Developing an e-Waste Disposal Strategy Using Digital Divide Statistics:
8-S Geographical Framework For Classifying Locations
Ramasamy Ramachandran,
iyavoo@gmail.com
The National ICT Association of Malaysia (PIKOM), Petaling Jaya, Malaysia
ABSTRACT
In combating the fast growing e-wastes menace, the paper proposes developing an e-waste disposal
management strategy using the digital divide data at the lowest administrative level or mukim in
Malaysia. Though the new age information communications technology (ICT) helps to reduce pollution
and destruction of forests through promoting reduced travels and paperless environment, it also comes
with problem of e-wastes, arising from disposals of all kinds of electronic equipment. The e-wastes are
highly toxic and, when irresponsibly disposed they can be detrimental to environment, climate and
ecology, as well as to our health. For developing the proposed strategy, the Population and Housing
Census 2000 and Household Basic Amenities Survey (HBAS) 2004 sampling records were augmented
at the mukim level for obtaining timely statistics on ICT penetration rates for all the 927 mukim in the
country. In turn, the paper classified all the mukim under eight categories of development under a
proposed 8-S Framework using inter-disciplinary statistical approaches such as ranking procedure,
square-rooting techniques for minimizing data variations, Goal-Post Method for converting rates into
indexes and Dalenius-Hodges Method of Stratification. The final presentation of data under the 8-S
Framework provided a small area or bottom-up approach for tackling the e-waste disposal problems.
1.
INTRODUCTION
This paper expounds the use of digital divide statistics at the lowest administrative level (or “mukim”
level in Malaysia) for developing an e-waste disposal strategy. Viewing from green initiatives, no
doubt, that the new age information communications technology (ICT) helps to reduce pollution and
destruction of forests through promoting reduced travels and paperless environment. Nonetheless, the
ICT advancements and its proliferation at an unprecedented rate in all spheres of life becomes a major
concern with regard to e-wastes disposals such as accumulators, mercury switches, glass from cathoderay tubes and other activated glass or polychlorinated phenyl capacitors, or contaminated with
cadmium, mercury, lead, nickel, chromium, copper, lithium, silver, manganese or polychlorinated by
phenyls (Ramachandra and Varghese, 2004; UNEP, 2009). Indeed, they are highly toxic. When these
elements are disposed randomly or irresponsibly they can affect the quality of air, water and
soil, causing detrimental effects to the environment, ecology and climate, as well as huge threat to our
health (UNEP,2009). Therefore, it is imperative to manage the disposals of all kinds of electronic
equipment such as TVs, video players, PCs, monitors, printers, batteries, scanners and mobile phones
in a systematic and an organized manner. Since the bulk of e-wastes occur at grassroots levels, the
paper proposes developing an e-wastes disposal strategy at small area level that can be measured,
monitored and evaluated over time and space (Singh and Mantel, 1991).
1
2.
POLICY STRATEGY
Acknowledging the diversity and economically at various stages development the exercise proposed to classify
all the 927 mukim under the 8-S Framework category (Ramachandran, 2010), as shown in the Box 1 below. For
formulating effective policy and programme strategies such geography or administrative based categorization is
crucial at least for three purposes. First, it will provide a reflection on the distribution of digital divide
phenomenon at mukim level in the country (Ramachandran, 2008). Second, it can help to prioritize
areas for the implementation of e-wastes disposal programmes especially when resources are scarce.
Third, it can provide a bottom-up basis for estimating the various types of e-wastes toxics by type of
ICT items.
Index Score Range
More than 80 to 100
More than 70 to 80
More than 60 to 70
More than 50 to 60
More than 40 to 50
More than 30 to 40
More than 10 to 30
10 and below
3.
8-S Framework
Skaters
Striders
Sprinters
Sliders
Strollers
Shufflers
Starters
Sleepers
Box I: 8-S Framework
Description
In a strong position for taking advantage of information age opportunities
Moving very fast and gaining momentum for info-age activities
Moving fast but lacking consistency in the momentum
Moving steadily but lacking momentum due to lack of motivation
Moving ahead with ICT up-take but not very consistent in growth
Embracing ICT in a slow phase due to challenges like affordability
Recognized the importance of ICT phenomena and have begun to embrace
Hardly started in embracing new age ICT technology
METHODOLOGY
The 8-S Framework preparation requires five stages of statistical procedures and inter-disciplinary approach.
First, estimation of ICT penetration rates at mukim level using small area estimation (SAE) procedure (Singh
and Mantel, 1991; Rao and Yu, 1992). Second, ranking of all mukim from most developed to least developed
in the up-take of various ICT items. Third, minimizing the variation in the data using square root techniques
mooted under the Dalenius- Hodges Method of Stratification (Cohran, 1977). Fourth, conversion of ICT
penetration rates into indexes using the Goal Post Method (Minges, 2003). Finally, classifying all the mukim
under the proposed 8-S Framework using the Dalenius- Hodges Method of Stratification concept (Dalenius &
Hodges 1959).
3.1
Data Strategy and Small Area Estimation (SAE) Procedure
The exercise considered the Population and Housing Census 2000 and Household Basic Amenities
Survey (HBSA) 2004 records. Though the census records were available for all the 927 “mukim”, it
lacked timeliness for reflecting the fast technological up-takes at household level. But, the HBSA
sample records met the timeliness criterion but lacked the adequacy of geographical coverage at mukim
level due to limited sample size. In addressing both timeliness and coverage concerns, a segmented
approach was used to augment the census and the sample records (Ramachandran, 2008; Rao and Yu,
1992), as follows:Case I : Sample counts, n ≥ 400. Those Mukim with adequate sample counts, that is, n ≥ 400, the ICT
penetration rates (𝒓𝒊,𝒋 ) is given by,
𝒙
𝒓𝒊,𝒋 = 𝒙𝒊,𝒋 %, where,
𝒋
“x” refers to number of households, subscript “i” refers to ICT items and “j” refers to mukim .
Case II: Sample counts 30 ≤ n <400. For mukim falling under this category the ICT Penetration Rate
(𝒓𝒊,𝒋 ) is computed through following procedures:2
𝒓𝒊,𝒋 = max{(𝒙𝒊,𝒋 ); (𝒙𝒊,𝒋,𝒂𝒅𝒋𝒖𝒔𝒕𝒆𝒅 )} ; where
𝒙𝒊,𝒋,𝒂𝒅𝒋𝒖𝒔𝒕𝒆𝒅=𝑹𝒊,𝒋 ∗ (𝟏 + 𝜶𝒊,𝒌 ); 𝜶𝒊,𝒌 = {
(𝒙𝒊,𝒌 −𝑹𝒊,𝒌 )
𝑵𝒊,𝒌
𝑹𝒊,𝒌
𝑵𝒌
} % ; 𝑹𝒊,𝒌 =
*% ; 𝒙𝒊,𝒌 =
𝒏𝒊,𝒌
𝒏𝒌
*%
Where (𝒙𝒊,𝒋,𝒂𝒅𝒋𝒖𝒔𝒕𝒆𝒅 ) refers to the sample based ICT rate after adjusting with the rate of growth at strata
level; 𝑹𝒊,𝒋 - refers to the census based ICT penetration rates; and 𝜶𝒊,𝒌 refers to rate of growth of ICT
penetration rates between the Census year 2000 and HBAS 2004 for the kth strata that contains the
mukim under investigation; and n refers to sample counts and N refers to Census counts. The
stratification refers to the census based 8-S Framework.
Case III: Sample counts n < 30. For mukim falling under this category the (𝒓𝒊,𝒋 ) is obtained by taking
geometric mean (GM) of the rates obtained at mukim and strata level as
𝒓𝒊,𝒋 = √{𝒙𝒊𝒋 ∗ 𝒙𝒊,𝒌 }.
Case IV: Sample count n =0; A total of 145 mukims (that is, 16%) that were not selected under the
HBAS 2004 sampling scheme, especially those located in rural areas, it is assumed that the mukim and
strata have the same ICT penetration rate, that is,
𝒓𝒊,𝒋 = {𝒙𝒊,𝒌 }
Case V: Census count Nj =0 and mukim not selected in the sample survey. In such cases the ICT
penetration rates were obtained, as shown below, by “borrowing strengths” from the neighbourhood
mukim that were perceived to have similar socio-economic and technology conditions:
𝒔
𝒓𝒊,𝒋 = √
𝒙𝒊,𝒋 , where j=1,2,3, .. s
3.2
Ranking Procedure
The ICT penetration rates were sorted in an descending order, as such the best performing mukim,
denoted by max{rij} occupies the first position and the least performing nation, denoted min{rij},
occupies the last position.
3.3
Minimizing Variations Using Dalenius-Hodges Square Root-Techniques
For minimizing the variations in the data set the square-root technique, as shown below was applied,
before demarcating the threshold limits (Dalenius & Hodges 1959; Cohran, 1977).
𝒚
𝒁(𝒚) = ∫𝒚 𝟐[√𝒇(𝒚)]𝒅𝒚
𝟏
3.4 Indexing Procedure: Converting Variables into Indexes
By dividing the performance of each mukim against the best performing mukim, the ranked variables are
converted into an index ( Ii,j ) series, using the Goal Post Method (Minges, 2003) as :
Ii,j=√ri,j /max{√ri,j } *%,
An index score of 100 is assigned to the best performance nation and zero for the lowest performance.
3.5
Grouping of Mukims under 8-S Framework
The proposed 8-S Framework provided the requisite demarcation points, which in the Dalenius &
Hodges (1959) Method of Stratification is determined arbitrarily.
4
RESULTS
The results shown in Table 1 below indicates the distribution of various ICT penetration rates and the number of
mukim involved, as shown in parenthesis, reflecting the digital divide distribution in the country. For instance,
as shown in the Table 1 in the year 2004 a total of 124 mukim was experiencing a penetration rate of 80.1
mobile phones per 100 population. A scientific report revealed that a mobile phone can contain more than 40
elements and in one single unit the precious metal content estimated as . 250 mg silver (Ag), 24 mg of gold (Au),
9 mg of palladium (Pd) and 9 mg. of copper (Cu) as well as 3.5 g of cobalt (Co) in the Li-ion battery (UNEP,
3
2009). Similar, numbers also can be estimated for other electrical and electronic equipment. Based on the
information furnished in Table 1, the quantum of such e-waste contents can be computed all the way from local
to macro level and also can specifically pin point which locality warrants the highest attention and priority in
terms of e-waste disposal programmes and resource allocation.
Table 1: Distribution of ICT Penetration Rates Per 100 Population and Number of Mukim Involved, 2004
Computer
Internet
Fixed line
Mobile Phone
Radio
TV
Video/VCD/DVD
Skaters
Striders
Sprinters
Slider
Strollers
Shufflers
Starters
Sleepers
59.3
(4)
52.8
(2)
74.1
(64)
80.1
(124)
86.9
92.6
(607)
82.7
(179)
50.4
(3)
43.0
(3)
64.7
(175)
69.2
(144)
76.1
75.1
(219)
71.0
(154)
47.3
(1)
37.3
(6)
56.3
(222)
60.2
(147)
66.4
66.0
(77)
63.0
(125)
36.9
(16)
31.8
(10)
48.7
(209)
51.0
(133)
55.3
54.1
(10)
52.9
(133)
31.2
(37)
25.4
(30)
39.8
(100)
41.4
(151)
46.7
45.6
(7)
42.4
(192)
23.4
(58)
20.1
(30)
30.9
(58)
32.9
(118)
31.8
37.9
(4)
34.5
(81)
11.3
(548)
9.0
(278)
18.1
(78)
18.6
(39)
16.7
15.3
(1)
22.3
(57)
3.6
(260)
2.2
(595)
1.6
(21)
4.4
(17)
7.2
5.7
(2)
4.3
(6)
5. CONCLUSION
The final presentation of data in the form of index under the 8-S Framework provided a reflection on
digital divide distribution at the lowest administrative level, identification and prioritization of
localities for the implementation of e-wastes disposal programmes and a basis for estimating the
various types of e-wastes toxics using scientifically determined rates and ratios. The US Environmental
Protection Agency reported that in 2007 only 18% of televisions, 18 % of computer products and 10% cell
phones disposed subject to recycling, indicating the enormity of the growing e-wastes disposal especially those
discarded irresponsibly. Such numbers in developing countries are likely to be higher, warranting an urgent need
for a proper e-waste disposal strategy.
References
1.
2.
3.
4.
5.
6.
7.
8.
9.
Cochran, W. G. (1977). Sampling Techniques. Third Edition. Dalenius-Hodges Methodology, p. 127-130.
Dalenius, T., & Hodges, J. L. (1959). Minimum variance stratification. Journal of the American Statistical Association,
54, 88-101.
Minges, M. (2003). Digital Access Index 2002. Geneva, Switzerland: International Telecommunication Union.
Rao, J.N.K. and Yu, M. (1992). Small area estimation by combining time series and cross-sectional data. In
proceedings of the Survey Research Methods Section 1-19. Amer. Statist. Assoc., Alexandria, VA.
Singh, A.C. and Mantel, H.J. (1991). State space composite estimation for small areas. In Symposium 91 – Spatial
Issues in Statistics – Proceedings 17-25. Statistics Canada, Ottawa.
Ramachandran, R. (2010). Benchmarking Malaysia in the Global Information Society: Regressing or Progressing?
Published in the Journal of Centrum Cathedera . The Business and Economic Research Journal. Pontificia Universidad
Catholica Del Peru.
Ramachandran, R. (2008). A Policy Tool on Developing a National Strategic Framework on Bridging Digital Divide:
A Malaysian Public Policy Perspective. Published in the Proceedings of the 2nd International Conference on Theory and
Practice of Electronic Governance, Cairo, 1-4, December 2008.
Ramachandra T.V. and Saira Varghese K (2004). Environmentally Sound Options for e-Wastes Management. Published
by Journal of Human Settlements, March 2004. Energy and Wetlands Group, Center for Ecological Sciences, Indian
Institute of Science, Bangalore, India.
UNEP (United Nations Environment Programme). 2009. Recycling: From e-waste to resources. Paris and Bonn:
United Nations Environment Programme and United Nations University.
RRC, April, 2010
4