Towards a More-than-Human Approach to Smart and Sustainable Urban Development: Designing for Multispecies Justice

The term ‘sustainability’ has become an overused umbrella term that encompasses a range of climate actions and environmental infrastructure investments; however, there is still an urgent need for transformative reform work. Scholars of urban studies have made compelling cases for a more-than-human conceptualisation of urban and environmental planning and also share a common interest in translating theory into practical approaches and implications that recognise (i) our ecological entanglements with planetary systems and (ii) the urgent need for multispecies justice in the reconceptualisation of genuinely sustainable cities. More-than-human sensibility draws on a range of disciplines and encompasses conventional and non-conventional research methods and design approaches. In this article, we offer a horizon scan type of review of key posthuman and more-than-human literature sources at the intersection of urban studies and environmental humanities. The aim of this review is to (i) contribute to the emerging discourse that is starting to operationalise a more-than-human approach to smart and sustainable urban development, and; (ii) to articulate a nascent framework for more-than-human spatial planning policy and practice.

Inland waterway network mapping of AIS data for freight transportation planning

Travel demand models (TDMs) with freight forecasts estimate performance metrics for competing infrastructure investments and potential policy changes. Unfortunately, freight TDMs fail to represent non-truck modes with levels of detail adequate for multi-modal infrastructure and policy evaluation. Recent expansions in the availability of maritime movement data, i.e. Automatic Identification System (AIS), make it possible to expand and improve representation of maritime modes within freight TDMs. AIS may be used to track vessel locations as timestamped latitude–longitude points. For estimation, calibration and validation of freight TDMs, this work identifies vessel trips by applying network mapping (map-matching) heuristics to AIS data. The automated methods are evaluated on a 747-mile inland waterway network, with AIS data representing 88% of vessel activity. Inspection of 3820 AIS trajectories was used to train the heuristic parameters including stop time, duration and location. Validation shows 84⋅0% accuracy in detecting stops at ports and 83⋅5% accuracy in identifying trips crossing locks. The resulting map-matched vessel trips may be applied to generate origin–destination matrices, calculate time impedances, etc. The proposed methods are transferable to waterways or maritime port systems, as AIS continues to grow.

The Effects of Commercial Orientation on (Agro) Pastoralists’ Household Food Security: Evidence from (Agro) Pastoral Communities of Afar, Northeastern Ethiopia

Commercialization has been increasingly promoted for (agro) pastoral communities as an intervention to improve incomes and food access. Using households from rural Afar, this study examines the food security effects of the livestock commercial orientations of (agro) pastoralists by employing propensity score matching (PSM) procedures. The results show that, despite the fact that the market production of (agro) pastoralists is stressed by a broad range of factors, identified as cultural, infrastructural, and production risks, participation in livestock sales significantly decreased the severity of food insecurity in both the household food insecurity access score (HFIAS), and the reduced coping strategy index (rCSI) measures. However, the results failed to find consistently significant effects via the per capita consumption expenditure measure, in which case, the ‘subsistence’ and ‘commercially’ oriented groups are alike. Yet, given the factors depressing market production, properly addressed with policy measures, the income generated from livestock sales improved the welfare of (agro) pastoralists, at least by some (the HFIAS and rCSI) of the livelihood indicators. This highlights the importance of combining market infrastructure investments with culturally sensitive policy measures in order to sustain the traditional livestock husbandry of (agro) pastoralists. Therefore, in order to sustainably improve the food security situations in (agro) pastoral areas, the promotion of market production through the broadening of market access for both sales and purchases is important.

Quantifying changes in bicycle volumes using crowdsourced data

Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

Smart Tourism Destinations

Tourism businesses and touristic destinations that want to reach a highly competitive position in the world should fulfill the required infrastructure investments and acquire a service concept that is consistent with the smart tourism strategy in order to turn into smart tourism. In order to compete with rival destinations, it has become a prerequisite for Turkey's world-famous destinations such as Istanbul, Antalya, Izmir, and Cappadocia to embrace digital transformation and smart tourism concepts. Accordingly, the concepts of smart tourism, smart city, and smart tourism destinations will be explained, and smart tourism applications in Turkey's touristic destinations will be explored in this chapter within the framework of current academic studies. It is aimed to ascertain how well these specified destinations adhere to the concept of smart tourism.

The Impact of Assuming Perfect Foresight When Planning Infrastructure in the Water–Energy–Food Nexus

Perfect foresight hydroeconomic optimization models are tools to evaluate impacts of water infrastructure investments and policies considering complex system interlinkages. However, when assuming perfect foresight, optimal management decisions are found assuming perfect knowledge of climate and runoff, which might bias the economic evaluation of investments and policies. We investigate the impacts of assuming perfect foresight by using Model Predictive Control (MPC) as an alternative. We apply MPC in WHAT-IF, a hydroeconomic optimization model, for two study cases: a synthetic setup inspired by the Nile River, and a large-scale investment problem on the Zambezi River Basin considering the water–energy–food nexus. We validate the MPC framework against Stochastic Dynamic Programming and observe more realistic modeled reservoir operation compared to perfect foresight, especially regarding anticipation of spills and droughts. We find that the impact of perfect foresight on total system benefits remains small (<2%). However, when evaluating investments and policies using with-without analysis, perfect foresight is found to overestimate or underestimate values of investments by more than 20% in some scenarios. As the importance of different effects varies between scenarios, it is difficult to find general, case-independent guidelines predicting whether perfect foresight is a reasonable assumption. However, we find that the uncertainty linked to climate change in our study cases has more significant impacts than the assumption of perfect foresight. Hence, we recommend MPC to perform the economic evaluation of investments and policies, however, under high uncertainty of future climate, increased computational costs of MPC must be traded off against computational costs of exhaustive scenario exploration.

Infrastructure investment and employment: Evidence for Portugal

We estimated how investment in 12 infrastructure types affects employment in Portugal. Using a vector-autoregressive specification at the industry level, we found a double dividend associated with ports and airports: investing in either delivers the greatest bang per euro, both on impact and in the long run. One million euros invested in ports and airports creates 717.1 and 290.5 jobs in the long run, respectively, and 535 and 253.3 jobs in the short run, respectively. Regarding long-term employment effects, these are followed by municipal roads, telecommunications, national roads, health structures, education facilities, refineries, railroads, and highways. Water infrastructures and electricity and gas infrastructures have negligible effects. With the long-term effects decomposed, sizable supply-side employment effects for health and education facilities exist, while demand-side effects dominate for airports, ports, municipal roads, and telecommunications. Employment following the investment in national roads is balanced across demand and supply channels. We found no significant employment-related location effects of infrastructure investments. Also, investing in either health facilities or in education buildings entails non-negligible job losses in the short run. These results suggest that the magnitude and the timing of job creation crucially depend on the type of infrastructure investment. Policymakers in Portugal need to be aware of this in choosing between countercyclical or structural targets.

The Value of Sector Coupling for the Development of Offshore Power Grids

Offshore grids can have a key role in the transition of the energy system to sustainability. Although they require extensive infrastructure investments, they open up for the exploitation of additional resources and may be important to provide for part of the increasing electricity demand driven by sector coupling. This paper quantifies the socio-economic value of offshore grids and identifies their major drivers, performing energy system optimization in a model application of the Northern-central European energy system and the North Sea offshore grid towards 2050. The increasing wake loss with the size of hub-connected wind farms is integrated in the modelling. We find that without sector coupling no offshore grid may develop, and that the higher the level of sector coupling, the higher the value of offshore grids. Therefore, it can be strongly stated that offshore grid infrastructure development should not be discussed as a separate political topic, but seen in connection to sector coupling.