Title of DTech study An attempt at quantifying factors that affect efficiency in the management of solid waste in the City of Pretoria 12 November 2011

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Objectives of study

   To assess the current level of efficiency in the collection and disposal of solid waste produced by the 7 categories of waste in the CBD of Pretoria. To identify factors that are responsible for the inefficient management and disposal of solid waste produced by the 7 categories of waste in the CBD of Pretoria. Construct a model that could be used for improving efficiency in the management of solid waste in the CBD of Pretoria. 2

Research questions

 What are the key socio-economic, demographic, sanitary and health-related factors that affect efficiency in the collection and disposal of solid waste in Pretoria CBD (Central Business District)?

 How useful is the waste disposal and management model used by the City of Kuala-Lumpur, Malaysia for the City of Pretoria? ( Value and relevance of the Kuala-Lumpur Model for Pretoria ) 3

Key regulations on waste management

     National Policy on Waste Management by the Department of Environmental Affairs and Tourism (DEAT, 2007) Municipal by-laws of the City of Tshwane Metropolitan Municipality (CTMM, 2008) Regulation by the National Department of Health (DOH, 2008) The South African Constitution (1996) Policy on Primary Health Care by the World Health Organization (WHO, 2007 4

Literature review (1)

The following researchers have published the

list of indicators

that affect efficiency in the management of solid waste:        The South African Department of Environmental Affairs and Tourism (2010) Statistics South Africa (2010) The South African Department of Health (2009) World Health Organization (2010) City of Tshwane Metropolitan Municipality (2008) Sierra-Vargas et al. (2009) Federico et al. (2009) 5

Literature review (2)

The following authors have published

practical models

that could be used for improving efficiency in the management of solid waste:        

Kuala Lumpur City Hall (2008)

World Health Organization (2010) Sierra-Vargas et al. (2009) Federico et al. (2009) Khan (2009) Environmental Protection Agency (2007) United Nations Development Programme (2007) Godfrey (2007) 6

Conceptual framework (1)

A strategic collaboration to be established among the following key stakeholders based on framework in Kuala Lumpur City :     Businesses and institutions that operate in the CBD of Pretoria and ratepayers DEAT, DOH, Stats SA, CTMM, WHO, The Press Academic and research institutions, the World Health Organization (WHO) Non-governmental organizations (NGOs) 7

Conceptual framework (2)

A successful model from the

City of Kuala Lumpur in Malaysia

to be followed.  Waste management in Kuala Lumpur is dependent on landfill facilities more than 70km away from Kuala Lumpur City.  A solid waste treatment plant is used for converting solid waste into energy or reusable products such as ethanol or Refuse Derived Fuels (

RDF

).

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Sample size of study (2)

Stratum or category of solid waste Sample size to be selected from stratum

Industrial Commercial Institutional Construction and demolition Municipal services Processing or manufacturing Agriculture

Total

104 753 21 58 56 28 14

1, 034

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Examples of variables of study

            

Category of solid waste (1, 2, 3, 4, 5, 6, 7) Geographical location of source of waste Type of waste generated Volume of waste generated Frequency of waste generation Method used for waste disposal Frequency of waste disposal Dustbins, toilets, hand-washing basins, etc. Method of waste disposal used during strike actions Degree of adherence to guidelines on waste management (1, 2, 3, 4, 5) Level of education (1, 2, 3, 4, 5) Ownership of premises (Owner, Employee) Duration of service at premises

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Methods of data collection

         Personal observation at site of study Personal or face-to-face interviews with owners or managers of businesses Personal or face-to-face interviews with employees A structured questionnaire Records from the City Council of Tshwane Records from Statistics South Africa Records from the Department of Environmental Affairs and Tourism (DEAT) Records from the Environmental Protection Agency (EPA) Records from the World Health Organization (WHO) 11

Statistical methods of data analysis

    Pearson’s chi-square tests of associations between pairs of categorical variables Binary logistic regression analysis Multilevel analysis The statistical package analysis

STATA

was used for data entry and 12

Top 10 significant associations with overall efficiency Variable of study associated with overall efficiency in waste management

Perception: Perception on the importance of efficient and proper waste disposal Frequency: Frequency at which business premises are inspected Trashcan: Availability of trash cans within the food outlet or business premises for customers Hygiene: Personal hygiene of staff working for business Maintenance: Degree of maintenance of trash bins and their environment in the organization Clean premises: Degree to which premises are kept clean Manager: Are you the manager or owner of this business?

Commitment: Degree to which owner is committed for cleanliness Condition: Condition of building Customers: Number of customers

Observed chi-square value

916.49

702.59

459.49

379.62

300.04

250.03

166.06

139.47

127.19

115.02

P-value

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

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Results from binary logistic regression analysis Factors that affect efficiency Odds Ratio Wrong perception on the benefits of proper waste disposal and management Non-availability of dust bins to customers 10.92

3.08

2.73

Operation of businesses by non-owners Inadequate commitment for proper waste management 1.81

P-Value 0.000

0.000

0.000

0.019

(95% Conf. Interval) (6.55, 18.19) (1.92, 4.96) (1.70, 4.36) (1.10, 2.98)

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Interpretation of odds ratios

 The odds ratio of the variable wrong perception is 10.91. This shows that a business in which the owner has the wrong perception on the benefits of proper waste management is 10.91 times as likely not to be efficient in waste management. 15

Key results from multilevel analysis

 There were significant differences among the 7 categories of waste.  23.05% of the total variation in efficiency is due to differences among the 7 categories of waste produced in the City of Pretoria.  Businesses within the same category of waste and geographical location were equally efficient in waste disposal and management. 16