Measuring urban digitalization using cognitive mapping and the best worst method (BWM)

Vieira, Fabiana C.; Ferreira, Fernando; Govindan, Kannan; Ferreira, Neuza C. M. Q. F.; Banaitis, Audrius

doi:10.1016/j.techsoc.2022.102131

Cited by 16 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we comprehensively used various coupled and spatial–temporal effect models to measure and analyze the coupling between urban digitalization and green development in China and the four economic zones, which provides an empirical basis for green sustainable development in China’s urban digitalization construction process. Compared with the existing studies that separately measure urban digitization [ 97 , 98 ] and green development [ 99 , 100 ], or analyze the relationship between the two from the perspective of the unidirectional impact of the former on the latter [ 12 , 101 ], based on the perspective of coordination and interaction, in this study, we systematically measured the spatial–temporal differentiation, dynamic evolution and spatial effects of urban digitalization and green development, analyzed the current pattern and future trend of the CD&GDD, and comprehensively and systematically grasped the development law of the coupling coordination between digitalization and green development at the regional city level to solve the dilemma of the “energy rebound effect” of digitalization [ 9 , 102 ], and to provide a reasonable reference for the realization of urban green and high-quality development.…”

Section: Discussionmentioning

confidence: 99%

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

Guo

Diao

2022

IJERPH

View full text Add to dashboard Cite

The coordinated promotion of urban digitalization and green development is an inevitable requirement for sustainable development in the digital age. Based on the coupling mechanism of urban digitalization and green development, in this study, we took 282 cities at the prefecture level and above in China from 2011 to 2019 as the research object, and we constructed the evaluation index system and calculated the coupling coordination degree (CD&GDD) of the two through the coupling coordination degree model. We further used the Dagum Gini coefficient, kernel density estimation, Markov chain and Moran’s I to assess the spatial effects of the regional differences, dynamic evolution trends and degree of coupling coordination. The results show the following: (1) The level of urban digitalization and green development show a fluctuating upward trend, and the interaction between the two is obvious. (2) Although the CD&GDD of most cities is continuously improving, it is still at a low level. There are large differences in the levels between the regions. (3) The inter-regional differences are the main source of the large overall differences in the CD&GDD in China, and these are mainly composed of the hypervariable density and net differences between the regions. (4) The phenomenon of “club convergence” exists in the CD&GDD. (5) The coupling coordination relationship between cities has a substantial spatial effect, and the spatial effect has obvious regional heterogeneity. The results and conclusions provide a reference for developing countries to promote green and low-carbon urban development.

show abstract

Section: Discussionmentioning

confidence: 99%

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

Guo

Diao

2022

IJERPH

View full text Add to dashboard Cite

show abstract

“… Objectives Tools Sources Selection of sustainable and resilient IoT supplier Spherical fuzzy BWM and TRUST Bonab et al [ 75 ] Evaluating man-made risks to urban areas and crucial resources Grey BWM-Grey MARCOS Bitarafan et al [ 76 ] Evaluating barriers to implementing circular economy in the electronics industry BWM-ISM-MICMAC Debnath et al [ 74 ] Exploring the Industry 5.0 challenges for post-pandemic supply chain sustainability BWM-ISM-MICMAC Karmaker et al [ 53 ] Analyzing Warehouse Accident Risks during Picking and Material Handling BWM-Grey Relational Analysis Hsu et al [ 77 ] Examining sustainable entrepreneurship determinants in SMEs. Cognitive mapping-BWM Mendes et al [ 78 ] Quantifying the Level of Digitalization in Municipalities Cognitive mapping-BWM Vieira et al [ 79 ] …”

Section: Methodsmentioning

confidence: 99%

Analysis of the fire risks and mitigation approaches in the apparel manufacturing industry: Implications toward operational safety and sustainability

Siraj,

Debnath,

Payel

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…Cognitive mapping is a graphical representation that seeks to reflect decision makers' values, objectives, ideas, and experiences regarding complex decision problems (Ackermann & Eden, 2010). Various authors (e.g., Belton & Stewart, 2002;Eden, 2004;Vaz et al, 2022;Vieira et al, 2022) have advocated using cognitive mapping as a tool due to its great capability for structuring and clarifying complicated issues. According to Ferreira et -The calculation of blight levels was limited because the index is an average of the individual scores for each property, which may not correctly reflect reality.…”

Section: Problem-structuring Methods (Psms) and Cognitive Mappingmentioning

confidence: 99%

Ranking Residential Neighborhoods Based on Their Sustainability: A Cm-BWM Approach

Andrade

Ferreira

Correia

2022

International Journal of Strategic Property Management

View full text Add to dashboard Cite

Population growth and rapid urbanization have consequences that are reflected in the economic, environmental, and social stability of city-residential neighborhoods. These impacts directly affect not only residents but also real estate markets and local governments. The professionals working in the latter entities have become increasingly concerned about urban sustainability and its strategic integration into their plans. Strategies have been implemented that focus on both addressing negative aspects of residential neighborhoods and enhancing positive features that can contribute to the continuous improvement of locals’ living conditions. This study applies the multiple-criteria decision analysis approach and a combination of cognitive mapping and the best-worst method (BWM) to identify the most relevant criteria and use these to rank residential neighborhoods according to their sustainability. To apply the selected techniques, two group meetings were held with a panel of decision makers. The results were validated by the panel members and the Funchal City Council councilor for urbanism, who concurred that the proposed ranking system facilitates the identification of the most sustainable residential neighborhoods. The contributions and limitations of the methodological approach are also discussed.

show abstract

Measuring urban digitalization using cognitive mapping and the best worst method (BWM)

Cited by 16 publications

References 40 publications

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

Coupling Coordination Measurement and Evaluation of Urban Digitalization and Green Development in China

Analysis of the fire risks and mitigation approaches in the apparel manufacturing industry: Implications toward operational safety and sustainability

Ranking Residential Neighborhoods Based on Their Sustainability: A Cm-BWM Approach

Contact Info

Product

Resources

About