Engaging Tourism Stakeholders in the Development of Climate Change Decision-Support Tools: A Case Study from Michigan, USA

2008 ◽  
Vol 12 (1) ◽  
pp. 25-42 ◽  
Author(s):  
Sarah Nicholls ◽  
Donald F. Holecek
Keyword(s):  
Climate Change ◽  
Decision Support ◽  
Decision Support Tools ◽  
Support Tools

scholarly journals Creating the data basis to adapt agricultural decision support tools to new environments, land management and climate change—A case study of the RiceAdvice App

2020 ◽  
Vol 206 (4) ◽  
pp. 423-432 ◽  
Author(s):  
Marc Cotter ◽  
Folkard Asch ◽  
Bayuh Belay Abera ◽  
Boshuwenda Andre Chuma ◽  
Kalimuthu Senthilkumar ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Support ◽  
Land Management ◽  
Decision Support Tools ◽  
Data Basis ◽  
Support Tools

scholarly journals Review of current approaches to spatially explicit urban vulnerability assessments: hazard complexity, data sources, and cartographic representations

GeoScape ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 47-61
Author(s):  
Pavel Raška ◽  
Martin Dolejš ◽  
Jan Pacina ◽  
Jan Popelka ◽  
Jan Píša ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Support ◽  
Data Sources ◽  
Small Scale ◽  
Spatially Explicit ◽  
Decision Support Tools ◽  
Multiple Hazards ◽  
Vulnerability Assessments ◽  
Support Tools ◽  
Combined Data

AbstractSocio-ecological hazards are processes that − depending on the vulnerability of societal systems − may have profound adverse impacts. For this reason, the current discourse in disaster risk reduction (DRR) has been experiencing a shift toward a vulnerability-led paradigm, raising new questions about how to address (i) the complexity of vulnerabilities to multiple hazards, (ii) their cultural, dynamic, and subjective character, and (iii) the effectiveness and legitimacy of vulnerability assessments as decision-support tools. In this paper, we present a review of 707 vulnerability studies (derived from the Clarivate WoS database; 1988−2018) with a particular focus on urban settings and spatially explicit assessments in order to evaluate current efforts to meet the aforementioned issues. The reviewed studies assessed vulnerabilities to 35 hazard types that were predominantly (n=603, 85%) analysed as single hazards (mostly seismic, flood, and groundwater contamination hazards, as well as climate change), whereas only 15% (n=104) of studies focused on multiple hazards (mostly atmospheric hazards). Within the spatially explicit vulnerability studies, almost 60% used data collected by the study itself (mostly seismic hazards), while statistical and combined data were both employed in 20% of cases (mostly floods, climate change, and social and political hazards). Statistical data were found to have only limited transferability, often being generalised to be applicable in small-scale studies, while reducing the role of cultural and contextual factors. Field research data provided high-resolution information, but their acquisition is time-consuming, and therefore fixed at a local scale and single temporal stage. Underlying hazard types and suitable data sources resulting in other differences found a preference towards the specific coverage and resolution of vulnerability maps that appeared in 44% of all reviewed studies. Altogether, the differences we found indicated a division of spatially explicit vulnerability research in two major directions: (i) geological and geomorphological studies focusing on physical vulnerability, using their own data surveys at a detailed scale and lacking links to other hazards, and (ii) other studies (mostly atmospheric hazards and socialpolitical hazards) focusing on social or combined vulnerabilities, using primarily statistical or combined data at a municipal, regional, and country scale with occasional efforts to integrate multiple hazards. Finally, although cartographic representations have become a frequent component of vulnerability studies, our review found only vague rationalisations for the presentation of maps, and a lack of guidelines for the interpretation of uncertainties and the use of maps as decision-support tools.

Decision Support Tools for Managing Unpredictability in the Supply Chain

2000 ◽  
Author(s):  
Carol Vesier
Keyword(s):  
Supply Chain ◽  
Decision Support ◽  
Inventory Management ◽  
Production Scheduling ◽  
Capital Investment ◽  
Investment Strategy ◽  
Decision Support Tools ◽  
Business Outcomes ◽  
Support Tools

Abstract Effectively managing unpredictability requires decision support tools that can predict the financial and business outcomes of various supply chain strategies. This paper will discuss the role of these decision support tools and their characteristics as well as review a case study. In the case study, decision support tools facilitated development of strategies that increased after tax profit by $140 Million. These strategies included: • Reliability improvement strategy: Identifying the reliability improvements that offered the biggest profitability impact. • Supply chain strategy: Defining inventory management and production scheduling rules that ensured order shipment within two days. • Capital investment strategy: Defining when new capacity should come on line as well as the minimum capital investment.

scholarly journals Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2642 ◽  
Author(s):  
Thuc D. Phan ◽  
James C. R. Smart ◽  
Ben Stewart-Koster ◽  
Oz. Sahin ◽  
Wade L. Hadwen ◽  
...  
Keyword(s):  
Climate Change ◽  
Decision Support ◽  
Resource Management ◽  
Bayesian Networks ◽  
Water Resource ◽  
Water Resource Management ◽  
Decision Support Tools ◽  
Management Measures ◽  
Support Tools ◽  
Economic Stressors

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.

scholarly journals Exploring Public Sector Managers’ Motivations in Deploying Decision Support Tools to the Street Level

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Chris McIntyre
Keyword(s):  
Decision Support ◽  
Public Sector ◽  
Digital Tools ◽  
Decision Support Tools ◽  
Structured Interviews ◽  
Senior Managers ◽  
Street Level ◽  
Support Tools ◽  
Public Sector Managers

This article explores the motivations of public sector managers in developing and deploying digital tools to support decision making at the front lines of public service delivery. Two digital decision support tools created by New Zealand’s Ministry of Social Development are presented as a case study, drawing primarily on semi-structured interviews with senior managers. Results provide empirical evidence that public sector managers deploy digital tools not to curtail, but to support street-level bureaucrats’ discretion. Managers appear to be motivated not by increased control over front-line staff, but, rather, by improving clients’ experience of the system and decreasing longterm service costs.

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