Introduction
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Many studies focus on assessing environmental data for sustainability improvements. For instance, incorporating aspects such as worker well-being, community impacts, and social equity into the analysis could result in holistic sustainability strategies. Consequently, there is a gap in integrating social and environmental data to provide a full view of sustainability.
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Social impacts are always difficult to quantify and analyze. Hence, there is little understanding of how to incorporate life-cycle evaluation of product impacts during manufacturing enterprise processes using BDA models and approaches.
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Monitoring and decision-making in real-time using BDA could be extremely beneficial and enrich the literature by creating algorithms and frameworks that enable manufacturing enterprises to make quick sustainability-related decisions based on real-time data.
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Integrating supply chain management in the product life cycle of manufacturing enterprises is challenging. As a result, there’s a gap in exploring how BDA can be used to enhance sustainability across the entire supply chain, from raw material sourcing to end-of-life product management.
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BDA applications for sustainability assessments are still in their infancy. Thus, it is unclear how to successfully achieve sustainability needs. Literally, achieving the 2030 Sustainable Development Goals (SDG) may be impossible for stakeholders in the key fields of sustainable development without a solid understanding of the above challenges.
Literature review
Sustainable manufacturing enterprises
Big Data Analytics and sustainable development
Life cycle sustainability assessment and impacts monetization
Theoretical backgrounds
Environmental priority strategy
Definitions
Impact assessment in EPS
Big Data Analytics models and techniques
BD integration and management (DIM)
BD advanced analytics and visualization
BDA-based LCSA approach
Environmental and social impacts monetization
Database name | \(\#\) of manufacturing activity sectors | \(\#\) of manufacturing enterprises | \(\#\) of countries | \(\#\) of observations |
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Corporate environmental impacts | 20 | 839 | 41 | 5100 |
Adapted Apache Spark ecosystem
Spark core
Upper-level libraries
Experiments results and analyses
Descriptive and diagnostic analytics
Predictive and prescriptive analytics
SDGs (Y) | Multiple linear regression | Artificial neural network | ||||
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MSE | \({R^{2}}\) | Epochs | MSE | \({R^{2}}\) | Epochs | |
SDG1 | 1.12067e+07 | 0.985611 | 10 | 0.21056e+09 | 0.994 | 100 |
SDG2 | 3.01021e+06 | 0.99459 | 10 | 2.33940e+06 | 0.990334 | 100 |
SDG3 | 3.68953e+08 | 0.99698 | 10 | 2.22087e+08 | 0.998938 | 100 |
SDG4 | 671584 | 0.997372 | 10 | 0.33027e+05 | 0.999321 | 100 |
SDG5 | 1.94250e+06 | 0.980005 | 10 | 1.78067e+07 | 0.9902 | 100 |
SDG6 | 5.8932e+06 | 0.990303 | 10 | 3.98393e+06 | 0.9987 | 100 |
SDG7 | 2.209393e+07 | 0.704473 | 10 | 1.03928e+08 | 0.765021 | 100 |
SDG8 | 7.890493e+05 | 0.92933 | 10 | 5.435305e+04 | 0.90392 | 100 |
SDG9 | 2.89754e+07 | 0.988229 | 10 | 1.45303e+06 | 0.993241 | 100 |
SDG10 | 1.20943e+09 | 0.97775 | 10 | 1.02343e+08 | 0.985712 | 100 |
SDG11 | 3029.33 | 0.969286 | 10 | 1.29403e+02 | 0.97789 | 100 |
SDG12 | 10431.3 | 0.547476 | 10 | 934.55 | 0.609831 | 100 |
SDG13 | 95848 | 0.950487 | 10 | 10394.5 | 0.975493 | 100 |
SDG14 | 394.67 | 0.982938 | 10 | 123.43 | 0.995283 | 100 |
SDG15 | 39283.32 | 0.97327 | 10 | 29182.9 | 0.985283 | 100 |
SDG16 | 428399 | 0.962839 | 10 | 293839 | 0.9709 | 100 |
SDG17 | 2.249403e+06 | 0.959283 | 10 | 1.19393e+05 | 0.9638294 | 100 |
Conclusion
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Sustainable manufacturing enterprises are enhanced and are better positioned to create high returns at a level of risk that is well-balanced when sustainability concepts are incorporated into the investing process.
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Measuring enterprises’ impacts on the sustainable environment and society is a hard task.
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Standardizing sustainability metrics is hard because it’s difficult to decide which impacts on the environment and society are most severe so to be prioritized in order to reduce the overall environmental and societal impacts.
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Monetization of impacts is one of the best solutions to measure the manifold impacts on the environment and society.
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Practical implications from manufacturing enterprise owners and managers are necessary to allow efficient applicability of the approach in real-life scenarios.