TY - CHAP
T1 - Use of DEA for studying the link between environmental and manufacturing performance
AU - Ramanathan, Ram
PY - 2017/7/17
Y1 - 2017/7/17
N2 - In this era of big data and business analytics, huge data is available in public domain and it is important for researchers to analyse this data to be able to make business sense to help businesses grow and to help policy makers to obtain useful insights. In this chapter, we first outline various available Big Data in the public domain that can be used to investigate an important issue in environmental policy: the relationship between environmental expenditure and manufacturing efficiency. We then illustrate how a multi-criteria tool, namely the Data Envelopment Analysis, can be advantageously combined with other statistical models to help study the above relationship. DEA is used to obtain manufacturing efficiency scores of various sectors in the UK. DEA scores are then combined with further data on pollution abatement expenditure in these sectors. Using previous literature, we hypothesise that there is a positive relationship between environmental expenditure and manufacturing efficiency of sectors, and verify it using sector-level data from the UK manufacturing industry. Our study illustrates the use of MCDM tools in using publicly available Big Data for use in public policy analysis.
AB - In this era of big data and business analytics, huge data is available in public domain and it is important for researchers to analyse this data to be able to make business sense to help businesses grow and to help policy makers to obtain useful insights. In this chapter, we first outline various available Big Data in the public domain that can be used to investigate an important issue in environmental policy: the relationship between environmental expenditure and manufacturing efficiency. We then illustrate how a multi-criteria tool, namely the Data Envelopment Analysis, can be advantageously combined with other statistical models to help study the above relationship. DEA is used to obtain manufacturing efficiency scores of various sectors in the UK. DEA scores are then combined with further data on pollution abatement expenditure in these sectors. Using previous literature, we hypothesise that there is a positive relationship between environmental expenditure and manufacturing efficiency of sectors, and verify it using sector-level data from the UK manufacturing industry. Our study illustrates the use of MCDM tools in using publicly available Big Data for use in public policy analysis.
KW - Secondary data
KW - Data envelopment analysis
KW - Mathematical Sciences
KW - Regression analysis
KW - Computer Sciences and Mathematical Tools
KW - Efficiency
KW - Environmental performance
UR - https://www.routledge.com/Big-Data-Analytics-Using-Multiple-Criteria-Decision-Making-Models/Ramanathan-Mathirajan-Ravindran/p/book/9781498753555
U2 - 10.1201/9781315152653-13
DO - 10.1201/9781315152653-13
M3 - Chapter
SN - 9781498753555
T3 - The operations research series
SP - 303
EP - 313
BT - Big data analytics using multiple criteria decision making models
PB - CRC Press
CY - Florida, USA
ER -