Assessing local planning capacity to promote environmentally sustainable residential development

Smart growth and sustainability planning have, in recent years, become central issues in planning discourse. Scholars have argued that planning capacity at the local government level is critical for smart growth planning, and that planners have a fundamental role to play in advancing local and regional sustainability. In this paper, we investigate the extent to which local planning capacity enables communities to promote more sustainable, smart growth residential development. Based on a 2013 survey of 38 county and 53 municipal governments in the state of Wisconsin, USA, this study finds that the majority of the sample communities have permitted residential developments characterized as transit-oriented, New Urbanist, mixed use, infill developments, or conservation subdivisions as alternatives to low-density, automobile-dependent conventional developments. The study also finds that jurisdictions with higher planning capacities are more likely to overcome significant barriers to more sustainable residential development.     

A multi-criteria decision analysis of solid waste treatment options in Pakistan: Lahore City—a case in point

Population of the world is increasing day by day, resulting in enormous amount of waste production. In the modern age of great technological advancements, there needs to be a systematic method to keep the environment clean. The waste management activities, i.e., collection, transport and disposal, pose a great challenge to the waste managers as they have to factor in various eclectic factors such as land availability, facilities available, budget, time required and the impact it would have on the environment, while tackling this problem. Lahore, despite being the most developed city of Pakistan, does not have a suitable solid waste management system. An increasing population leads to more waste generation, and in Lahore the situation is no different. Several waste management companies are working in the city, but as of yet they have not been able to make significant inroads to completely eradicate the problem. The aim of this paper is to suggest a suitable way for dealing with the waste. To accomplish this aim, a hierarchy-based model is used, considering six criteria and five alternatives. We used multi-criteria decision analysis to decide among different waste management alternatives. Forecasting has been used to find the population and waste produced over the years. Analytical hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used to rank the feasible alternative. The results show that the population and waste were increasing drastically. Aerobic digestion was ranked as the best alternative for waste management according to AHP and TOPSIS, but there is great variation among the rank of other alternatives.     

Cryptocurrency: governance for what was meant to be ungovernable

Cryptocurrencies have the potential to revolutionize the exchange of information and money through blockchain and distributed ledger technologies. Despite the promise of such underlying technologies, their reliance upon distributed consensus processes to approve software updates raises the potential for governance failures to destabilize a given cryptocurrency. These governance failures, known as ‘hard forks,’ can separate a cryptocurrency into two rival camps. Where such events can destabilize a given cryptocurrency’s value, and instill distrust in the capacity of a cryptocurrency to survive as a reliable vehicle of exchange, it is imperative for the cryptocurrency community to improve their governance processes and limit the potential for hard forks to occur. While the distributed nature of cryptocurrency governance makes any traditional governing process unlikely to succeed, anticipatory approaches that establish thresholds and metrics to determine when software reform is necessary may help alleviate the governance failures presented by many hard forks.     

Flood resilience: consolidating knowledge between and within critical infrastructure sectors

Flood resilience has been rising up the political, economic and social agendas. Taking an integrated systems approach, using the right design guidance and tools and ensuring that education is in place for all stakeholders are three themes which are intrinsically linked to delivering flood resilience. This paper reviews these themes across the academic research, policy landscape and practitioner approaches, drawing conclusions on the way forward to increase our societies resilience to floods. The term ‘flood resilience’ is being increasingly used, however, it remains to be clearly defined and implemented. The UK, USA and Australia are leading the way in considering what flood resilience really means, but our review has found few examples of action underpinned by an understanding of systems and complexity. This review investigates how performance objectives & indicators are currently interpreted in guidance documents. It provides an in-depth exploration of the methods, that although developed through European and US expertise, can be used for worldwide application. Our analysis highlights that resilience is often embedded in engineering education and frequently linked to risk. This may however, mask the importance of resilience and where it differs from risk. With £2.6 billion to be spent in the UK over the next 6 years on strengthening the country’s flood and coastal defences, this is the opportunity to rethink resilience from a systems approach, and embed that learning into education and professional development of engineers. Our conclusions indicate how consolidating flood resilience knowledge between and within critical infrastructure sectors is the way forward to deliver flood resilience engineering.     

Economic and Ecological Rules for Water Quality Trading1

Horan, Richard D. and James S. Shortle, 2011. Economic and Ecological Rules for Water Quality Trading. Journal of the American Water Resources Association (JAWRA) 47(1):59‐69. DOI: 10.1111/j.1752‐1688.2010.00463.x Abstract: Emissions trading in textbook form uses markets to achieve pollution targets cost‐efficiently. This result is accomplished in markets that regulators can implement without knowing pollution abatement costs. The theoretical promise of emissions trading, along with real‐world success stories from air emissions trading, has led to initiatives to use trading for water pollution control. Yet, trading, particularly when it involves nonpoint sources of pollution, requires significant departures from the textbook concept. This paper explores how features of water quality problems affect the design of markets for water pollution control relative to textbook emissions markets. Three fundamental design tasks that regulators must address for pollution trading to achieve an environmental goal at low cost are examined: (1) defining the point and nonpoint commodities to be traded, (2) defining rules governing commodity exchange, and (3) setting caps on the commodity supplies so as to achieve an environmental target. We show that the way in which these tasks are optimally addressed for water quality markets differs significantly from the textbook model and its real‐world analogs. We also show that the fundamental appeal of emissions trading is lost in the case of realistic water quality markets, as market designs that reduce the costs of achieving water quality goals may no longer be implementable without the regulatory authority having information on abatement costs.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries

One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting (RCAFF) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.     

Detention Outlet Retrofit Improves the Functionality of Existing Detention Basins by Reducing Erosive Flows in Receiving Channels

By discharging excess stormwater at rates that more frequently exceed the critical flow for stream erosion, conventional detention basins often contribute to increased channel instability in urban and suburban systems that can be detrimental to aquatic habitat and water quality, as well as adjacent property and infrastructure. However, these ubiquitous assets, valued at approximately $600,000 per km² in a representative suburban watershed, are ideal candidates to aid in reversing such cycles of channel degradation because improving their functionality would not necessarily require property acquisition or heavy construction. The objective of this research was to develop a simple, cost‐effective device that could be installed in detention basin outlets to reduce the erosive power of the relatively frequent storm events (~ < two‐year recurrence) and provide a passive bypass to maintain flood control performance during infrequent storms (such as the 100‐year recurrence). Results from a pilot installation show that the Detain H₂O device reduced the cumulative sediment transport capacity of the preretrofit condition by greater than 40%, and contributed to reduced flashiness and prolonged baseflows in receiving streams. When scaling the strategy across a watershed, these results suggest that potential gains in water quality and stream channel stability could be achieved at costs that are orders of magnitude less than comparable benefits from newly constructed stormwater control measures.     

The development of a method to determine the burden of climate change on different health outcomes at a local scale: a case study in Osaka Prefecture,Japan

Climate change impacts human health in a variety of ways. Variables including the climate-related risk factor, the health outcome and location all determine the nature and extent of the impact. The existence of different pathways and endpoints presents a problem for quantifying and comparing impacts. Disability-adjusted life year (DALY) provides a common scale, whereby the impact of climate change on both acute and chronic health outcomes can be compared. This study presents a methodology to calculate the impact of climate change on human health at a local scale, using cardiovascular disease (CVD) and meteorological disaster-related injuries (DRIs) in Osaka Prefecture, Japan, as applied case studies. An additional very fine scale assessment of CVD conducted at the neighbourhood level to demonstrate the importance of conducting risk assessments at a local level. The comparative results calculated the impact of climate change in 2050 to be 16.866 DALY/100,000 population for CVD and 0.645 DALY/100,000 for meteorological DRIs. The actual impact of climate change by 2050 on CVD is judged to be higher, although the relative risk was projected to be lower (1.006, compared to 1.263 for meteorological DRIs). The fine scale assessment revealed the variations in the projected impact of climate change on CVD for all administrative zones in Osaka Prefecture. The range of impacts varied from 0 to 114.29 DALY/100,000. The results demonstrate the applicability of using DALY to quantify the impact of climate change on different health outcomes, using a transferable methodology, and provide information that enables evidence-based prioritisation of climate change adaptation strategies at a local scale.     

Optimizing angles of rooftop photovoltaics,ratios of solar to vegetated roof systems,and economic benefits,in Portland,Oregon, USA

This study analyzed insolation data to account for multiple scattering in calculating optimal tilt angles for stationary and seasonally moving photovoltaics on three different roof types in the US Pacific Northwest: vegetated roofs, white roofs, and dark roofs. Using these results, we modeled the energy savings for vegetated roofs and roofs covered in varying numbers of photovoltaic panels. We then calculated the net present value, internal rate of return, and other economic measures for all possible combinations of covering rooftops in mixes of photovoltaic arrays and vegetation, accounting for installation costs, proposed carbon taxes, stormwater management discounts, and other relevant factors. Our results quantify how, in the US Pacific Northwest and similar locations, photovoltaics produce higher returns on investment than do vegetated roofs for new buildings, while vegetated roofs produce better returns on investment than do photovoltaics for older buildings. This is important because in many areas, some buildings have photovoltaics when a vegetated roof would have been more cost and energy efficient, while other buildings have vegetated roofs when photovoltaics would have been more cost and energy efficient. Potential applications include modifying incentive programs and other policies to account properly for building age, use, and other relevant factors to ensure building owners make the most energy-efficient decisions between photovoltaic versus vegetated roof installation. Our research also demonstrates how positive returns on investment can be realized in the US Pacific Northwest and similar regions through vegetated roofs and photovoltaics provided they are each installed optimally.