Use of multiple indicators to compare sustainability performance of organic vs conventional vineyard management

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

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Sustainability Assessment of Public Transport, Part I—A Multi-Criteria Assessment Method to Compare Different Bus Technologies

Sustainability ◽

10.3390/su13020825 ◽

2021 ◽

Vol 13 (2) ◽

pp. 825

Author(s):

Jonas Ammenberg ◽

Sofia Dahlgren

Keyword(s):

Fossil Fuels ◽

Sustainability Assessment ◽

Public Procurement ◽

Assessment Method ◽

County Level ◽

Participatory Process ◽

Problem Context ◽

The Public ◽

Sustainability Performance ◽

Sustainability Assessments

This article departs from the perspective of Swedish regional transport authorities and focuses on the public procurement of bus transports. Many of these public organizations on the county level have the ambition to contribute to a transition involving the continued marginalization of fossil fuels and improved sustainability performance. However, there are several renewable bus technologies to choose between and it can be difficult to know what alternative (or combination) is preferable. Prior research and the authors’ experiences indicate a need for improved knowledge and supportive methods on how sustainability assessments can support public procurement processes. The purpose of this article is to develop a multi-criteria assessment (MCA) method to support assessments of public bus technologies’ sustainability. The method, which was established in an iterative and participatory process, consists of four key areas and 12 indicators. The article introduces the problem context and reviews selected prior research of relevance dealing with green or sustainable public procurement and sustainability assessments. Further on, the process and MCA method are presented and discussed based on advice for effective and efficient sustainability assessments. In the companion article (Part II), the MCA method is applied to assess several bus technologies involving biodiesel, biomethane, diesel, electricity, ethanol and natural gas.

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Beyond Bounded Rationality: CEO Reflective Capacity and Firm Sustainability Performance

Management and Organization Review ◽

10.1017/mor.2021.4 ◽

2021 ◽

pp. 1-38

Author(s):

Yingya Jia ◽

Anne S. Tsui ◽

Xiaoyu Yu

Keyword(s):

Decision Making ◽

Bounded Rationality ◽

Three Dimensional ◽

Strategic Decision ◽

Future Research ◽

Sustainability Performance ◽

Business Executives ◽

Dimensional Measure ◽

Reflective Capacity ◽

Mediating Mechanisms

ABSTRACT Optimal or rational decision making is not possible due to informational constraints and limits in computation capability of humans (March & Simon, 1958; March, 1978). This bounded rationality serves as a filtering process in decision making among business executives (Hambrick & Mason, 1984). In this study, we propose the concept of CEO reflective capacity as a behavior-oriented cognitive capability that may overcome to some extent the pervasive limitation of bounded rationality in executive decision-making. Following Hinkin's (1998) method and two executive samples, we developed and validated a three-dimensional measure of CEO reflective capacity. Based on two-wave surveys of CEOs and their executive-subordinates in 213 Chinese small-medium sized firms, we tested and confirmed three hypotheses on how CEO reflective capacity is related to a firm's sustainability performance (including economic, societal, and environmental dimensions) through the mediating mechanisms of strategic decision comprehensiveness and CEO behavioral complexity. We discuss the contribution of this study to the literature on the upper echelons and information processing perspectives. We also identify the implications for future research on strategic leadership and managerial cognition in complex and dynamic contexts.

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Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

Sustainability ◽

10.3390/su13073870 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3870

Author(s):

Mehrbakhsh Nilashi ◽

Shahla Asadi ◽

Rabab Ali Abumalloh ◽

Sarminah Samad ◽

Fahad Ghabban ◽

...

Keyword(s):

Performance Assessment ◽

Prediction Accuracy ◽

Prediction Models ◽

Sustainability Assessment ◽

Regression Trees ◽

Machine Learning Techniques ◽

Coefficient Of Determination ◽

Sustainability Performance ◽

Learning Techniques ◽

Self Organizing

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

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