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An Urban System Optimization Model Based on CO 2 Sequestration Index: A Big Data Analytics Approach

Author

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  • Vasile Dogaru

    () (Department of Nicholas Georgescu-Roegen Interdisciplinary Research Platform, West University of Timisoara, Timisoara 300223, Romania)

  • Claudiu Brandas

    () (Department of Business Information Systems, Faculty of Economics and Business Administration, West University of Timisoara, Timisoara 300223, Romania)

  • Marian Cristescu

    () (Department of Finance and Accounting, Faculty of Economic Sciences, “Lucian Blaga” University of Sibiu, Sibiu 550324, Romania)

Abstract

Urban development in recent decades has had a significant impact on climate change. Cities have implemented traffic monitoring systems to sustain the new building code in meeting the target for environmental indicators. Timisoara is the second city in Romania and manages over 60% of the development and pollution of Timis County. The analysis of large volumes of data provided by local sensors and databases requires big data analytics. In this research, for the first time, we simultaneously developed two parallel scenario-based decision-making support models to assess a CO 2 sequestration index. The model is based on a tree inventory for traffic area and car flow using Roegenian processes with borders. The first scenario (dt1) analyzes the real O 2 -pollution car flows for process streets as receivers of pollution. The second scenario (dt2) analyzes O 2 -pollution flows for the same streets from the perspective of streets that garage the cars. We modeled the parallel integration of actual O 2 production and pollution flows for 160 main streets that account for over 50% of the urban mileage of Timisoara city. The carbon sequestration indexes of the streets are in the range of 0.0000043–0.437 (dt1) and 0.0000092–11.78 (dt2). The results can be used to support local decision making regarding the environment CO 2 -O 2 balances by optimizing the local fiscal policies. The research could be extended to secondary streets and separately for the pollution of heating-cooling devices for residential building areas.

Suggested Citation

  • Vasile Dogaru & Claudiu Brandas & Marian Cristescu, 2019. "An Urban System Optimization Model Based on CO 2 Sequestration Index: A Big Data Analytics Approach," Sustainability, MDPI, Open Access Journal, vol. 11(18), pages 1-14, September.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:4821-:d:263900
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    References listed on IDEAS

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    1. Chengcheng Xu & Shuyue Wu, 2019. "Evaluating the Effects of Household Characteristics on Household Daily Traffic Emissions Based on Household Travel Survey Data," Sustainability, MDPI, Open Access Journal, vol. 11(6), pages 1-12, March.
    2. André Luiz Lopes Toledo & Emílio Lèbre La Rovere, 2018. "Urban Mobility and Greenhouse Gas Emissions: Status, Public Policies, and Scenarios in a Developing Economy City, Natal, Brazil," Sustainability, MDPI, Open Access Journal, vol. 10(11), pages 1-24, November.
    3. Lorena BATAGAN, 2011. "Smart Cities and Sustainability Models," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 15(3), pages 80-87.
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    Cited by:

    1. Valentin Grecu & Radu-Ilie-Gabriel Ciobotea & Adrian Florea, 2020. "Software Application for Organizational Sustainability Performance Assessment," Sustainability, MDPI, Open Access Journal, vol. 12(11), pages 1-1, May.
    2. Mohammad Al-Saidi, 2020. "From Economic to Extrinsic Values of Sustainable Energy: Prestige, Neo-Rentierism, and Geopolitics of the Energy Transition in the Arabian Peninsula," Energies, MDPI, Open Access Journal, vol. 13(21), pages 1-16, October.
    3. Olga Pilipczuk, 2020. "Sustainable Smart Cities and Energy Management: The Labor Market Perspective," Energies, MDPI, Open Access Journal, vol. 13(22), pages 1-24, November.

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    More about this item

    Keywords

    CO 2 sequestration; urban sustainable development; big data analytics; data mining; decision support systems;
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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