Sustainable Business Model optimisation for the automobile sector of India

Aim: This is the foundational article of the study. It aims to establish an understanding of the UN’s Sustainable Development Goals (SDGs) and review the Sustainable Business Models (SBMs) adopted by the automobile industry for achieving them. There will be special emphasis on environmental and social aspects of sustainability.

Method: Conduct a literature review using 15-20 academic journal articles published since 2015.

Structure:

• Introduction:
• Introduction to the problem of ecological degradation
• About the UN SDGs
• Aim of this milestone
• Environmental SDGs
• Social SDGs
• Sustainable Business Models (SBMs) in the automobile sector
• Definition of SBMs and their relevance in achieving SDGs
• SBMs implemented by the automobile sector
• SDGs represented by these SBMs
• Environmental SDGs
• Social SDGs
• Conclusion
• References

Aim: To conduct a systematic review of existing studies which assess sustainability performance of the automobile sector, using the environmental and social SDGs and their corresponding indicators.

Methodology: This milestone will employ a systematic review methodology to analyze and synthesize research findings related to the SDGs relevant to the automotive industry. The process will include identifying relevant literature published since 2015, focusing on peer-reviewed journal articles, industry reports, and case studies using PRISMA analysis methodology.

Structure:

• Introduction to the SDGs targeted by the automobile sector for achieving environmental and social sustainability
• Aim of this milestone
• Research methodology
  • Studies selection process: Clearly articulate the criteria used for selecting studies such as keywords used, and sources or libraries used.
  • Inclusion and exclusion criteria: explain the criteria employed to include or exclude studies, like year of publication, language, format, publication credibility, etc.
  • PRISMA diagram: Present the PRISMA diagram to visually depict the systematic review process and highlight the number of studies at each stage.
• Discussion: This is the main section presenting the systematic review. It will have two tables:
  • Systematic review table 1- Categorization of SDGs addressed by the automotive sector: Prepare a table containing columns for author and year, study aim, methodology, variables used, key findings, and study limitations. Here is an example. All the 15 studies must be included in this table.
  • Systematic review table 2- Sustainability indicators relevant to SBMs
    • Environmental Indicators (e.g., carbon emissions, resource usage)
    • Social Indicators (e.g., labor practices, community engagement)
    • Prepare a table containing columns for author and year, study aim, methodology, variables used, key findings, and study limitations. Here is an example. All the 15 studies must be included in this table.

• Conclusion- Identification of the gaps in environmental and social aspects of reporting in India's automobile sector
• References

Aim: To implement Principal Component Analysis (PCA) to identify and assess key environmental sustainability indicators within the automobile sector, generating insights for improving sustainability practices.

Methodology: This milestone is structured into three distinct parts to facilitate a comprehensive analysis of sustainability indicators through Principal Component Analysis (PCA) as below:

1. Data Preparation – load data, review it and address missing values or other problems
2. Principal Component Analysis (PCA) - Normalize the dataset, Execute PCA using the sklearn.decomposition.PCA module, Analyze the variance explained by each principal component using a scree plot, and Generate loading plots to identify the contribution of each sustainability indicator to the principal components i.e. environmental sustainability
3. Insight Reporting - Synthesize findings from both EDA and PCA into a cohesive report and provide actionable recommendations for automobile organizations based on the insights gained from the analysis.

Structure of report

1. Introduction
   • Introduction to sustainability indicators in the automotive sector.
   • Importance of quantifying environmental sustainability indicators
   • Objectives of the analysis.
2. Data Preparation
   • Description of the dataset used, including sources and key variables.
   • Summary of data preparation steps (loading, cleaning, handling missing values).
3. Principal Component Analysis (PCA)
   • Explanation of normalization
   • Description of the PCA method used and the rationale for selecting the number of components.
   • Scree plot showing the variance explained by each principal component.
   • Interpretation of loading plots to identify the contribution of indicators to principal components (indicators contribution to the environmental sustainability of automotive companies)
4. Insights and Recommendations
   • Synthesis of findings from EDA and PCA.
   • Highlighting key sustainability indicators reported by the companies.
   • Actionable recommendations for automotive organizations based on analysis results.
5. Conclusion
6. References

Submission

Upon completion of the analysis and reporting process, you are required to submit the final Word document of the report and your analysis file i.e. Jypyter notebook (in the original format used i.e. ipynb).

Aim: To utilize Principal Component Analysis (PCA) to identify and assess key social sustainability indicators within the automobile sector, generating insights for improving sustainability practices.

Methodology: This milestone is structured into three distinct parts to facilitate a comprehensive analysis of sustainability indicators through Principal Component Analysis (PCA) as below:

1. Data Preparation – load data, review it and address missing values or other problems
2. Principal Component Analysis (PCA) - Normalize the dataset, Execute PCA using the sklearn.decomposition. PCA module, analyze the variance explained by each principal component using a scree plot, and Generate loading plots to identify the contribution of each sustainability indicator to the principal components i.e. environmental sustainability
3. Insight Reporting - Synthesize findings from both EDA and PCA into a cohesive report and provide actionable recommendations for automotive organizations based on the insights gained from the analysis.

Structure of report

1. Introduction
   • Introduction to sustainability indicators in the automotive sector.
   • Importance of quantifying social sustainability indicators
   • Objectives of the analysis.
2. Data Preparation
   • Description of the dataset used, including sources and key variables.
   • Summary of data preparation steps (loading, cleaning, handling missing values).
3. Principal Component Analysis (PCA)
   • Explanation of normalization
   • Description of the PCA method used and the rationale for selecting the number of components.
   • Scree plot showing the variance explained by each principal component.
   • Interpretation of loading plots to identify the contribution of indicators to principal components (indicators contribution to the social sustainability of automotive companies)
4. Insights and Recommendations
   • Synthesis of findings from EDA and PCA.
   • Highlighting key sustainability indicators reported by the companies.
   • Actionable recommendations for automotive organizations based on analysis results.
5. Conclusion
6. References

Submission

Upon completion of the analysis and reporting process, you are required to submit the final Word document of the report and your analysis file i.e. Jypyter notebook (in the original format used i.e. ipynb).