Demand Management Strategies to Enhance Climate Resilience

2018 ◽  
pp. 31-47
Author(s):  
Robert C. Brears
Keyword(s):  
Demand Management ◽  
Management Strategies ◽  
Climate Resilience

scholarly journals Community seed network in an era of climate change: dynamics of maize diversity in Yucatán, Mexico

2021 ◽  
Author(s):  
Marianna Fenzi ◽  
Paul Rogé ◽  
Angel Cruz-Estrada ◽  
John Tuxill ◽  
Devra Jarvis
Keyword(s):  
Climate Change ◽  
Crop Production ◽  
Management Strategies ◽  
Farming Systems ◽  
Seed Systems ◽  
Farming Community ◽  
Climate Resilience ◽  
Maize Diversity ◽  
Climatic Period ◽  
Small Holder Farmers

AbstractLocal seed systems remain the fundamental source of seeds for many crops in developing countries. Climate resilience for small holder farmers continues to depend largely on locally available seeds of traditional crop varieties. High rainfall events can have as significant an impact on crop production as increased temperatures and drought. This article analyzes the dynamics of maize diversity over 3 years in a farming community of Yucatán state, Mexico, where elevated levels of precipitation forced farmers in 2012 to reduce maize diversity in their plots. We study how farmers maintained their agroecosystem resilience through seed networks, examining the drivers influencing maize diversity and seed provisioning in the year preceding and following the 2012 climatic disturbance (2011–2013). We found that, under these challenging circumstances, farmers focused their efforts on their most reliable landraces, disregarding hybrids. We show that farmers were able to recover and restore the diversity usually cultivated in the community in the year following the critical climate event. The maize dynamic assessed in this study demonstrates the importance of community level conservation of crop diversity. Understanding farmer management strategies of agrobiodiversity, especially during a challenging climatic period, is necessary to promote a more tailored response to climate change in traditional farming systems.

Linking Residential Parking to Automobile Transportation Impact Outcomes at a Development Level

2020 ◽  
pp. 036119812096479
Author(s):  
Kristina M. Currans ◽  
Gabriella Abou-Zeid ◽  
Nicole Iroz-Elardo
Keyword(s):  
Travel Behavior ◽  
Travel Demand ◽  
Demand Management ◽  
Management Strategies ◽  
Land Development ◽  
Future Research ◽  
Alternative Mode ◽  
Travel Demand Management ◽  
Development Level ◽  
Automobile Demand

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

scholarly journals Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4154 ◽  
Author(s):  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Demand ◽  
Learning Strategy ◽  
Demand Management ◽  
Management Strategies ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Similarity Function ◽  
Active Components

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

Exploring the Influence of Consumer Demographics on Online Purchase Benefits

2019 ◽  
Vol 8 (4) ◽  
pp. 303-316
Author(s):  
Suhail Ahmad Bhat ◽  
Mushtaq Ahmad Darzi
Keyword(s):  
Data Collection ◽  
Online Shopping ◽  
Demand Management ◽  
Management Strategies ◽  
Age Groups ◽  
Sampling Technique ◽  
Three Dimensions ◽  
Unique Contribution ◽  
Sociodemographic Variables ◽  
Consumer Demographics

The purpose of the study is to explore the influence of sociodemographic variables (gender & age) on consumers’ perception towards online shopping. The article specifically focuses on purchase benefit that has been categorized into three dimensions such as convenience, interactivity and enjoyment, which are associated with demand management of an Indian consumer. The study has adopted survey by questionnaire method for data collection from online shoppers. Quota sampling technique was used for data collection from 660 e-consumers. Data analysis and hypotheses testing were performed through descriptive (mean, percentage and standard deviation) and comparative statistics (Z-test and one-way ANOVA). Analysis revealed that gender does not have any influence on the purchase benefit variables; however, the significant mean difference was observed between ‘Below 20’ and ‘21–30’ years age groups for convenience and interactivity only. The findings of the current study will be helpful to web store executives in building marketing capabilities and demand management strategies for different age groups. The study has a unique contribution of filling the knowledge gaps in the existing literature by statistically evaluating the role of less-studied sociodemographic variables, that is, age and gender on the consumers’ intention towards purchase benefits associated with online shopping.

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