Development of multi-functional streetscape green infrastructure using a performance index approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a performance evaluation framework for streetscape vegetation. A performance index (PI) is conceived using the following seven traits, specific to the street environments – Pollution Flux Potential (PFP), Carbon Sequestration Potential (CSP), Thermal Comfort Potential (TCP), Noise Attenuation Potential (NAP), Biomass Energy Potential (BEP), Environmental Stress Tolerance (EST) and Crown Projection Factor (CPF). Its application is demonstrated through a case study using fifteen street vegetation species from the UK, utilising a combination of direct field measurements and inventoried literature data. Our results indicate greater preference to small-to-medium size trees and evergreen shrubs over larger trees for streetscaping. The proposed PI approach can be potentially applied two-fold: one, for evaluation of the performance of the existing street vegetation, facilitating the prospects for further improving them through management strategies and better species selection; two, for planning new streetscapes and multi-functional biomass as part of extending the green urban infrastructure

Stimulating urban transition and transformation to achieve sustainable and resilient cities

Political decision-makers need to consider the various challenges and opportunities that climate change can bring, and they must take decisions under high uncertainty to achieve resilient cities. Here, we synthesise the push and pull approaches reported in the literature and employed in practice to achieve sustainable and resilient cities. First, we present a literature review which identified the major research fields on transition theories, frameworks and methods that underpin this concept. We analyse the conditions for change, identify enablers or triggers for change at governance level for transitioning a city towards sustainability and resilience. We discuss the theories, frameworks and methods which can be used to address the urban climate change challenge at city level. Second, we present an empirical approach based on stakeholder participation that we conducted to detect the conditions for change. We report on the design and implementation of stakeholder exercises that helped us detecting the conditions for changes. Third, we combine the information obtained from these stakeholder exercises with that extracted from the literature in order to provide a fuller picture on how stimulate the transition and transformation to achieve sustainable and resilient cities. Based on our literature review and empirical approach, we formulate an integrated conceptual model for transition that enables the design of adaptation (and mitigation) strategies that consider the triggers of change. Uniquely we identified 8 triggers of change, including authority and political leadership, learning from disasters, co-responsibility, increased public-private interface, social participation and the living lab approach to innovation. The proposed model can be applied to the whole city or to a certain sector of the city (e.g. energy). We demonstrate that triggers of change help to overcome planning and implementation barriers and move the socio-ecological and socio-technical systems of any city towards those of a resilient city.This work was supported by the European Community's Seventh Framework Programme: Grant Agreement No. 308497, Project RAMSES “Reconciling Adaptation, Mitigation and Sustainable Development for Cities”, 2012–2017. In addition, this study has received partial funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 653522 (RESIN−Climate Resilient Cities and Infrastructures project)

Development of multi-functional streetscape green infrastructure using a performance index approach

This paper presents a performance evaluation framework for streetscape vegetation. A performance index (PI) is conceived using the following seven traits, specific to the street environments – Pollution Flux Potential (PFP), Carbon Sequestration Potential (CSP), Thermal Comfort Potential (TCP), Noise Attenuation Potential (NAP), Biomass Energy Potential (BEP), Environmental Stress Tolerance (EST) and Crown Projection Factor (CPF). Its application is demonstrated through a case study using fifteen street vegetation species from the UK, utilising a combination of direct field measurements and inventoried literature data. Our results indicate greater preference to small-to-medium size trees and evergreen shrubs over larger trees for streetscaping. The proposed PI approach can be potentially applied two-fold: one, for evaluation of the performance of the existing street vegetation, facilitating the prospects for further improving them through management strategies and better species selection; two, for planning new streetscapes and multi-functional biomass as part of extending the green urban infrastructure

Costs of sea dikes – regressions and uncertainty estimates

Failure to consider the costs of adaptation strategies can be seen by decision makers as a barrier to implementing coastal protection measures. In order to validate adaptation strategies to sea-level rise in the form of coastal protection, a consistent and repeatable assessment of the costs is necessary. This paper significantly extends current knowledge on cost estimates by developing – and implementing using real coastal dike data – probabilistic functions of dike costs. Data from Canada and the Netherlands are analysed and related to published studies from the US, UK, and Vietnam in order to provide a reproducible estimate of typical sea dike costs and their uncertainty. We plot the costs divided by dike length as a function of height and test four different regression models. Our analysis shows that a linear function without intercept is sufficient to model the costs, i.e. fixed costs and higher-order contributions such as that due to the volume of core fill material are less significant. We also characterise the spread around the regression models which represents an uncertainty stemming from factors beyond dike length and height. Drawing an analogy with project cost overruns, we employ log-normal distributions and calculate that the range between 3x and x∕3 contains 95 % of the data, where x represents the corresponding regression value. We compare our estimates with previously published unit costs for other countries. We note that the unit costs depend not only on the country and land use (urban/non-urban) of the sites where the dikes are being constructed but also on characteristics included in the costs, e.g. property acquisition, utility relocation, and project management. This paper gives decision makers an order of magnitude on the protection costs, which can help to remove potential barriers to developing adaptation strategies. Although the focus of this research is sea dikes, our approach is applicable and transferable to other adaptation measures

Costs of sea dikes – regressions and uncertainty estimates

Failure to consider the costs of adaptation strategies can be seen by decision makers as a barrier to implementing coastal protection measures. In order to validate adaptation strategies to sea-level rise in the form of coastal protection, a consistent and repeatable assessment of the costs is necessary. This paper significantly extends current knowledge on cost estimates by developing – and implementing using real coastal dike data – probabilistic functions of dike costs. Data from Canada and the Netherlands are analysed and related to published studies from the US, UK, and Vietnam in order to provide a reproducible estimate of typical sea dike costs and their uncertainty. We plot the costs divided by dike length as a function of height and test four different regression models. Our analysis shows that a linear function without intercept is sufficient to model the costs, i.e. fixed costs and higher-order contributions such as that due to the volume of core fill material are less significant. We also characterise the spread around the regression models which represents an uncertainty stemming from factors beyond dike length and height. Drawing an analogy with project cost overruns, we employ log-normal distributions and calculate that the range between 3x and x∕3 contains 95 % of the data, where x represents the corresponding regression value. We compare our estimates with previously published unit costs for other countries. We note that the unit costs depend not only on the country and land use (urban/non-urban) of the sites where the dikes are being constructed but also on characteristics included in the costs, e.g. property acquisition, utility relocation, and project management. This paper gives decision makers an order of magnitude on the protection costs, which can help to remove potential barriers to developing adaptation strategies. Although the focus of this research is sea dikes, our approach is applicable and transferable to other adaptation measures

Transformer Training Strategies for Forecasting Multiple Load Time Series

In the smart grid of the future, accurate load forecasts on the level of individual clients can help to balance supply and demand locally and to prevent grid outages. While the number of monitored clients will increase with the ongoing smart meter rollout, the amount of data per client will always be limited. We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients. In experiments with two datasets containing load time series from several hundred clients, we find that the global training strategy is superior to the multivariate and local training strategies used in related work. On average, the global training strategy results in 21.8% and 12.8% lower forecasting errors than the two other strategies, measured across forecasting horizons from one day to one month into the future. A comparison to linear models, multi-layer perceptrons and LSTMs shows that Transformers are effective for load forecasting when they are trained with the global training strategy

Non-Sequential Machine Learning Pipelines with pyWATTS

pyWATTS is an open-source Python-based workflow automation tool for time series analysis. pyWATTS simplifies the evaluation process and the design of repetitive machine learning experiments with time series by providing a powerful pipeline solution including preprocessing and analysis modules. Unlike existing sequential pipeline solutions, pyWATTS enables more complex and non-sequential pipelines. Such non-sequential pipelines are beneficial, for example, in forecasting electrical load time series, detecting anomalies in time series, or generating synthetic time series. This talk presents the basic ideas of pyWATTS, the current features, and existing use cases. It also gives an outlook on the future developments of pyWATTS and the cooperation with sktime

Independent Review of the 2021 CDP submission based on SCATTER by Newcastle City Council

The path to Net-Zero is always a complex one. Newcastle City Council has long been a leader on green city and climate policy, recently has developed the Net Zero Newcastle: 2030 Action Plan and currently is a CDT tier-A city (based on its 2020 submission). In 2020 Newcastle City Council (NCC) made a Carbon Disclosure Project (CDP) submission for the first time. NCC deems this as a successful submission, which scored highly (A score) as a city. NCC therefore set the bar high and need to maintain that standard going forwards, especially with COP looming. To this end, one of the areas where NCC can improve its 2021 submission is to have an independent evaluation of our emissions inventory for the city, which by and large is based on national data which is extrapolated to local level and is largely based on the attached analysis through a tool called SCATTER which has been developed by Anthesis and made available to UK Local Authorities through a time-limited grant support from BEIS central government. This exercise was undertaken jointly by Northumbria and Newcastle universities, in a pro-bono activity in their guise as civic universities supporting the implementation of the UN{\textquoteright}s Sustainable Development Goals, including as members of the COP26 Universities Network1 which both Northumbria and Newcastle University are part of. The independent evaluation took its cue from initial suggestions from NCC to verify – as far as possible within the time frame and the resources at hand - its 2021 SCATTER-based CDP submission (data, questionnaire, and local proxy data suggestions for the future) and to provide some general advice on how NCC (alongside its partners across the city and region) could potentially improve its disclosure and data strategy (including and especially with a more bottom-up one) in the future. A series of (online) meetings took place between NCC colleagues and the independent evaluations over June and July 2021, including an audit-style one where NCC colleagues demonstrated how they access and use the SCATTER tool, and obtain the data from there. Live questions were asked by the independent evaluators in the meeting, in addition to a list of questions that was shared with the NCC team only some hours before this audit-style meeting. NCC colleagues made a series of documents and data files available during the course of the exercise, including some reference examples after the audit-style meeting. The cross-universities independent evaluation team considers that Newcastle city Council is engaging well with the process and procedure of the SCATTER-based CDP disclosure activity, is committed to understand better inventory and the data pre-populated by the Anthesis Group for its submission, is clearly on the way to consider potential improvements for the future which may also rest (partly) on a more bottom-up (locally generated and verified data). The evaluators have made some comments on the SCATTER tool and methodological approach in this report, and furthermore discuss in general terms some of the current limitations. The report also provides some general pointers as to how a more bottom-up data strategy could be built in the future for both CDP-compliant disclosure, but also to consider the links between data recording and interventions/policy impacts on the journey towards Net Zero (or carbon neutrality) via meeting the city{\textquoteright}s 2030 carbon emission reductions target