Title of DTech study

1
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

2
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.

3
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)

4
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

5
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)

6
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)

7
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)

8
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).

9
Sample size of study (2)
Stratum or category of solid waste Sample size to be selected from stratum
Industrial 104
Commercial 753
Institutional 21
Construction and demolition 58
Municipal services 56
Processing or manufacturing 28
Agriculture 14
Total 1, 034
10
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

11
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)

12
Statistical methods of data analysis

• Pearsons chi-square tests of associations
  between pairs of categorical variables
• Binary logistic regression analysis
• Multilevel analysis
• The statistical package STATA was used for data
  entry and analysis

13
Top 10 significant associations with overall
efficiency
Variable of study associated with overall efficiency in waste management Observed chi-square value P-value
Perception Perception on the importance of efficient and proper waste disposal 916.49 0.0000
Frequency Frequency at which business premises are inspected 702.59 0.0000
Trashcan Availability of trash cans within the food outlet or business premises for customers 459.49 0.0000
Hygiene Personal hygiene of staff working for business 379.62 0.0000
Maintenance Degree of maintenance of trash bins and their environment in the organization 300.04 0.0000
Clean premises Degree to which premises are kept clean 250.03 0.0000
Manager Are you the manager or owner of this business? 166.06 0.0000
Commitment Degree to which owner is committed for cleanliness 139.47 0.0000
Condition Condition of building 127.19 0.0000
Customers Number of customers 115.02 0.0000
14
Results from binary logistic regression analysis

Factors that affect efficiency Odds Ratio P-Value (95 Conf. Interval)
Wrong perception on the benefits of proper waste disposal and management 10.92 0.000 (6.55, 18.19)
Non-availability of dust bins to customers 3.08 0.000 (1.92, 4.96)
Operation of businesses by non-owners 2.73 0.000 (1.70, 4.36)
Inadequate commitment for proper waste management 1.81 0.019 (1.10, 2.98)
15
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.

16
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.