Combining a land parcel cellular automata (LP-CA) model with participatory approaches in the simulation of disruptive future scenarios of urban land use change

Urban development is a process that becomes increasingly complex as the city evolves and in which unexpected events can happen which may alter the envisaged trend over time. To anticipate and examine the sudden emergence of processes that are difficult to predict over long-term future timelines, prospective methodologies are required to manage and implement disruptive narrative storylines in future scenario planning. In this research, a method that combines Land Parcel Cellular Automata (LP-CA) and participatory approaches was developed in order to generate land use trajectories that are spatially consistent with disruptive narrative storylines. The urban-industrial corridor of Henares (Spain), which has undergone important urban transformations in recent decades, was chosen as the study area to test the model. In a preliminary validation of the LP-CA model, a Figure of Merit (FOM) value of 0.2817 indicated satisfactory performance. The results demonstrated the usefulness of the participatory scenario-building and the workshop in supporting the configuration of the model parameters and the spatial representation of complex urban dynamics. In conclusion, this methodology can be used to generate simulations of urban land use change in disruptive future scenarios and to spatially observe the propagation of the uncertainty associated with future events across different urban land uses.This work was supported by the Spanish Ministry of Science, Innovation and Universities and the European Social Fund [grant number PRE2018–084663]; the Spanish Ministry of Economy and Competitiveness [TRANSURBAN Project CSO2017–86914-C2–1-P]; and the “Estímulo a la Excelencia para Profesores Universitarios Permanentes” research programme funded by the University of Alcal´a and the Regional Government of Madrid [grant number EPU-INV/2020/009]

Combining narratives and modelling approaches to simulate fine scale and long-term urban growth scenarios for climate adaptation

International audienceAlthough climate scientists explore the effects of climate change for 2100, it is a challenging time frame for urban modellers to foresee the future of cities. The question addressed in this paper is how to improve the existing methodologies in order to build scenarios to explore urban climate impacts in the long term and at a fine scale. This study provides a structural framework in six steps that combines narratives and model-based approaches. The results present seven scenarios of urban growth based on land use strategies and technological and socio-economic trends. These contrasted scenarios span the largest possible world of futures for the city under study. Urban maps for 2010, 2040 and 2100 were used to assess the impacts on the Urban Heat Island. The comparison of these scenarios and related outputs allowed some levers to be evaluated for their capacity to limit the increase of air temperature

A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales

This paper explores potential future land use/cover (LUC) dynamics in the Attica region, Greece, under three distinct economic performance scenarios. During the last decades, Attica underwent a significant and predominantly unregulated process of urban growth, due to a substantial increase in housing demand coupled with limited land use planning controls. However, the recent financial crisis affected urban growth trends considerably. This paper uses the observed LUC trends between 1991 and 2016 to sketch three divergent future scenarios of economic development. The observed LUC trends are then analysed using 27 dynamic, biophysical, socio-economic, terrain and proximity-based factors, to generate transition potential maps, implementing a Random Forests (RF) regression modelling approach. Scenarios are projected to 2040 by implementing a spatially explicit Cellular Automata (CA) model. The resulting maps are subjected to a multiple resolution sensitivity analysis to assess the effect of spatial resolution of the input data to the model outputs. Findings show that, under the current setting of an underdeveloped land use planning apparatus, a long-term scenario of high economic growth will increase built-up surfaces in the region by almost 24%, accompanied by a notable decrease in natural areas and cropland. Interestingly, in the case that the currently negative economic growth rates persist, artificial surfaces in the region are still expected to increase by approximately 7.5% by 2040

A Future Outlook of Narratives for the Built Environment in Japan

The evolution of long-term sustainable societies is closely connected to the transformation of the physical built environment in which those societies operate. In this paper, we present a comprehensive set of narratives for the built environment in Japan, consistent with the shared socio-economic pathways (SSPs) framework, to assess the future evolution of the adaptation and mitigation challenges. We focus on the linkage between sustainability factors and human living environments including urban form, buildings, and basic infrastructures. We introduce a new, sixth narrative to the SSPs, an alternative interpretation of SSP1. Whereas the original SSP1 assumes high societal and environmental sustainability combined with relatively high economic growth, the SSP1 variant does not highly rely on economic growth and is oriented towards a lower and more locally oriented consumption lifestyle. Nature-based solutions are integrated and examined in the new SSP1 narrative, which is aligned with the adaptation to the digital era with freedom of location. Recent global crises such as climate change and the COVID-19 pandemic may accelerate the transformation of societies. Therefore, this study attempts to imply the benefits and trade-offs of alternative pathways for the built environment

Aplicación de un modelo basado en autómatas celulares irregulares para la simulación de escenarios futuros de cambios de uso de suelo urbano

La urbanización es uno de los fenómenos más drásticos de transformación del territorio. En las últimas décadas, este fenómeno ha experimentado un aumento vertiginoso. Según indica el informe más reciente de World Urbanization Prospects de Naciones Unidas, se estima que el 68,4% de la población mundial vivirá en zonas urbanas en 2050. Además, se prevé que dicha población se duplique en los países desarrollados y se triplique en los países en vías de desarrollo. Todo ello ha supuesto impactos irreversibles sobre el territorio, afectando enormemente al conjunto de la sociedad en términos de gestión y acceso a recursos, problemas de índole social y económica, contaminación ambiental, etc. Ante esta situación, se ha observado un creciente interés por el desarrollo y mejora de instrumentos que den soporte a la toma de decisiones y a la gestión de las áreas urbanas. Uno de los instrumentos más empleados para este fin ha sido la planificación de escenarios futuros. Este permite conocer cómo podría afectar la evolución de los usos del suelo urbano a la configuración de los patrones espaciales bajo distintas perspectivas futuras. Con este enfoque, la planificación de escenarios trata de reducir la incertidumbre facilitando la toma de medidas proactivas para minimizar los posibles impactos territoriales. No obstante, la planificación de escenarios puede verse limitada ante un futuro complejo e incierto si todos los escenarios se mantienen muy próximos a una proyección tendencial. Como ejemplo, el surgimiento de acontecimientos inesperados puede llegar a inhabilitar la utilidad de una planificación lineal basada únicamente en tendencias pasadas. Por dicha razón, y para gestionar de la mejor manera posible los futuros desarrollos urbanos (no) deseados, el pensamiento disruptivo debe formar parte del proceso de previsión, rompiendo así con la linealidad de los acontecimientos actuales para abarcar lo inesperado. Como parte del proceso de planificación urbana, los modelos de simulación intentan representar el desarrollo futuro de las ciudades para garantizar que puedan desarrollarse de manera eficiente y sostenible. De ellos, los modelos basados en Autómatas Celulares (AC) se encuentran entre los más utilizados como apoyo a la gestión de las áreas urbanas. Estos modelos han experimentado una importante flexibilización, adaptándose a entornos irregulares (parcelario catastral) para ofrecer simulaciones de cambio de uso del suelo urbano a escala local. En esta línea, son cada vez más los estudios que combinan escenarios narrativos con tareas de modelización de manera participativa, y todo ello con la finalidad de obtener resultados más realistas que contemplen los actuales retos que afronta la planificación urbana. Sin embargo, es difícil que estos modelos consideren por sí solos la amplia gama de factores que intervienen en la evolución futura de las zonas urbanas, especialmente cuando tratan de representar escenarios imaginativos y disruptivos. Ante la situación actual en la que se encuentra la planificación espacial de escenarios urbanos, la presente investigación desarrolla e implementa una metodología que trata de cubrir algunos de los huecos más notables que se observan en este ámbito de estudio. En primer lugar, se presenta un estudio basado en el diseño y cartografiado de escenarios disruptivos a través de un taller participativo donde colaboraron conjuntamente expertos de diversos ámbitos relacionados con el urbanismo y el transporte. Los resultados derivados de dicho taller se analizaron mediante un método estadístico denominado Regresión Logística Geográficamente Ponderada (RLGP) con el objetivo de determinar los principales factores que explican la localización de los usos del suelo urbano en los distintos escenarios disruptivos. Posteriormente se emplearon los resultados del análisis previo para calibrar un nuevo modelo desarrollado basado en AC vectoriales, denominado Land Parcel – Cellular Automata (LP-CA). Este se encarga de simular a futuro diferentes escenarios imaginativos y disruptivos reproduciendo dinámicas urbanas de crecimiento, cambio y pérdida de usos del suelo. Al mismo tiempo, se aplicó una metodología de validación parcial para observar la robustez del modelo respecto a la influencia de los factores en las simulaciones. Finalmente, se aplicó una metodología innovadora diseñada para evaluar los diferentes escenarios disruptivos. Esta emplea métricas espaciales multiescalares basadas en el uso de ventanas móviles aplicadas a nivel de parcela que permiten caracterizar la diversidad y el tipo de expansión urbana. La metodología desarrollada fue aplicada a un sector del Corredor del Henares (España), empleándose este como laboratorio territorial experimental. Los resultados han demostrado la utilidad de la integración de escenarios disruptivos en la planificación espacial para mostrar contrastes entre los diferentes escenarios, destacando la utilidad de las visiones y del taller de cartografiado participativo en la representación espacial de la cantidad y dirección del crecimiento de los usos urbanos y la organización de la red de transporte. De manera complementaria, el análisis estadístico mediante RLGP permitió un ajuste relevante del parámetro de aptitud en el modelo de simulación, hecho que favoreció una calibración más adaptada a cada escenario. En cuanto a los avances desarrollados para el modelo LP-CA, las simulaciones lograron reproducir satisfactoriamente dinámicas urbanas disruptivas (además de crecimiento, transformación de usos y abandono). Los patrones espaciales generados se ajustaron a los escenarios narrativos descritos. Adicionalmente, el análisis de sensibilidad constató la incidencia equilibrada de todos los factores en las simulaciones generadas por el modelo LP-CA. Por último, la evaluación de escenarios permitió caracterizar en profundidad y realizar comparaciones más detalladas de las implicaciones territoriales de cada escenario en lo que respecta a la diversidad y al tipo de expansión urbana. En conclusión, la información proporcionada por esta investigación aporta nuevas herramientas y mejora algunos de los métodos ya existentes dentro de la planificación espacial de escenarios. Concretamente, ofrece una novedosa metodología capaz de generar simulaciones de crecimiento y cambio en los usos del suelo urbano para escenarios futuros disruptivos. Los resultados facilitan la observación de la propagación espacial de la incertidumbre asociada a los eventos futuros a través de los patrones que configuran los nuevos usos del suelo. En definitiva, trata de extraer información compleja de diferentes enfoques de evolución urbana futura, presentándola de manera sencilla para que pueda ser empleada por los responsables de la toma de decisiones

An agent-based approach to model farmers' land use cover change intentions

Land Use and Cover Change (LUCC) occurs as a consequence of both natural and human activities, causing impacts on biophysical and agricultural resources. In enlarged urban regions, the major changes are those that occur from agriculture to urban uses. Urban uses compete with rural ones due among others, to population growth and housing demand. This competition and the rapid nature of change can lead to fragmented and scattered land use development generating new challenges, for example, concerning food security, soil and biodiversity preservation, among others. Landowners play a key role in LUCC. In peri-urban contexts, three interrelated key actors are pre-eminent in LUCC complex process: 1) investors or developers, who are waiting to take advantage of urban development to obtain the highest profit margin. They rely on population growth, housing demand and spatial planning strategies; 2) farmers, who are affected by urban development and intend to capitalise on their investment, or farmers who own property for amenity and lifestyle values; 3) and at a broader scale, land use planners/ decision-makers. Farmers’ participation in the real estate market as buyers, sellers or developers and in the land renting market has major implications for LUCC because they have the capacity for financial investment and to control future agricultural land use. Several studies have analysed farmer decision-making processes in peri-urban regions. These studies identified agricultural areas as the most vulnerable to changes, and where farmers are presented with the choice of maintaining their agricultural activities and maximising the production potential of their crops or selling their farmland to land investors. Also, some evaluate the behavioural response of peri-urban farmers to urban development, and income from agricultural production, agritourism, and off-farm employment. Uncertainty about future land profits is a major motivator for decisions to transform farmland into urban development. Thus, LUCC occurs when the value of expected urban development rents exceeds the value of agricultural ones. Some studies have considered two main approaches in analysing farmer decisions: how drivers influence farmer’s decisions; and how their decisions influence LUCC. To analyse farmers’ decisions is to acknowledge the present and future trends and their potential spatial impacts. Simulation models, using cellular automata (CA), artificial neural networks (ANN) or agent-based systems (ABM) are commonly used. This PhD research aims to propose a model to understand the agricultural land-use change in a peri-urban context. We seek to understand how human drivers (e.g., demographic, economic, planning) and biophysical drivers can affect farmer’s intentions regarding the future agricultural land and model those intentions. This study presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: the A0 scenario – based on current demographic, social and economic trends and investigating what happens if conditions are maintained (BAU); the A1 scenario – based on a regional food security; the A2 scenario – based on climate change; and the B0 scenario – based on farming under urban pressure, and investigating what happens if people start to move to rural areas. These scenarios were selected because of the early urbanisation of the study area, as a consequence of economic, social and demographic development; and because of the interest in preserving and maintaining agriculture as an essential resource. Also, Torres Vedras represents one of the leading suppliers of agricultural goods (mainly fresh fruits, vegetables, and wine) in Portugal. To model LUCC a CA-Markov, an ANN-multilayer perceptron, and an ABM approach were applied. Our results suggest that significant LUCC will occur depending on farmers’ intentions in different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the most significant drop in non-irrigated arable land, and the highest growth in the forest and semi-natural areas in the A2 scenario; and (3) the greatest urban growth was recognised in the B0 scenario. To verify if the fitting simulations performed well, statistical analysis to measure agreement and quantity-allocation disagreements and a participatory workshop with local stakeholders to validate the achieved results were applied. These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future

Razvoj modela za integrisano upravljanje izvorom mera prilagođavanja na klimatske promene na lokalnom nivou

In synergy with other socio-economic risks, the effects of climate change pose contemporary structural challenges that can not be considered only as an environmental issue. They affect the general development and therefore make the adaptive capacity of a population uncertain in the following decades. The subject of this dissertation comprises the development of a new decision support model for the selection of local level climate change adaptation measures. Considering the nature of management issues in climate policies, which involves decision-making under the conditions of uncertainty, the model employs adaptive management principles. It was designed to help decision-makers in selection of adequate adaptation measures, and to enable monitoring of the implementation process. The key objective of the research is fulfilled by developing a model for the selection of priority adaptation measures. The model is based on scenarios of the synergistic influence of diverse sets of measures on the observed system vulnerability. It takes into account climate projections and relevant biophysical and anthropogenic factors. The model relies on a combination of several methodological approaches. The scenario method was used for the selection of adaptation measures. It is based on the assessment of the simultaneous contribution of a group of measures to the reduction of vulnerability of the observed climate impact, by forming a conditional probability diagram using Bayesian networks. Through the analysis of the likelihood of diverse states of the observed group of criteria, it is possible to examine the effect of individual measures (or sets of measures) adaptation capacity, as a result of the joint probability distribution of all criteria in the network. The analytical hierarchical process (AHP) was used to quantify the distinct qualitative relationships between the risk criteria of the observed climate impact and the adaptation measures. A GIS is used to calculate the specific values of the criteria on the network, to profile the vulnerability, sensitivity, adaptation capacity and exposure index, as well as for data integration. The model can improve the decision-making in adaptation planning process. As the results are expressed as a probability distribution for each alternative, the model can help decision makers predict the chances of achieving desired effects of selected measures, and develop detailed programs at the local level to increase their efficiency. The model is also capable to transparently monitor the application process and facilitate the development of appropriate capacities for the purpose in local communities. In this respect, the developed model also provides a methodological contribution for improving the planning framework for the local adaptation project management

Assessing Consistency of Scenarios Across Scales Developing globally linked internally consistent scenarios under the Shared Socioeconomic Pathways framework

In global environmental change research, anticipating the implications of large-scale environmental changes on local development is an important endeavour for mitigating and adapting to difficult challenges. Researchers have used multi-scale scenario analysis to anticipate future changes. Simply put, multi-scale scenario analysis is used to model cross influences between factors or drivers operating at different scales, for example, global, regional, and national levels. To ensure that scenarios are plausible, which is important for policy decisions, scenarios must be consistent across scales. However, there is confusion to what cross-scale consistency means. Consistent scenarios across scales refers to how lower level (e.g., national) scenarios should be developed considering various development pathways at the global scale that can potentially influence domestic developments. Scenario studies often use the term ‘consistent’ as defined by Zurek and Henrichs’ (2007) linking strategies. Zurek and Henrichs (2007) categorize different strategies for linking scenarios across scales. The categorization is based on the process by which scenarios developed by different modelling teams are linked. The degree to which these scenarios are linked is characterized as equivalent, consistent, coherent, comparable, and complimentary—with equivalent as the strongest link, whereas complimentary as a weak or no link. Link strength is defined by how similar (or different) the scenario elements (logics, drivers, assumptions) are. Linking scenarios across scales (e.g., global and regional) should aim to be equivalent or consistent across scales; this can be achieved by quantitative downscaling. For scenarios developed in parallel, the degree to which these scenarios can be viewed as consistent depends on whether the elements in these scenarios are the same, if not similar. However, adhering to this criterion is challenging because lower level scenarios may require different scenario elements to be incorporated in the scenario development process—these elements are factors or drivers that are operating at a more localized scale. Therefore, constraining the selection of scenario elements for developing regional or national level scenarios may be impractical. There are varying degrees of consistency of scenarios across scales much like the concept proposed by Zurek and Henrichs (2007) that spans from equivalent to complimentary. However, there is a missing ‘threshold’ in their framework—at what point should scenario studies be considered inconsistent. This thesis offers a re-interpretation on the concept of linking strategies by identifying the threshold for which scenarios can be considered inconsistent. In so doing, I would argue for the need to reinterpret Zurek and Henrichs (2007) concept of linking strategies to advance scholarship in multi-scale scenario research. This dissertation presents original research by developing an extension study on Canada’s energy futures under the Shared Socioeconomic Pathway (SSP) scenario framework. The SSP framework is intended to support more detailed analyses of societal change at a more localized scale; this framework is described in thematic special issues in Climatic Change and Global Environmental Change in 2014 and 2017 respectively. The SSPs described in these special issues are the ‘basic’ global version; from them, ‘extended’ SSPs could be elaborated further for detailed regional and national analyses (O’Neill et al., 2017, 2014). The basic SSPs provide a global framing for different socioeconomic and climate change policy developments up to 2100 (O’Neill et al., 2014). The Canadian oil and gas sector interacts directly with global energy markets and is already playing a key role in driving climate change, both as a high carbon emitter and as a major exporter of fossil fuels. Given this context, a multi-scale study provides an understanding of the broader implications of global influences on Canada’s low-carbon energy transition and vice-versa. According to the requirement set out in the SSP guidance note (van Ruijven et al., 2014), extension studies must be linked (or ‘hooked’) to the global SSPs in order to be consistent. The scientific community has developed multiple approaches for extending basic SSPs. One of the approaches is to re-specify the SSP elements. This extension study links to the SSP elements by adding elements necessary for more detailed national and sectoral analyses. Prior to developing scenarios for Canada, there is a need to identify relevant scenario elements. Identifying and prioritizing scenario elements are usually left to scenario developers’ subjective interpretation of experts or stakeholder opinions. How one expresses which scenario elements are important resides in individuals’ mental models, which are not accessible to others. In contrast, here candidate scenario elements are gleaned from the existing Canadian energy futures studies published in 2015 to 2016, which are then subjected to a network analysis. Network statistics can be used to more objectively identify which scenario elements are key since the method is transparent and data is accessible for public inspection (Lloyd and Schweizer, 2014). Elements identified as important by network analysis are then incorporated for multi-scale scenario analysis. Cross-impact balance (CIB) analysis (Weimer-Jehle, 2006) is used to search for scenario configurations that are consistent across scales. The result of multi-scale scenario analysis suggests that pathways to decarbonization in Canada are likely promoted by domestic effort regardless of which global development pathways (either carbonized or decarbonized) unfold. Scenarios in which the world remains carbonized and Canada decarbonizes and vice-versa are internally consistent. In relation to Zurek and Henrichs’ (2007) linking strategies, a conventional belief or assumption that global and local scenario outcomes must match across scales to be “consistent” has emerged in the scenario research community—though not everyone agrees with this assumption (e.g., van Ruijven et al., 2014; Wiek et al., 2013). This assumption was tested in this research. The result also tells us that internal consistency does not require that the outcomes across scales should be the same. Due to confusion about what cross-scale consistency means, there is the need to perform internal consistency checks in multi-scale scenario analysis. There is also the need to revise the operational definition of consistency across scales. The term scenario consistency across scales should not be confused with their degree of linkages (i.e., more or fewer links). Instead, we can use the consistency definition provided by CIB: internally logically consistent. Nonetheless, what may be more useful is to define the term “inconsistent”. This should be reserved for scenarios that are found to have internal logic problems—scenarios that, for good reasons, would be dismissed as implausible