Space and Price in Adapting Cities : Exploring the Spatial Economic Role of Climate-Sensitive Ecological Risks and Amenities in Finnish Housing Markets

As the adaptation of cities to climate change is increasingly overlapping sustainable urban development, the necessity to harmonize climate-proofing with economic objectives becomes ever clearer. Climate-sensitive ecological risks and amenities, and their role in markets and urban planning, are central in this issue. This research explores the reaction of urban housing markets to changes related to green amenities and flood risks; deepens the understanding of complex spatial processes, in housing markets and urban growth, that relate to the implementation of sustainable adaptation strategies; and develops advanced spatial modelling methodology that renders urban economic analysis better suitable to address questions of sustainable and climate-proof urban planning. The results demonstrate that physical or behavioral planning interventions surrounding climate-sensitive ecological risks and amenities generate economic benefits via multiple channels, when attuned with market mechanisms. This is an important building block in synchronizing climate-proofing with economic development objectives, therefore facilitating urban adaptation that is also sustainable. The synchronization requires an evidence-based understanding of the effects linked to particular interventions, at concrete locations and spatiotemporal scales. The overall message is that, while trade-offs are unavoidable, if green cities maintain agglomeration benefits, ensure increased information flows about ecological risks and amenities, while implementing amenities in a spatially parameterized manner, they are able to achieve both climate-proofing and sustainability objectives. The thesis consists of five quantitative analysis articles, while the introductory chapter synthesizes the results in the context of urban planning, spatial economics, and climate change adaptation. The first three articles apply empirical microeconometric methodologies (spatial hedonic and difference-in-differences analysis) to explore the response of housing markets to changes in green infrastructure and to policy instruments related to flood risk information. The fourth and fifth articles apply spatial complexity methods (cellular automata, fractal geometry) to extend the intuitions of microeconometric estimations into dynamic spatial processes in housing prices and urban growth. The five articles use environmental-economic datasets developed by this dissertation research, covering the urban region of Helsinki (Helsinki, Espoo, and Vantaa) and the cities of Pori and Rovaniemi.In future cities, local climate and ecosystems will be an important part of urban planning. This dissertation explores how growing cities can deal with green spaces and flood risks. Climate and environmental changes are not only about threats, but cities can use them as opportunities, provided well-informed policies based on research evidence. The study explores how house prices react to green spaces and to flood risks, and how sustainable development and climate adaptation strategy can be successful. Complicated problems such as these require innovative solutions, and the dissertation uses methods such as fractals, cellular automata, and spatial economic analysis. The study analyzes housing markets and urban dynamics in the Finnish capital region, in Pori, and in Rovaniemi, combining and developing new datasets. The dissertation shows that green spaces and information about climate-related risks are powerful tools for climate-proof sustainable cities, provided that there is a clear understanding of how all their costs and benefits are behaving in time and in different types of neighborhoods

Space and price in adapting cities : Exploring the spatial economic role of climate-sensitive ecological risks and amenities in finnish housing markets

As the adaptation of cities to climate change is increasingly overlapping sustainable urban development, the necessity to harmonize climate-proofing with economic objectives becomes ever clearer. Climate-sensitive ecological risks and amenities, and their role in markets and urban planning, are central in this issue. This research explores the reaction of urban housing markets to changes related to green amenities and flood risks; deepens the understanding of complex spatial processes, in housing markets and urban growth, that relate to the implementation of sustainable adaptation strategies; and develops advanced spatial modelling methodology that renders urban economic analysis better suitable to address questions of sustainable and climate-proof urban planning. The results demonstrate that physical or behavioral planning interventions surrounding climate-sensitive ecological risks and amenities generate economic benefits via multiple channels, when attuned with market mechanisms. This is an important building block in synchronizing climate-proofing with economic development objectives, therefore facilitating urban adaptation that is also sustainable. The synchronization requires an evidence-based understanding of the effects linked to particular interventions, at concrete locations and spatiotemporal scales. The overall message is that, while trade-offs are unavoidable, if green cities maintain agglomeration benefits, ensure increased information flows about ecological risks and amenities, while implementing amenities in a spatially parameterized manner, they are able to achieve both climate-proofing and sustainability objectives. The thesis consists of five quantitative analysis articles, while the introductory chapter synthesizes the results in the context of urban planning, spatial economics, and climate change adaptation. The first three articles apply empirical microeconometric methodologies (spatial hedonic and difference-in-differences analysis) to explore the response of housing markets to changes in green infrastructure and to policy instruments related to flood risk information. The fourth and fifth articles apply spatial complexity methods (cellular automata, fractal geometry) to extend the intuitions of microeconometric estimations into dynamic spatial processes in housing prices and urban growth. The five articles use environmental-economic datasets developed by this dissertation research, covering the urban region of Helsinki (Helsinki, Espoo, and Vantaa) and the cities of Pori and Rovaniemi

Characterization and Modeling Agricultural and Forest Trajectories in the Northern Ecuadorian Amazon: Spatial Heterogeneity, Socioeconomic Drivers and Spatial Simulations

This research shows that agricultural frontier regions are heterogeneous and complex entities. This dissertation links four interconnected questions that seek to generate new insights into the processes of land use and land cover change in the Northern Ecuadorian Amazon (NEA). The research uses household survey data collected in the study area in 1990 and 1999 and a set of classified Landsat images for 1973, 1986, 1999, 1996, and 2002. This study, first, analyzes the composition and spatial configuration of the Land Use and Land Cover (LULC) trajectories in the NEA. Land trajectories are built using image algebra and stratified by deforestation stage and census sector. The analysis of LULC trajectories has suggested a core and periphery pattern of transitions in the NEA and shows the complexity of land changes in the region. Second, this research characterizes secondary forest succession, its extent and the socioeconomic, demographic, and biophysical factors that control forest generation. The analysis, using logistic regression, shows how improvements in accessibility and off-farm employment contribute positively to forest regeneration. Third, this research analyzes the spatial heterogeneity and spatial dependence of the relationships between socioeconomic, demographic, and biophysical drivers and LULC. The intent of this question is to find the spatial non-stationarity of the relationships between factors and LULC change using Geographically Weighted Regression and Spatial Lag Models. There is also an emphasis on new spatial representations of the parameters resulting from the regression analysis. This research component determined that the intensity of the drivers of LULC change is heterogeneous across space. Four, this research develops a cellular automata model that simulates LULC trajectories using pixels, neighborhoods, and spatial regimes that interact to produce broad LULC patterns. LULC patterns emerge from rules that control interactions among cells, cell neighborhoods and other spatial regimes created using GWR models. The aim of this research is to clarify the spatial and temporal nature of the relationship between population and land change and to predict positive and negative feedbacks between social, geographical, and biophysical factors that have implications for environmental management and policy

Modelling land-use and climate change impacts on hydrology: the Upper Ganges river basin

This thesis explores the effects that large-scale land-use/cover change (LUCC) and climate change pose to the terrestrial water cycle, by developing a case study in the Upper Ganges (UG) river basin, in India. In an area experiencing rapid rates of LUCC and changes in irrigation practices, historic land-use maps are developed, based on satellite images, to investigate historical trends of LUCC. Future projection scenarios of LUCC for years up to 2035 are derived from Markov chain analysis. To explore the impacts of those changes in hydrology, the generated maps are used to force the Land Surface Model (LSM) JULES. JULES is found to be reasonably skilful in terms of its ability to reproduce observed streamflow. However, the results indicate that there is much room left for improved estimates of evapotranspiration (ET) fluxes, which JULES is found to over-predict. By dynamically coupling JULES with the crop model InfoCrop, the simulated ET fluxes are improved, compared to the original JULES model. The difference in mean annual ET between the two models (coupled and original) is approximately 150 mm/yr and indicates the potential error in ET flux estimations of an LSM without dynamic vegetation. The impact of LUCC and climate change on the hydrological response of the UG basin is quantified, by calculating variations in hydrological components (streamflow, ET and soil moisture) during the period 2000–2035. Severe increases in the high extremes of flows (+40% in the multi-model mean) are being projected for the nearby future (2030–2035). The changes in all examined hydrological components are greater in the combined land-use and climate change scenario, whilst climate change is the main driver of those changes. These results provide the necessary evidence-base to support regional land-use planning, advanced irrigation practices and develop future-proof water resource management strategies under a water-limited environment.Open Acces

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Road network maintenance and repair considering day-to-day traffic dynamics and transient congestion

Road maintenance and repair (M&R) are essential for keeping the performance of traffic infrastructure at a satisfactory level, and extending their lifetime to the fullest extent possible. For road networks, effective M&R plans should not be constructed in a myopic or ad-hoc fashion regardless of the subsequent benefits and costs associated with those projects considered. A hallmark of road M&R studies is the use of user equilibrium (UE) models to predict network traffic for a given set of road conditions with or without M&R. However, UE approaches ignore the traffic disequilibrium states and transient congestion as a result of M&R derived disruptions to network traffic on a day-to-day (DTD) time scale, which could produce additional substantial travel costs. As shown in the numerical studies on a M&R plan of the Sioux Falls network, the additional maintenance derived travel cost is about 4 billion, which is far exceed the actual M&R construction cost of 0.2 billion. Therefore, it is necessary to recognise the substantial social costs induced by maintenance-derived disruptions in the form of transient congestion when planning M&R. This realistic and pressing issue is not properly addressed by the road M&R planning problems with traffic equilibrium constraints. This thesis proposes a dual-time-scale road network M&R model aiming to simultaneously capture the long-term effects of M&R activities under traffic equilibria, and the maintenance-derived transient congestion using day-to-day (DTD) traffic evolutionary dynamics. The notion of ‘day’ is arbitrarily defined (e.g. weeks or months). The proposed M&R model consists of three sub-models: (1) a within-day dynamic network loading (DNL) model; (2) a day-to-day dynamic traffic assignment (DTD DTA) model; and (3) a day-to-day road quality model. The within-day traffic dynamics is captured by the Lighthill-Whitham-Richards (LWR) fluid dynamic network loading model. The day-to-day phase of the traffic dynamics specify travellers’ route and departure time choices in a stochastic manner based on a sequential mixed multinomial or nested Logit model. Travel information sharing behaviour is further integrated into this macroscopic doubly dynamic (both within-day and day-to-day dynamic) traffic assignment (DDTA) model to account for the impact of incomplete information on travel experiences. A deterministic day-to-day road quality model based on an exponential form of traffic flow is employed to govern the road deterioration process, where a quarter-car index (QI) is applied. All these dynamics are incorporated in a holistic dual-time-scale M&R model, which captures realistic phenomena associated with short-term and long-term effects of M&R, including physical queuing and spillback, road capacity reduction, temporal-spatial shift of congestion due to on-going M&R activities, and the tendency to converge to an equilibrium after M&R actions. Following the dual-time-scale road network M&R model, a bi-level road M&R optimisation model is proposed, where the aforementioned three sub-models are incorporated into the lower-level problem, while the upper-level is to minimise M&R expenditure and network travel costs while maintaining a satisfactory level of road quality. The M&R planning horizon is long yet finite (e.g. years or decades). A ‘quality-usage’ feedback mechanism is investigated in the proposed bi-level M&R model, namely, (1) the DTD road quality evolution as a result of DTD traffic loads and the M&R effectiveness; and (2) the evolution of DTD traffic in response to both DTD road deterioration and the improved road quality after M&R activities. The effectiveness of developed M&R optimisation model is demonstrated through case studies on the Sioux Falls network. A metaheuristic Genetic Algorithm (GA) approach is employed to solve the M&R problems given its highly nonlinear, nonconvex and non-differentiable nature. Explicit travellers’ choice behaviour dynamics and complex traffic phenomena such as network paradoxes arising from M&R activities are illustrated. Through a comparison with the results under the dynamic user equilibrium (DUE) method, the proposed DTD method achieves significant reduction in network travel cost of $ 25 million, approximately 20% of the total cost. This points to the benefit of using the DTD dynamics for capturing network’s responses to M&R in a more realistic way. The M&R model proposed in this thesis could provide valuable managerial insights for road M&R planning agencies.Open Acces

Urban Productivity & Spatial Patterns Across Scales

Understanding the nuances at play across different spatial scales is of crucial importance when considering urban economic-energetic size-cost performance, specifically when longer-term consequences are considered. Through the application of an allometric understanding of cities, a more nuanced narrative is offered highlighting the interplay of urban productivity and spatial configurations of human interactions across scales. This is presented in three parts. In the initial examination of the urban economic-energetic size-cost balance across spatial scales, we seek new insights on the effects of scale in relation to urban connectivity and density for maximizing urban size-cost balance. For this, we use the urban system in England and Wales as a topical testbed where agglomeration-based arguments have been used in support of better inter-city connectivity in order to address a historic North-South regional economic productivity divide. The inadequate connectivity thought to be affecting the economic performance across the urban network in England and Wales, however, is shown to permeate across spatial scales. More broadly, this points at a scale-induced hierarchy of urban connectivity concerning potential improvements needed at inter- and intra-city scales. This is followed by an examination of the universality and transferability of scaling insights, and their nuances, between different cities and systems of cities. Considering the current transport schemes designed to address the North-South economic gap, we examine the continental comparisons drawn specifically from the inter-city transport infrastructure connecting the Randstad in the Netherlands and Rhine-Ruhr metropolitan region in Germany. Our examination points towards fundamental differences that exist in the structure and distribution of population density across the countries and their city-regions across various scales. Additionally, the cross comparison demonstrates that, although scaling insights are transferable between urban systems, a simple multi-scale assessment of individual systems of cities in isolation is sufficient when investigating urban connectivity from an urban allometric point of view. Finally, returning full circle to the effects of spatial scales and distance on the geographical patterns of urban connectivity, we review a mathematically grounded approach to sort and organize the intra- and inter-city connectivity hierarchy while matching complementary infrastructural needs based on size-cost balances for a number of different scenarios. Together, this narrative provides a somewhat enhanced and most crucially spatially multi-scale examination of the arguments regarding connectivity and agglomeration in an urban context

Quantitative Techniques in Participatory Forest Management

Forest management has evolved from a mercantilist view to a multi-functional one that integrates economic, social, and ecological aspects. However, the issue of sustainability is not yet resolved. Quantitative Techniques in Participatory Forest Management brings together global research in three areas of application: inventory of the forest variables that determine the main environmental indices, description and design of new environmental indices, and the application of sustainability indices for regional implementations. All these quantitative techniques create the basis for the development of scientific methodologies of participatory sustainable forest management