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Wind characteristics (e.g., mean wind speed, gust factor, turbulence intensity and integral scale, etc.) are quite scattered in different measurement conditions, especially during typhoon and/or hurricane processes, which results in the structural engineer ambiguously determining the wind parameters in wind-resistant design of buildings and structures in cyclone-prone regions. In tropical cyclones (including typhoons and hurricanes), the inconsistent wind characteristics may be in part ascribed to the complex flow structure with the coexistence of both mechanical and convective turbulence in the boundary layer of tropical cyclones. Another significant contribution to the scattered wind characteristics is due to various measurement conditions (e.g., terrain exposure and height) and data processing schemes (e.g., averaging time). The removal of the inconsistency in the field-measurement system may offer a more rational comparison of measured wind data from various observation platforms, and hence facilitates a better identification scheme of the wind characteristics to guide the urban planning design and wind-resistant design of buildings and structures. In this study, an analytical framework was firstly proposed to eliminate the potential observation-related effects in wind characteristics and then the wind characteristics of seven field measured tropical cyclones (four typhoons and three hurricanes) were comparatively investigated. Specifically, field measurements of wind characteristics were converted to a standard reference station with a roughness length of 0.03 m, observation duration of 10 min for mean wind and averaging time of 3 s for gusty wind at a 10 m height. The differences of the measured wind characteristics between the typhoons and hurricanes were highlighted. The standardized turbulent wind characteristics under the analytical framework for typhoons and hurricanes were compared with the corresponding recommendations in standard of American Society of Civil Engineers (ASCE 7-10) and Architectural Institute of Japan Recommendations for Loads on Buildings (AIJ-RLB-2004).

Understanding the system of connections between societal contexts and policy outcomes in municipal governments provides important insights into how community sustainability happens, and why it happens differently in various communities. A growing body of research in recent years has focused on understanding the socio-economic characteristics of communities and cities that are recognized as policy leaders in sustainability. In this paper, we expand the focus beyond the leaders in sustainability as we apply a selection of socio-demographic influences of community sustainability to a large sample of U.S. communities using community classification analytics to identity a range of community types and levels of engagement with sustainability. Our typology presents an integrated and comprehensive perspective on the structure of community sustainability in the United States, highlighting key points of comparison between human capital factors such as population size and density, affluence, home ownership, and adoption of sustainability policy. The analysis provides new insights not only about community leaders in sustainability, but also communities with the civic and social capacity to do more, and the challenges that may inhibit sustainability efforts in others.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

Energy is consumed at every stage of the cycle of water production, distribution, end use, and recycled water treatment. Understanding the nexus of energy and water may help to minimize energy and water consumption and reduce environmental emissions. However, the interlinkages between water and energy have not received adequate attention. To address this gap, this paper disaggregates and quantifies the energy consumption of the entire water cycle process in Beijing. The results of this study show that total energy consumption by water production, treatment and distribution, end use, and recycled water reuse amounts to 55.6 billion kWh of electricity in 2015, or about 33% of the total urban energy usage. While water supply amount increased by only 10% from 2005 to 2015, the related energy consumption increased by 215% due to water supply structural change. The Beijing municipal government plans to implement many water saving measures in the area from 2016 to 2020, however, these policies will increase energy consumption by 74 million kWh in Beijing. This study responds to the urgent need for research on the synergies between energy and water. In order to achieve the goal of low-energy water utilization in the future, water and energy should be integrated in planning and management.

This paper develops a strategy for the continuing and improved supply of woodfuels to urban and industrial consumers in Sub-Sahara Africa. It argues that continued use of these fuels is not only a necessity, but is also in the best economic interest of most of the countries in this region. It shows that intensified and more orderly utilization of woodfuels can help to enhance, rather than impinge upon environmental parameters. Some examples are provided that illustrate how such strategies can be put into practice.

We present Vehicle Energy Dataset (VED), a novel large-scale dataset of fuel and energy data collected from 383 personal cars in Ann Arbor, Michigan, USA. This open dataset captures GPS trajectories of vehicles along with their time-series data of fuel, energy, speed, and auxiliary power usage. A diverse fleet consisting of 264 gasoline vehicles, 92 HEVs, and 27 PHEV/EVs drove in real-world from Nov, 2017 to Nov, 2018, where the data were collected through onboard OBD-II loggers. Driving scenarios range from highways to traffic-dense downtown area in various driving conditions and seasons. In total, VED accumulates approximately 374,000 miles. We discuss participant privacy protection and develop a method to de-identify personally identifiable information while preserving the quality of the data. After the de-identification, we conducted case studies on the dataset to investigate the impacts of factors known to affect fuel economy and identify energy-saving opportunities that hybrid-electric vehicles and eco-driving techniques can provide. The case studies are supplemented with a number of examples to demonstrate how VED can be utilized for vehicle energy and behavior studies. Potential research opportunities include data-driven vehicle energy consumption modeling, driver behavior modeling, machine and deep learning, calibration of traffic simulators, optimal route choice modeling, prediction of human driver behaviors, and decision making of self-driving cars. We believe that VED can be an instrumental asset to the development of future automotive technologies. The dataset can be accessed at https://github.com/gsoh/VED. 11 pages, 15 figures

Abstract This paper formulates a model that assesses the economic and policy implications of the adoption and operation of microgrids. The recent climate agreement between the US and China as well as the historic Lima accord on climate change are prompting a massive demand in climate-friendly technologies. Given the increasing demand for electricity and the mounting costs of electricity supply associated with maximizing system performance through an optimal economic scheduling of microgrids, decision makers and policymakers are already facing the problem of sketching energy management strategies for localized operation and grid penetration. We employ a modified Benders' decomposition approach to explore the optimal scheduling of supply from different technology units in the microgrid in the face of supply and demand uncertainties. We guarantee that the power generated by the units meets systemic load while satisfying the primary operational and technological constraints. We implement our model in a quantitative numerical analysis where we investigate the operational regulation in the light of a public environmental policy. We extend prior research on this subject by uncovering a greater reliance on the grid under simultaneous uncertainties in demand and supply. We unlock a strategy for a proper mitigation of the effects of emissions. We find that carbon abatement policy can be used to motivate microgrids operators to invest in fuel cells and natural gas technologies when dealing with uncertainty.

A promising route to transition wastewater treatment facilities (WWTFs) from energy-consuming to net energy-positive is to retrofit existing facilities with process modifications, residual biosolid upcycling, and effluent thermal energy recovery. This study assesses the economics and life cycle environmental impacts of three proposed retrofits of WWTFs that consider thermochemical conversion technologies, namely, hydrothermal liquefaction, slow pyrolysis, and fast pyrolysis, along with advanced bioreactors. The results are in turn compared to the reference design, showing the retrofitting design with hydrothermal liquefaction, and an up-flow anaerobic sludge blanket has the highest net present value (NPV) of $177.36MM over a 20-year plant lifetime despite 15% higher annual production costs than the reference design. According to the ReCiPe method, chlorination is identified as the major contributor for most impact categories in all cases. There are several uncertainties embedded in the techno-economic analysis and life cycle assessment, including the discount rate, capital investment, sewer rate, and prices of main products; among which, the price of biochar presents the widest variation from $50 to $1900/t. Sensitivity analyses reveal that the variation of discount rates causes the most significant changes in NPVs. The impact of the biochar price is more pronounced in the slow pyrolysis-based pathway compared to the fast pyrolysis since biochar is the main product of slow pyrolysis.