Big data: New tend to sustainable consumption research

Z Wang, M Xue, Y Wang, M Song, S Li… - Journal of Cleaner …, 2019 - Elsevier
Z Wang, M Xue, Y Wang, M Song, S Li, RA Daziano, B Wang, G Ma, K Chen, X Li, B Zhang
Journal of Cleaner Production, 2019Elsevier
Growing consumption has brought a series of environmental problems. Sustainable
consumption patterns which could meet human needs, improve the quality of lives, and
reduce pollutants in the product life cycle emerge and develop. With the development and
application of information and network technology, the scale and variety of data are
increasing rapidly; advances in data analytics have made the economy, and consumption,
quantifiable and visible. At present, many scholars rely on a big-data background and carry …
Abstract
Growing consumption has brought a series of environmental problems. Sustainable consumption patterns which could meet human needs, improve the quality of lives, and reduce pollutants in the product life cycle emerge and develop. With the development and application of information and network technology, the scale and variety of data are increasing rapidly; advances in data analytics have made the economy, and consumption, quantifiable and visible. At present, many scholars rely on a big-data background and carry out research on sustainable consumption. Therefore, we called for sustainable and consumption papers for special volume of Journal of Cleaner Production (JCLPRO). We received submissions from all over the world and eventually accepted 45. This Special Issue forming a study on sustainable energy consumption, low-carbon transportation, waste recovery and recycling, climate change cost assessment, application and policy modelling for big data and sustainable consumption to promote sustainable development in the fields of energy consumption, low-carbon transportation, waste recovery, and so on. The authors have analysed the problems of pollution and carbon emission in different regions and product production cycles, according to the background of specific regions and enterprises, through data mining, measurement models, and an evaluation index system. Some suggestions are provided for urban construction and enterprise development according to the results.
Elsevier
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