (Fabos et al., 1978). Land-use scenarios have as the main goal to recognise the uncertainties to better prepare the future (Cork et al., 2000), and to promote desirable land uses (Leão et al., 2004a). Scenarios have been increasingly used as decision support tools to provide strategic urban planning (Hill and Lindner, 2010; Houet et al., 2016) and to identify spatial planning actors intentions (Ligtenberg et al., 2001). They help decision-makers to anticipate potential futures (Aalders, 2008; Fonderflick et al., 2010), to assess extreme alternative prospects, and to create planning strategies.

Integrating spatial scenarios into adaptive planning incorporates different steps. This process can answer to different hypotheses (Rauws and De Roo, 2016), such as to identify the problem (in collaboration with stakeholders), to select driving forces, and to define a time scale. Cork et al. (2000) identified the following characteristics which scenarios should have:

(1) driving forces that had the most relevance in the past; (2) driving forces that will have a great importance for changes in the future; (3) explore the uncertainties; and (4) identify the needs to be addressed in the present to prepare the future. The last steps to prepare a scenario planning consists of returning to the original question, and answer with different scenarios (Wulf et al., 2010).

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Conclusion Chapter 3

Land-use planning strategies are focused on opportunities, organisational strengths and framing processes. It is defined as support for decision-makers to develop skills and to lead to better decisions about future land use actions. Spatial scenarios can anticipate and understand human’s behaviours, and they can improve the communication between stakeholders, identification of potential conflicts, and create strategies to apply in the land use planning context.

While new planning standards such as compact urban development and polycentric development have been encouraged, urban sprawl and the loss of agricultural land remains as one of the significant challenges in Europe and in Portugal. It was found that the lack of effective spatial planning has resulted in uncoordinated urban outgrowth.

This chapter described the land use planning in Europe and in Portugal, recognizing how different stakeholders can be mobilized to participate in the decision-making process, and how land-use planning can play a key role in guiding agricultural preservation, to contribute for better decisions, and to promote land-use sustainability in the context of peri-urban areas.

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Conclusion PART ONE

Preserve agricultural land is a critical issue for generating food security and preserve biodiversity to support our livelihood. Since it is not possible to avoid land artificialization, it is important to continue developing methods to improve our understanding of land use management. Complexity science can provide an epistemological approach to better recognise the evolution and prediction of land-use dynamics. It can help planners in the decision-making process clarifying unpredictable conditions, identifying, in time and space, plausible future images, and ensuring a better quality of the living environment.

The development of LUCC models can become an essential tool for spatial planning (Herold et al., 2005). A better LUCC analysis can support better-planning practices (de Kok et al. 2001;

FAO 1993; Yirsaw et al. 2017) and identify the valuation of different land use options and socioeconomic settings to identify desirable land uses (FAO 1993). Land use planning provides policies to promote regulatory land-use implemented by decision-makers. These plans intend to control land use activities in the future, to preserve open landscapes for agriculture and nature, and to encourage sustainable development. However, these plans are too rigid especially when applied to peri-urban areas where land conversion occurs very fast. Thus, land use planning and decision-making processes applied to peri-urban areas are one of the significant policy challenges at the moment, and more studies are needed (Abrantes et al., 2016a).

The greater proximity to urban development has been increasing farmland prices (Guiling et al., 2009). The proximity to urban settlements and the urban pressure felt in these places’

present farmers with new challenges for the future. The literature focuses on three main challenges: maintaining their farmland (Malan, 2015); expanding their farmland (Deininger and Byerlee, 2011); and selling their farmland for urban development (Curran-Cournane et al., 2016; Satterthwaite et al., 2010). Analyse and understand how farmers would change the territory are of great importance to anticipate the uncertainties of the future.

Although complexity science has been applied by some researchers in simulating LUCC (Fu et al., 2018; Sang et al., 2011), few studies have combined participatory planning from the LUCC perspective.

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In the next Part, we will introduce the data and methods used in our case study and the results achieved to identify the LUCC transformations.

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Part Two: Measurement of LUCC in Torres Vedras

(Portugal)

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Introduction PART TWO

Part Two Measurement of LUCC in Torres Vedras (Portugal) is divided into two chapters.

Firstly, in chapter 4, the GIS data (acquisition, management, storage, and normalization), and the case study were presented. Secondly, in chapter 5, a detailed description of the agricultural land use dynamics in Torres Vedras municipality over time, using different techniques were shown.

We focused our analysis on measuring the influence of urban growth, and its impacts on agricultural land-use change and fragmentation.

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Chapter 4 – Study area, GIS data acquisition and