A study of solid waste generation from commercial
and institutional sectors and its potential for recovery
in Vietnam「ベトナムにおける事業系廃棄物の発生及びその資源化ポテンシャ
ルに関する研究」

PhD. Dissertation
August 2016

DO THI THU TRANG

Graduate School of Environmental and Life Science
OKAYAMA UNIVERSITYTABLE OF CONTENTSTABLE OF CONTENTS.......................................................................................................................................... i
LIST OF TABLE...................................................................................................................................................... v
LIST OF FIGURE .................................................................................................................................................. vi
LIST OF ABBREVIATIONS AND ACRONYMS .............................................................................................. vii
ABSTRACT ............................................................................................................................................................. 1
CHAPTER 1 INTRODUCTION ........................................................................................................................... 4
1.1

Research background ................................................................................................................................ 4

1.1.1

Overview of solid waste management ................................................................................................... 4

1.1.2

Overview of solid waste management in Hue city ................................................................................ 9

1.2

Objective of study ................................................................................................................................... 12

1.3

Scope of study ......................................................................................................................................... 13

References for chapter 1………………………………………………………………………………………………
CHAPTER 2 SOLID WASTE GENERATION FROM COMMERCIAL SECTOR .................................... 10
2.1

Solid waste generation from hotel sector ............................................................................................ 10

2.1.1

Introduction ....................................................................................................................................... 10

2.1.2

Methodology ...................................................................................................................................... 122.1.2.1

Sample selection ............................................................................................................................. 122.1.2.2

Outline of survey ........................................................................................................................... 14

2.1.2.3

Analytical procedure....................................................................................................................... 15

2.1.3

Results and discussion ....................................................................................................................... 17

2.1.3.1

Total waste amount by hotel class .................................................................................................. 17

2.1.3.2

Relevant factor influencing waste generation amount.................................................................... 18

2.1.3.3

Daily waste generation rate by category ........................................................................................ 19

2.1.3.4

Waste composition, potential of composting and recycling ............................................................ 20

2.1.3.5

Predictive model of waste generation from hotel sector in Hue ..................................................... 21

2.1.3.6

Estimation of total waste generation from hotel sector .................................................................. 22

2.1.3.7

Interval estimation and Uncertainty analysisfor hotel ................................................................... 25

Conclusions..................................................................................................................................................... 26
References....................................................................................................................................................... 27
2.2

Solid waste generation from restaurant and beverage sector ........................................................... 28

2.2.1

Introduction ....................................................................................................................................... 28

2.2.2

Methodology ....................................................................................................................................... 29

2.2.2.1

Sample selection ............................................................................................................................. 29

2.2.2.2

Definitions and terminology ........................................................................................................... 29

2.2.2.3

Outline of survey............................................................................................................................. 30

2.2.2.4

Analytical procedure....................................................................................................................... 31

2.2.3

Results and discussion ....................................................................................................................... 31

2.2.3.1

Total waste generation amount ....................................................................................................... 31

2.2.3.2

Total waste generation rate ............................................................................................................ 32ii

2.2.3.3

Waste separation status .................................................................................................................. 32

2.2.3.4

Detail waste generation rate by business category ........................................................................ 33

2.2.3.5

Factors influencing waste generation rate ..................................................................................... 34

2.2.3.6

Waste composition .......................................................................................................................... 35

2.2.3.7

Estimation of total generated waste................................................................................................ 35

2.2.3.8

Interval estimation .......................................................................................................................... 40

Conclusion ........................................................................................................................................................... 41
References ........................................................................................................................................................... 42
2.3

Solid waste generation from home-based business ............................................................................ 44

2.3.1

Introduction ....................................................................................................................................... 44

2.3.2

Methodology ....................................................................................................................................... 44

2.3.2.1

Sample selection ............................................................................................................................. 44

2.3.2.2

Outline of survey............................................................................................................................. 46

2.3.2.3

Analytical procedure....................................................................................................................... 47

2.3.3

Results and discussion ....................................................................................................................... 47

2.3.3.1

Waste generation rate ..................................................................................................................... 47

2.3.3.2

Waste composition and potential .................................................................................................... 51

2.3.3.3

Estimation of WGA from home-based business .............................................................................. 52

2.3.3.4

Interval estimation and Uncertainty analysis ................................................................................. 53

Conclusion .......................................................................................................................................................... 54
References........................................................................................................................................................... 55
2.4

Solid waste generation from traditional markets and supermarket................................................. 56

2.4.1

Introduction ....................................................................................................................................... 56

2.4.2

Methodology ....................................................................................................................................... 57

2.4.2.1

Sample selection ............................................................................................................................. 57

2.4.2.2

Outline of survey............................................................................................................................. 58

2.4.2.3

Analytical procedure....................................................................................................................... 59

2.4.3

Results and discussion ....................................................................................................................... 60

2.4.3.1

Waste generation rate of traditional markets ................................................................................. 60

2.4.3.2

Waste composition of market .......................................................................................................... 64

2.4.3.3

Estimation of total waste generation from traditional market in Hue ............................................ 66

2.4.3.4

Interval estimation of total waste generation from traditional market in Hue ............................... 68

2.4.4

Waste generation from supermarket ................................................................................................. 69

Conclusions......................................................................................................................................................... 69
References........................................................................................................................................................... 70
CHAPTER 3. SOLID WASTE GENERATION FROM INSTITUTIONAL SECTOR ................................. 72
3.1

Solid waste generation from educational facilities ............................................................................. 72

3.1.1

Introduction ....................................................................................................................................... 72

3.1.2

Methodology ....................................................................................................................................... 73

3.1.2.1

Sample selection ............................................................................................................................. 73

3.1.2.2

Outline of survey............................................................................................................................. 74

3.1.2.3

Analytical procedure....................................................................................................................... 75iii

3.1.3Results and discussion ....................................................................................................................... 76

3.1.3.1

Waste generation rates of educational facilities ............................................................................. 76

3.1.3.2

Mean difference in waste generation rates by food service provided ............................................. 78

3.1.3.3

Waste composition at educational facilities .................................................................................... 78

3.1.3.4

Estimation of total waste generation from the educational sector ................................................. 80

3.1.3.5

Interval estimation and Uncertainty analysis ................................................................................. 82

Conclusions......................................................................................................................................................... 83
References........................................................................................................................................................... 84
3.2

Solid waste generation from health-care facilities.............................................................................. 87

3.2.1

Introduction ....................................................................................................................................... 87

3.2.2

Results and discussion ....................................................................................................................... 88

3.2.2.1

Waste generation rates of health-care facilities.............................................................................. 88

CHAPTER 4 SOLID WASTE GENERATION FROM OTHER SECTOR .................................................... 93
4.1

Waste generation from household part at home-based business ............................................................. 93

4.1.1

Waste generation rate ......................................................................................................................... 93

4.1.2

Waste composition and potential ........................................................................................................ 93

4.1.3

Total estimation of waste generation amount from household part at HBB ....................................... 94

4.1.4

Interval estimation and Uncertainty analysis ..................................................................................... 94

4.2

Waste generation from household without business ............................................................................... 95

4.3

Waste generation from dormitory and street ........................................................................................... 95

CHAPTER 5 TOTAL ESTIMATION OF SOLID WASTE GENERATION AND ITS POTENTIAL FOR
RECOVERY .......................................................................................................................................................... 96
5.1

Total waste generation from commercial and institutional sectors ......................................................... 96

5.2

Total waste generation and discharge flow in Hue.................................................................................. 97

5.3

Interval estimation and uncertainty analysis ........................................................................................... 98

5.4

Recycling and composting potential ....................................................................................................... 99

5.1

Financial potential................................................................................................................................. 100

CHAPTER 6 CONCLUSION AND RECOMMENDATION .......................................................................... 102
6.1

Summary of key points ....................................................................................................................... 102

APPENDICES...................................................................................................................................................... 107

iv

LIST OF TABLE
Table 1- 1. Waste composition atSWM facilities in Hue city ......................................................................... 10
Table 1- 2. Outline of SWM facilities in Hue..................................................................................................11
Table 1- 3. Sources and types of solid wastes in Asia (World Bank, 1999). ..................................................... 6
Table 1- 4. Major components of commercial and institution sectors in this study…………………………..13
Table 2- 1 The total hotels in Hue ................................................................................................................... 12
Table 2- 2 Waste separation status at the target samples ................................................................................. 14
Table 2- 3. Classification category of waste from hotel .................................................................................. 16
Table 2- 4 Total waste generation amount of hotel by day (kg/day) ............................................................... 17
Table 2- 5 Relevant factor influencing hotel waste generation ...................................................................... 18
Table 2- 6 Daily waste generation rate by category ........................................................................................ 19
Table 2- 7 Physical composition of hotel (%) ................................................................................................. 20
Table 2- 8 Predictive model of waste generation............................................................................................. 21
Table 2- 9 Total number of room, check-in guest and event guest in Hue city in 2012 and 2020................... 23
Table 2- 10 The calculation base of estimation ............................................................................................... 23
Table 2- 11 Total estimation of waste generation from hotel sector in Hue city ............................................. 24
Table 2- 12 Calculation condition for interval estimation ............................................................................... 25
Table 2- 13 Information of target samples ....................................................................................................... 30
Table 2- 14 Total waste generation amount by each category by day ............................................................. 32
Table 2- 15 Total waste generation rate of each category ................................................................................ 32
Table 2- 16 Waste separation participation of target samples .......................................................................... 33
Table 2- 17 The basic stats waste generation rate of food and beverage service sector. ................................. 34
Table 2- 18. Physical composition, composting and recycling potential......................................................... 35
Table 2- 19 The recalculated waste generation rate by worker by each category ........................................... 36
Table 2- 20. The 95% confidential intervals of waste generation.................................................................... 40
Table 2- 21. The System of Economic Branches of Vietnam .......................................................................... 45
Table 2- 22. Sample selection of home-based business ................................................................................... 46
Table 2- 23. Waste generation rate at level 1 ................................................................................................... 48
Table 2- 24. Waste generation rate at level 2 ................................................................................................... 49
Table 2- 25. Waste generation rate at level 4 .................................................................................................. 50
Table 2- 26. Waste generation rate at level 5 ................................................................................................... 51
Table 2- 27. Waste composition and potential ................................................................................................. 51
Table 2- 28. Total estimation of WGA from home-based business ................................................................. 53
Table 2- 29. Outline of five target markets ...................................................................................................... 57
Table 2- 30. Definition of business category ................................................................................................... 58
Table 2- 31. Waste generation rate by stall (g/stall/day) .................................................................................. 61
Table 2- 32. Waste generation rate by vendor (g/vendor/day) ......................................................................... 61
Table 2- 33. Waste generation rate by floor area ............................................................................................. 61
Table 2- 34. Waste generation rate by market class (g/stall/day)..................................................................... 64
Table 2- 35. Recycling and composting potential of general waste (%) ......................................................... 65
Table 2- 36. Interval estimation of three markets and actual amount .............................................................. 66
Table 2- 37. Waste amounts by business category from 23 markets (kg/day) ................................................ 67
Table 3- 1. Total number of and samples from educational facilities in Hue by school category ................... 75
Table 3- 2. Waste generation rates of educational facilities............................................................................. 77
Table 3- 3. Waste generation rate of primary school by food service .............................................................. 78
Table 3- 4. Physical composition of GW at educational facilities (%) ............................................................ 79
Table 3- 5. Recycling and composting potential from general waste by school category (%) ........................ 80
Table 3- 6. Estimation of total waste and the breakdown from educational facilities ..................................... 82
Table 3- 7. Waste generation rate from health-care facilities in Hue ............................................................... 89
Table 3- 8. Amount of general healthcare waste from various levels of hospital establishments ................... 90
Table 4- 1. Waste generation rate from household part at home-based business............................................ 93
Table 4- 2. Total estimation of waste generation amount from household part at HBB .................................. 94
Table 5- 1. Total waste generation from commercial and institutional sectors................................................ 97
Table 5- 2. Interval estimation of total WGA in Hue in 2012 ........................................................................ 98
Table 5- 3. Financial potential in general waste ............................................................................................ 101v

LIST OF FIGURE
Figure 1- 1. Local of Vietnam ............................................................................................................................................. 9
Figure 1- 2. Average waste collection amount (tons/day) collected by HEPCO (2011) ................................................... 11
Figure 2- 1 Contribution to total WGA ............................................................................................................................ 24
Figure 2- 2 Uncertainty analysisof total waste generationfrom hotel sector ..................................................................... 26
Figure 2- 3 .Waste stream from food and beverage service sectors in Hue city ............................................................... 39
Figure 2- 4. Uncertainty analysisof total waste generation amount from food and beverage service sector ................... 41
Figure 2- 5. Uncertainty analysisfrom home-based business sector ................................................................................. 54
Figure 2- 6. Uncertainty analysisof total waste generation. .............................................................................................. 69
Figure 3- 1. Uncertainty analysiseducational facilities ..................................................................................................... 82
Figure 4- 1 . Waste composition of household_HB .......................................................................................................... 94
Figure 4- 2. Composting & Recycling Potential of general waste from household_HB .................................................. 94
Figure 4- 3. Uncertainty analysis from home-based business sector in Hue……………………….95
Figure 5- 1. Waste flow of total MSW in Hue city ........................................................................................................... 98
Figure 5- 2. Uncertainty analysisof total waste generation from MSW in Hue ................................................................ 99
Figure 5- 4. Contribution of component in recycling potential....................................................................................... 100
Figure 5- 5. Contribution of component in composting potential ................................................................................... 100

vi

LIST OF ABBREVIATIONS AND ACRONYMSHEPCO: Hue Urban Environment and Public Works State Limited Company
HSW: Household Solid Waste
MSW: Municipal Solid Waste
SWM: Solid Waste Management
GW: General Waste
SR: Separated Recyclable
SFR: Separated Food Residue
DWA: Direct Waste Analysis
HEPCO: Hue Urban Environment and Public Works State Limited Company
3R: Reduce, Reuse, Recycle
BC: British Columbia
Ave : Average
SD: Standard Deviation
WG: Waste Generation
ANOVA: Analysis of Variance
CI: Confidence Interval
VPM: Vietnam’s Prime Minister
EPA: Environmental Protection Agency

vii

ACKNOWLEDGEMENTFirst of all, I’d like to give my sincere thanks to my honorific supervisor, Associate Professor
Yasuhiro Matsui, who accepted me as his doctoral student, he offered me so much advice, patiently
supervised me, and always guided me in the right direction. I’ve learned a lot from him, without his
help I could not have finished my thesis successfully.
Special thanks are also given to Dr. Nguyen Phuc Thanh for his valuable advice and friendly help.
His encouragement and help made me feel confident to fulfill my thesis and to overcome every
difficulty I encountered. During my thesis work, he always gave many useful comments andsuggested possible improvements. It is not sufficient to express my gratitude with only a few words.
I would like to thank MEXT scholarship for giving me the opportunity to participate in this program
as well as to have a chance to study in Japan. Without their support and financial help, it would nothave been possible for me to pursue and to complete this thesis. My sincere thanks also go to all the
Teachers from Okayama University for enthusiastic teaching, interesting and useful subjects.
I’d like to convey my heartfelt thanks to the Teachers and students from Department ofEnvironmental Science, Hue College of Science, Hue University for collaborating and helping mewith my survey in Hue city. I also wish to thank Dr. Pham Khac Lieu for his encouragement andmeaningful suggestions. Moreover, I am also grateful for many friends: Ms. Yen Anh, Mr. Truong,
Mr. Cong, Ms. Hoa, Ms. Linh, Mr.Quang, who have supported enthusiastically during my survey in
Hue city.
I would like to express my appreciation to all those companies and persons who have offered me
their time when I collected necessary data for my case study in their companies, including Hue
Urban Environmentand Public Works Co. Ltd., People's Committee of Hue province, People's
Committee of Hue city, Hue University. I am special grateful for Mr. Quang and Mr. Thang for theirkind help during my survey.
I am deeply grateful to my lab-mates for sharing with me everything in daily life, from big
difficulties to small felicity. I would like to thank Vietnamese Student’s Association in Okayama
University for their warmly support during my time in Japan.
I am very grateful to my parents. Their understanding and their love encouraged me to work hard
and to continue pursuing study abroad. They always let me know that they are proud of me, which
motivates me to work harder and do my best.

1

ABSTRACTThe amount of municipal solid waste (MSW) generated in Vietnam has been increasing inrecent years. The rapid increase in MSW has posed significant challenges for Vietnamese solid
waste management authorities. In addition, in 2015, the Vietnamese government issued a national
strategy to manage waste and discarded material (Decree no. 38/2015/NĐ-CP), which indicated thatdaily-life solid waste must be classified and stored according to the following three categories:
biodegradable organic, reusable and recycled, and “other”. Determining the recyclable and organic
amounts is indispensable for Vietnam’s handling of waste in the years to come.
Currently, MSW management in Vietnam has been empirically planned without the use of
reliable data, but it will include such data in the near future. Shortcomings in MSW data include
inconsistencies in definitions and collecting data; lack of unification in reporting data among themunicipalities; and differences in the amount of waste generated, which makes predicting trends
difficult. It is important to understand the amount of waste generated, the waste composition, andthe waste stream as the first step in developing an effective MSW strategy that includes 3R
promotion (reduce, reuse, recycle). MSW is generated and discharged from various sources:
households; commercial sources like hotels, restaurants, and markets; and institutional sources likeeducational facilities, medical facilities, and offices. In Vietnam, most studies have investigated thewaste generation from resident area and very few studies considered on some categories ofcommercial and institutional sector.
To provide the scientific information that can contribute to the promotion of the 3Rs in a
Vietnamese city, this dissertation aimed:(1) To clarify the waste generation rate of various sectors by different business indicator(kg/unit/day);(2) To provide the physical and detailed composition of waste in order to identify therecycling and composting potential;(3) To explore the correlation between waste generation and relevant factors;(4) To estimate the total waste generation in Hue, and contribution of commercial andinstitutional sector to total amount;(5) To estimate the potential for recovery from general waste;
To estimate the interval estimation of the total waste in Hue, clarify the reliability ofcollected data and improve future tasks through uncertainty analysis.
The author conducted surveys for commercial and institutional sectors. Generally, thesample selection in this study was mainly based on the total list according to “The System of
Economic Branches of Vietnam”. The surveys conducted for 814 representative target samples infive markets, 95 beverage and restaurant sector, 35 educational facilities, 43 hotels, 245 home-based

2

businesses, 43 offices, 41 health-care facilities, 1 dormitory, 2 supermarkets in 5 months. This study
was comprised of 3 surveys: waste generation survey, waste composition survey, and questionnaire
survey.
Firstly, waste generation survey was conducted to acquire data on “General waste,”“Recyclables,” “Food residues,” for 7 consecutive days. During the waste generation survey, awaste composition survey was conducted for the selected samples. The waste was classified into 10
physical categories and 77 sub-categories. A questionnaire survey was also conducted by a face-toface interview at the target samples to obtain data on relevant factors influencing waste generationsuch as business scale indicators, and the current status of recycling activities.
The waste generation ratesfor abovementioned commercial and institutional sectors(kg/unit/day) were calculated, and the interrelationships between the waste generation amount andthe business scale indicators were analyzed. The influencing factors for waste generation were also
explored, and the author developed the predictive models on waste generation.
Waste composition was analyzed and discussed in weight (g/day) and percentage (%). The
potential for composting (food waste and garden waste), recycling (plastic and paper), and reducing
waste (food residues) and financial potential in commercial and institutional sectors were identified.
The total waste generated in Hue was estimated to be 252.2 tons/day, of which 205 tons(80%) was general waste, 34.7 tons (13.5%) was recyclable, and 39.8 tons (16%) was food residues.
General waste still contained 19.3% that could potentially be recycled and 102.4 % that could
potentially be composted in the total waste amount. The total disposal amount sent to the landfill
could be reduced from 205 tons (80%) to 27.1 tons (10.6%).
Regarding the commercial and institutional sectors, the total waste generated fromcommercial and institutional was 93.1 and 13.1 tons/day, respectively, of which 32.9 tons (31 %)wascollected by pig farmers for feeding livestock and 9.8 tons (10%) was sold to the recycling
market and 63.3 (59%) tons was sent to the landfill site. The compostingpotential accounted for
23 % and the recycling potential accounted for 17% of total waste generationfrom commercial andinstitutional in Hue. By a Monte Carlo simulation, the interval estimation for total waste generationin Hue was simulated and estimated.
From the results of financial potential, the amount of recyclable recovering from generalwaste can earn 211 million VND/day, this also can create the job for 1,813 people with the basic
salary is 3.5 million VND/month.
This is the first step for developing predictive models and total estimation of waste flow.
Through these studies, it would be possible to develop predictive models on commercial andinstitutional waste generation, and they will support the waste authorities for prediction, planning,and establishing integrated solid waste management.

3

CHAPTER 1 INTRODUCTION
1.1

Research background

1.1.1

Overview of solid waste management(1) Current situation of solid waste management
Waste is a continually growing problem at global, regional and local levels and one of themost intractable problems for local authorities in urban centers. With continuous economic
development and improving living standards, the demands for goods and services are increasing
rapidly, resulting in a commensurate increase in per capita waste generation (Narayana, 2008). In
recent years, MSW management has attracted attention from bilateral and multilateral development
agencies, due to the urgency of urban environmental problems and the increasing concern forcapacity building at the level of municipal management. With its broad organizational implications
and close links to other sectors, MSWM constitutes an important entry point for integrated urban
management support (UNDP et al., 1996).
In every urban centre, huge quantity of solid waste is generated during various activities.
These wastes are to be stored, collected, transported, processed, and disposed of in an environment
friendly manner so as to keep the city neat and clean. In spite of incurred huge expenditure, theservices provided to the solid waste management are not fulfilling the requirement, causing publichealth hazards and nuisance. Solid waste management is a large and vital public system spread overthe entire city, area and the system is responsible for maintaining the public surroundings. Hence,the system has to be planned rationally for a long and short term (Gawaikar and Deshpande,2006).
As the first step to develop an effective SWM strategy for a given region, it is important toknow the amount of waste generation, the waste composition, and the waste stream. The per capita
waste generation rate is essential to predict future waste generation and to evaluate the waste
generation trends (Bandara et al., 2007). Data on waste composition is required for the planning ofcollection, transportation, and treatment of MSW. Good data is the foundation of effective
integrated waste management systems (Forbes et al., 2001).
However, it must be noted that, due to a lot of reasons, Vietnam in general and Hue city,
have lacked a reliable database on SWM. Annually, Vietnam has a report on the state of theenvironment focusing on that year’s prominent issue. Of all reports, only in 2004, SWM was paid
much attention and became the main topic. The report, however, presented the information on
physical composition of MSW around Vietnam and had not clarified the in-depth data of waste
discharge flow and physical composition at various generation sources like households or businessactivities yet. As regards Hue city, Hue Urban Environment and Public Works State Limited
Company (HEPCO) is the office in charge of the City’s waste collection, transportation, andtreatment. Up to now,HEPCO only surveyed on MSW composition in Hue city in 2004 (Yen, 2006).

4

Using unreliable data can lead to poor policy decision makings and the establishment of
inappropriate waste management infrastructure (European Environment Agency, 2003). Therefore,
urban authorities in developing nations, like Vietnam, should study on SWM, especially in waste
generation and composition. These studies are not only the most suitable tool to support information
on current situation and existing issues of waste management systems, but also the promotion ofeffective solutions for the current problems as well as the future planning.

(2) Current of methodologies on solid waste characterization
In Asia, MSW is commonly regarded as the waste that is generated from human settlements
and small businesses, commercial and municipal activities. In other words, everything is collected
and treated by municipalities (UNEP/ GRID-Arendal, 2006). It is categorized into two basic types:
residential and non- residential wastes. For a particular area, the residential waste is almost uniform
while the non- residential sector shows a variation, particularly the waste derived from institutions
which have higher generation on weekdays and almost none during weekends and holidays. In
contrast, the commercial sector has increased generation during weekends, holidays and festivals,
and peak tourist seasons. However, in the case of industrial wastes, fewer changes in generation are
noticed, unless the production capacity of the industry is increased and a particular category
dominates the daily manufacture.
Table 1-3 shows the types of generators and corresponding waste categories. MSW in
general does not include agricultural waste unless it is from small-scale units.
Regarding to the waste composition and quantification of MSW, Ireland (EPA, 1996) had
formulated a methodology for household and commercial waste as follows: Waste analysis was carried out with three months interval. Sampling was avoided on
Bank holidays, Christmas, Easter and Public holidays. To facilitate the sampling selection, it
was suggested that social classes should be combined into three categories. For larger area, it
was recommended to split the area into several small areas.
 The commercial sector was divided into four broad categories, namely, retail trade,wholesale trade, non-distribution and education. Total numbers of employees from the selected
commercial areas were recorded so that the average weight of waste/ employee could bedetermined for each activity. In this case also, coning and quartering technique was adopted toreduce sample weight to 100 kg - 200 kg. After weighing and sorting the sample, picking out
the large items such as glass, paper and plastic was done. The remaining material was passed
through 20 mm mesh sieve and unclassified material was categorized as combustible andincombustible.

5Table 1- 1.Sources and types of solid wastes in Asia (World Bank, 1999).
SourceType of generator

Type of Waste
Food wastes, paper, cardboard, plastics, textiles, leather,

Residential

Single and multifamily

yard wastes, wood, glass, metals, ashes, special wastes

units(e.g., bulky items, consumer electronics, white goods,
batteries, oil, tyres), and household hazardous wastes

Industrial

Manufacturing units,

Housekeeping, packaging and food wastes, ashes,

power and chemical

construction and demolition debris, hazardous wastes,

plantsspecial wastes

Stores, hotels,
Commercial

restaurants, markets andmalls

Paper, cardboard, plastics, wood, food wastes, glass,metals, special wastes, hazardous wastes

Schools, hospitals,
Institutional

prisons,

Same as commercial

government offices
Construction

New construction sites,and demolition road repair, renovation
sites

Wood, steel, concrete, dirt, stones etc.

demolition sites
Street cleaning, parks,

Municipal

beaches, and otherservicesrecreational areas,
treatment plants

Street sweepings, landscape and tree trimmings, generalwastes from parks, beaches, other recreational areas,sludgeHeavy / light extraction Process wastes, scrap materials, offProcessing

andprocess units,

specificationproducts, slag, mineral tailings, unused raw

refineries

materials

Crops, farms, orchards,
Agriculture

vineyards, dairies,
feedlots

Spoiled food wastes, crop and animal wastes,
hazardous wastes (e.g., pesticides)

Yu and Maclaven (1995) compared two waste streams quantification and characterization
methodologies which contained direct waste analysis for determining the waste quantity and waste
composition using a social science approach (questionnaire survey) for dealing with the sameproblem. The advantages and disadvantages of the method were described as follows:6 Direct Waste Analysis (DWA) involved the direct examination of waste set out forcollection at point-of-generation or waste delivered to a waste processing facility or to a waste
disposal site.
 In contrast to DWA, the questionnaire survey, methodology was normally restricted
to collection of data at the point-of-generation, rather than at waste processing or waste disposal
facilities. The methodology involved questionnaire survey for waste generators by means offace-to-face interview, mail or telephone surveys. Respondents were asked information basedon the waste stream records and the visual inspection of waste containers about the quantity of
waste discharged for disposal, the composition of waste, and the seasonal variation of waste
generation.
Although various methods of waste quantification were discussed, the most common was
the method of direct waste analysis. Many past researches on solid waste generation andcomposition applied this method. Although the direct waste analysis has some limitations such as
high labor cost and errors in sample selection, it is still an appropriate method to determine MSW
composition and determine the effect of spatial, temporal and socio-economical variations on waste
composition. Besides, it provides a significant amount of information if combined a survey on
factors affecting waste generation. Actually, there have been a lot of researches correlating socioeconomic factors and solid waste generation (Bandara et al., 2007). In this literature review, somestudies presented to see how the direct waste analysis was used. The direct waste analysis involves
the actual quantification of waste through, sampling and weighing trucks as they enter a landfill orcollecting samples from generators, and conducting waste audits and studies where samples are
sorted by waste type and weighed. This method was selected because it can potentially provide datathat is detailed and accurate.
BritishColumbia(1991)

addressedthecharacterizationofresidential

andcommercial/institutional waste. The British Columbia (BC) manual recommended the use of adisposal site based approach and listed a number of advantages of this approach including:1)

It provides a better appreciation of the waste entering the landfill and ensuresthat a part of the waste stream is not overlooked;
2)

It provides a better estimate of large and bulky items; and,3)

It is more cost effective.

The details of BC methodology were as follows: Sample Selection: the BC manual stated that vehicles from which samples wereobtained should be chosen randomly with no bias towards morning or afternoon or, large andsmall loads. It recommended that the loads should come from the entire week, including
weekends if possible. A systematic sampling methodology was recommended to select vehicles.

7Each sub-waste stream (e.g. residential vs. IC&I, single family dwellings vs. multifamilydwellings, etc) studied was required to be considered separately. The BC manual described theuse of the grid method for sampling waste from selected truck loads in order to ensure that the
samples are obtained from random locations in the waste pile.
 Number of Samples: The BC manual provided a look-up chart for estimating thenumber of samples required based on the desired precision levels with 90% confidence and thecoefficient of variation of the waste stream. The BC manual recommended a sample size of
136 kg after the removal and weighing of large and oversize items.
 Material Categories: The BC manual listed 15 main categories and 60 sub-categories.
The manual recognized that depending on the goal of the study, fewer categories may berequired.
Ontario Ministry of Environment (1991) recommended that the characterization ofcommercial waste should be based on sampling at the point of generation and included thefollowing elements:
 Major groups of business sector were identified for study. In some cases generalgroups were desegregated to investigate the sub-groups within a group. The selection of thebusinesses included discussions with the operators and possibly site visits prior to sampling.
 Samples were collected a day prior to a scheduled pick-up. Some businesses werevisited 3 or more times to collect a week’s worth of waste. Two to three sites were sampled perday. Larger bins (6 – 8 cu. yd.) were only half sorted. The number of employees at the site wasalso recorded.
 Number and Size of Samples: the sampling involved the collection of one week ofaccumulated wastes from the selected businesses. The sample sizes varied from 2.4 to 5782 kg.
The report recommended that number of samples and size of these samples should bedetermined taking into account the study’s resource constraints (time and money).
 Material Categories: fourteen main categories and nearly 50 sub-categories ofmaterials were defined in the study. The study recommended that, where an item composed ofseveral materials is found; it should be classified on the basis of the predominant material byweight.

8

1.1.2

Overview of solid waste management in Hue city

(1) Location and social-economic condition

Figure 1-1. Location of Hue city, Vietnam

Hue city - the latest imperial capital of Vietnam under Nguyen dynasty is located in thecentral region of Vietnam, by the side of Perfume River, just a few miles inland from the East Sea.
It is about 700 kilometers south of the national capital of Hanoi and about 1100 kilometers north of
Ho Chi Minh City. The city has gone through 7 centuries of establishment and development. In
1802, Hue became Vietnam's capital when Emperor Gia Long, the first King of the Nguyenemperors, chose the location for the imperial city. The city remained to be the nation's capital until
1945. In the Vietnam War, Hue's central position placed it very near the border between North
Vietnam and South Vietnam. Thanks to the long precious history, Hue has integrated material andspiritual values, which become its own unique special Culture. Hue is one of the main cultural,religious and educational centers of Vietnam. Architecture in Hue is the combination of royalarchitect, folk, religious ones, traditional and modern sides. Hue is also an important center of
Buddhism. In Hue and the surroundings, there still exist tens of pagodas which were constructed
more than 300 years ago, and a hundred of temples and pagodas built in the early century. Thanks to
the historical values, Hue now is becoming an ideal destination not only for those who hope toenjoy lovely landscapes but also for those who seek cultural exchange opportunities of rich andlifelong history of education.

Hue is comprised of 27 wards with a total area of 71.7 km2 and a population of 342,556 asof 2011 [21]. Hue is well known for its historical monuments, which were deemed World Cultural
Heritage sites on December 11, 1993, by the United Nations Educational, Scientific, and Cultural
Organization (UNESCO). As a tourism center of the central Vietnam, Hue city considers tourism as
a key economic sector. In 2008, Hue city got GDP growth rate of 13.7% with a GDP of $940 per
capita. As a tourism center of the central Vietnam, Hue city considers tourism as a key economic
sector. The tourism revenue in 2008 was 801.5 million VND, increased 35.5% in comparison withthat in 2007 (Hue city Portal, 2010). The location of Hue city in Vietnam and Thua Thien Hue
province was illustrated in.
(2) Climate
There are two distinct seasons in Hue city. The dry season, which comes with the hot southwest wind, lasts for four months, from April through to August.
The rainy season with high and unevenly distributed rainfall is from September to March.
The annual rainfall ranges from 2,000–7,500 mm, with an average of 3,000–3,300 mm. About 80%of the total rainfall falls during this rainyseason,thewettestmonthsbeing

Table 1- 2. Waste composition atSWM facilities in
Hue city

November and December. Storms, floods,

Unit:%

soil erosion and landslides are threats to

Tam Sinh
Thuy
Nghia
Phuonglandfi compostingll siteplant

the natural environment during thisseason

(Thua

Thien

Hue

Statistical

Yearbook, 1999).
On the average, there are about
180 sunny days a year with an averagetemperature of about 280C during thehottest periods and 190C during thecoldest months. Compared with otherregions of Vietnam, Hue city in particularand the Central Coast in general, haveinclement climatic conditions (Hue City
Portal, 2010).

No

Typeof waste

1
2
3
4
5
6
7
8
9
10

Kitchen waste
Paper
TextileWood
Plastic
Leather and Rubber
Metal
GlassesCeramic
Stone and sand

11
12
13
14

Briquette coalDangerous
DiaperOthers
Total

0.59
12.47
0.28
0.4
0.39
0.79
1.7

78.03
2.12
2.74
1.09
12.45
0.56
0.31
0.33
0.51
0.71

1.46
-

0.01
1.13
0.01

100

100

77.11
1.92
2.89

(3) Current condition of MSWM in Hue city
Collection and transportation
MSW in Hue is managed by Thua Thien-Hue province. Hue Environment and Public Works

10

Company (HEPCO) is the only one state-owned company which engages in MSW collection andtransportation in Hue city.
There are two collection methods applied in Hue.
 Door-to-door collection by Handcart: residents discharge waste on pavements, or in
front of their houses. Then, collection workers use handcarts to collect on foot door-to-door indesigned areas. When the handcarts are full, they are transported to designated transfer stations
not far off where waste trucks transfer the waste and transport to the treatment or the disposalsites.
 Dustbin collection by truck: Dustbins are located in fixed points. Residents bringwaste to discharge into those containers. Compactor trucks come to load waste from thecontainers directly and transport to treatment or disposal sites. Dustbin system has been applied
since 1999 as a trial in three model areas. Since then, it has shown the advantages in sanitation
improvement and been expanded to the whole area of Northern part of Huong river.
Waste generated from other sources such as small business units, commerce, institutions iscollected by contracts with HEPCO. As regards waste from rivers, HEPCO has a special team withboats and tools to pick up waste.
Hazardous medical waste is under thecontrol of hospital (HEPCO, 2011).
c) Treatment and disposal
There are two facilities in Hue,
one composting plant and one landfill
site, located 15km far from the center of
Hue city.

Figure 1- 2. Average waste collection amount(tons/day) collected by HEPCO (2011)

Table 1- 3. Outline of SWM facilities in Hue
No

Facility
Thuy Phuong

1composting
plant

2

Thuy Phuong
landfillManagement
AgencyArea

4.2 ha- Composting and recycling- Receiving: Around 200 tons/day- Compost product: 14 tons/day- Operation: 5 days/week (Closed on Saturday and
Sunday)10 ha- Sanitary landfill- Receiving: around 200 tons/day- Operation: Receiving the residues from composting
plant on weekdays, receiving all waste on weekends

Tam Sinh
Nghia, JSC.,

HEPCO(4) Remaining issues

RemarkAs other urban areas in Vietnam and other developing nations, the present SWM system in
Hue city is facing some difficulties. Firstly, as regards the waste collection and transportation
service, there are many remaining issues. The waste collection rate in rural areas is still low.
Secondly, the city has a waste treatment plant producing compost and other recyclable products
from waste, but the city does not still have any separation-at-source activities. The experience fromother countries show that, if the waste is properly separated at source, it will become useful items
contributing to resource reservation as well as reducing pressure on waste treatment or disposal.
Lastly, the citizen’s perception toward SWM systems is also worth to mention. Although Hue city
has daily waste collection services and a system of public dustbins along the street, there are still
people who discharge waste by improper ways.
To plan an effective SWM system, it is necessary to collect data on waste generation andcharacter. Waste characterization determines the type and proportion of materials discarded in a
waste stream.Because the quantity and quality of waste is always changing, it is essential to conduct
studies on waste generation and character continuously in order to make a systematic databaseserving for management and research task. Secondly, past researches have also shown thatobtaining waste generation and composition data is essential for planning an integrated waste
management system, since the source of waste will determine the collection method and strategy.
Besides, once any particular waste item is identified what it is made of, it is easier to determine its
suitability for material recycling (Forbes et al., 2001).
Lastly, concerning Hue city, 3R activities is highly suitable. Because Hue city has a small
area and population, quite homogeneous waste stream, and an integrated waste treatment plant
including a compost plant with plastic recycling factory and a landfill; the city has an advantage inplanning and carrying out 3R activities
1.2

Objective of study

Design and operation of appropriate solid waste management systems are necessary forensuring good sanitation and clean environment. The source specific solid waste quantification andcharacterization will be helpful in predicting the waste quantity from various waste generating
sources in a city and this can be used as a basis for the planning of the system. This will also enablein saving of time, manpower, and financial inputs required to be spent for estimating the wastequantity for the entire city. Such a developed methodology can be very easily adopted by themunicipal agency and would help them in managing the system in a befitting manner. Therefore,
this study focused to achieve some objectives as follows:
1) To clarify the waste generation rate of various sectors by different business indicator(kg/unit/day);2) To provide the physical and detailed composition of waste in order to identify the recycling

12and composting potential;
3) To explore the correlation between waste generation and relevant factors;
4) To estimate the total waste generation in Hue, and contribution of commercial andinstitutional sector to total amount;5) To estimate the potential for recovery from general waste;6) To draw of waste flow in Hue city
1.3

Scope of study

The author surveyed the target samples for each component of commercial and institutional sectors
composed of three surveys; a waste generation survey by actual measurement, a waste composition
survey, and a questionnaire survey. All surveys were carried out simultaneously in dry season, from
April 16th to September 25th. The components of research was presented in table 1-4
Table 1-4 Survey period of each category
Component
Institutional waste

Commercial waste

Category Educational facilities
 Healthcare facilities Office/Government office Hotel Beverage and Food services Market & Supermarket
 Home-based business

Based on the boundary of the study, the figure 1-3 presented the total schematic framework ofentire research in this study.

13

Target subject

Components

Waste generators

 General waste
 Separated recyclable
 Separated food residue

Commercial sector

Institutional sector

Other Hotels
 Beverage and food services
 Traditional markets
 Supermarkets
 Home-based business Educational facilities
 Health-care facilities Offices Dormitory Householdwith/withoutbusiness Street waste

Sample selection
Data base TTHue Statistical office TTHue Department of Culture, Sports and Tourism
 TTHue Department of heath TTHue Department of Industry and Trade TTHue Department of education
 Environmental company in Hue (HEPCO)

 Hotel: Based on No.bed by 6 categories
 Beverage and food services: based on No.worker by 8 categories
 Traditional market: based on No.stall by 17 categories
 Supermarkets: random sampling Home-based business: based on 58 categories
 Educational facilities based on No.student by 5 categories
 Health-care: based on No.bed and facilities Office: based on facilities by 11 categories

Data collection methods
Waste measurement survey

Waste composition survey

Questionnaire survey

Daily measurement in 7
consecutive days

10 physical categories and 77 subcategories

Information of relevant
factors on waste generation

Analytical procedure
Waste characteristic
 g/capita/day
 Percentage in composition (%)
 Potential of composting andrecycling (%)

Mean difference among categories
 ANOVA
Proper indicator for estimation
 Coefficients of variations (CV) Availability of data

Estimation and validation
 Estimation model 95% CI by Monte Carlo simulation
 Uncertainty analysis
 Validation

Output of study
Total waste generation amount and
95% Confidential Interval MSW in Hue
 SW from C&I sector
 Separated recyclable
 Separated food residue
 General waste

Potential for recovery in GW
 Recycling and composting
potential
 Financial potential
 GHGs emission mitigation
 Proposed plan for recovery
recycling items in GW

Proposal for integrated framework
on waste characterization
 Sample selection and datacollection
 Modelling and interval estimation
 Potential on recycling, composting
and financial benefit

Figure 1_3 Schematic framework of entire research8References
Bandara, N. J. G. J., Hettiaratchi, J. P. A., Wirasinghe, S. C., Pilapjiya, S., 2007.Relation of waste
generation and composition to socio-economic factors: a case study. Environ Monit Assess 135, 31– 39.
British Columbia, 1991. Procedural Manual for Municipal Solid Waste Composition Analysis.
Municipal Waste Branch, Gartner Lee Ltd, BC Environment. Municipal Waste Branch.
Byer, P.H., Hoang, C.P., Nguyen, T.T.T., Chopra, S., Maclaren, V., and Haight, M., Household, hotel andmarket waste audits for composting in Vietnam and Laos, Waste Management Research, 24, 465 –
472, 2006.
Environmental Protection Agency (EPA), 1996. Municipal Waste Characterization. Wexford, Ireland.
(http://www.epa.ie/whatsnew/wastechar.pdf)
European Environment Agency (EPA), 2003. Europe’s environment: the third assessment, Chapter 7:
Waste Generation and Management. ISBN: 92-9167-574-1, Denmark.
Forbes, R M., Peter, R W., Marian, F., Peter, H., 2001. Integrated solid waste management: a Life Cycle
Inventory, 2nd edition. Blackwell Science, Oxford, United Kingdom
Gawaikar, V and Deshpande, V. P., 2006. Source Specific Quantification and Characterization of
Municipal Solid Waste - a Review. Journal-Institution of Engineers India Part En Environmental
Engineering Division 86, MAR, 33-38.
HEPCO, 2011. Summary report on solid Waste management of Hue city. Hue Urban Environment andPublic Works State Company (HEPCO)(in Vietnamese).
Hue City Portal, 2010. Natural conditions. http://www.huecity.gov.vn/Index.aspx
JICA and Kokusai Kogyu Co. Ltd., 2003. The Study on Solid Waste Management in the Municipality ofPhnom Penh. ICA, Phnom Penh, Cambodia.
JICA, 2010. The study on urban environmental management in Vietnam. Volume 06: Study Report on
Solid
Waste
Managementin
Vietnam.
Available
onlineat:
http://www.nea.gov.vn/VN/tintuc/tinnoibo/Documents/PrR2_Vol_06_SWM%20withAnnex.pdf(accessed August 2011)
Narayana, T., 2008. Municipal Solid Waste Management in India: From Waste Disposal to Recovery of
Resources. Waste Management Vol. 29, No.3, pp. 1163 – 1166.
Ontario Ministry of Environment, 1991. Procedures for the Assessment of Solid Waste Residential andCommercial, Volume III of the Ontario Waste Composition Study. Ontario Ministry of theEnvironment. (http://archive.org/details/proceduresforass03onta)

9CHAPTER 2 SOLID WASTE GENERATION FROM COMMERCIAL SECTOR

2.1

Solid waste generation from hotel sector

2.1.1 Introduction
One of the challenges faced by waste management authorities is determining the amount ofwaste generation and characteristics in order to establish waste management systems. Everyday, a large
quantity of solid waste is generated by various sources such as households, restaurants, markets, hoteland other business. Viet Nam consists of long coastlines, forests, and mountainous regions withnumerous magnificent landscapes. As well, Viet Nam has a long history and diversified culture withvariety of ancient architecture constructions, and wonderful legends. These factors have created a great
potential for the tourism industry in Vietnam. In 2015, Viet Nam would welcome 7-7.5 million
international tourist arrivals and 36-37 million domestic tourists with total revenue from tourism would
reach US$10-11 billion, contributing 5.5-6% of GDP. In upcoming 2020, Viet Nam expected to receive
10-10.5 million international arrivals and 47-48 million domestic tourists; total revenue from tourism
would reach US$18-19 billion, contributing 6.5-7% of the country's GDP. To meet the growth ofinternational and domestic tourists, a lot of accommodation establishments have built in recent years. In
the “Strategy on Viet Nam’s tourism development until 2020, vision to 2030”, the prime minister ofVietnam mentioned total accommodation suites would be 580,000 and 35-40% of which are of three tofive-star standards (Decision 2473, 2011). The increasing number of accommodation establishment
leads to the expansion in so many service sectors as well as their management operations, especiallysolid waste management.
Tourism is one of the activities that depend heavily on the environment (Ishmael.,2005). The
hotel industry is one of the major contributors of organic/wet waste in landfills, which is the main causeof Green House Gas emission (Nripendra et al., 2014). Commercial sector, which hotels represents alarge part, accounts for 35% to 45% of the municipal waste stream (Franklin Associates,1999). Recently,
Otoma et al., 2012 found that the hotel sector contributed 9% of total municipal solid waste in Danang
city, Vietnam.

The literature revealed that the proper management of waste can lead to higher

profitability for hotel and save environmental pollution (Nripendra et al., 2014). To develop an effective
strategy for waste management, it is important to know the amount of waste generation, the waste
composition, and the waste stream (Bandara et al., 2007).
There were several studies on hotel sector in the past literature. Two approaches are generally
10

adopted for estimating waste quantity and composition. Either questionnaires are sent to the producersof the waste, or a direct waste analysis of the waste stream is carried out at the point of waste production
or at the waste treatment facility (Yu and Maclaren 1995). By the questionnaire method, in 2000, Trunget al conducted a survey in 37 hotels in nine major tourist provinces. The results showed the waste
generation rate by guest at different hotel classes in different areas of Vietnam. However, theinformation on waste amount by questionnaire may not be reliable because the data much depends on
the people who answered the question. By direct waste analysis, Byer et al, 2004 conducted a survey inHalong, Vietnam on three hotels including 2-star, 3-star and 4-star hotel during one week. The study
showed the generation rate by room, guest and composition on 9 physical categories with percentage oforganic waste, inorganic waste and recyclable. This study, however, is limited by small sample size.
Regarding estimation of total waste from hotel, Otoma et al, 2012 showed the estimated waste amountfrom hotel in Danang by number of room. Nevertheless, this study only showed the waste generation
rate by hotel without consideration of hotel class that was proved significantly different in study of Byer
et al, 2004. In hotels, the staff normally attempt to collect recyclables for sale and to give left over foodwaste to farmers for animal feeding (Byer et al., 2006). The recyclable amount and leftover food, thus,
should be controlled to reflect the actual amount of waste generation from hotel. To date, there are no
studies on solid waste generation on hotel sector in Vietnam considered the factors affecting waste
generation, the amount of recyclable and amount of food. Furthermore, according to Vietnam’s national
strategy on management of waste and discarded material in 2015 (Decree no.38/2015/NĐ-CP), thedaily-life solid waste must be classified and stored as the following groups: biodegradable organicgroup,reusable and recycled group, the other group.Therefore, determining the recyclable and organic
amount is extremely important for Vietnam in years to come.
Predictive model on solid waste management is an essential tool for the waste planning andmanagement. Model contributing to the improved estimation of present and future waste quantities andcharacteristics are aimed at enabling the best possible waste management planning decisions within thegiven constraints (Beigl et al., 2008). While many studies focused on modelling of household waste
generation (Benitez et al., 2008; Thanh et al., 2010; Lindh, 2003; Lebersorger et al., 2003) or modellingon municipal solid waste in general (Chang and Dyson., 2005; Daskalopoulos et al., 1998), there were
few studies on commercial sector. In 2000, Salhofer et al developed a model of businesses on 14 sectors
for recyclabes, sector-specific wastes and residual waste using the number of employees. The results
showed the total waste amount by 3 above-types on 14 sectors and revealed the differences between
11large business and small/medium businesses. This study, however, did not focus detail on each sector,
for example, they put the tourism and gastronomy together in one category without consideration of thedifferences in sector or detail business size. In 2012, Kato et al developed models on food residues
recycling by swine breeders in Danang, Vietnam. From the model, the study had estimated the total foodresidue collection by swine breeder in Danang, but had not paid much attention of collection source.
Hue city is a tourism city, the authors decided to choose Hue city as a study area on determining
the waste generation from hotel sector. In 2014, the government approved the plan of Hue to 2030 andoriented to 2050. In which, Hue city aimed to become a city that is friendly with environment and inharmony with nature(Decree No.649, 2014). Integrating the informal sector to formal sector in waste

management is an important option to consider when attempting to create a sustainable city indeveloping countries (Baud et al,. 2001). Accurate data, concerning estimates of present and future
production and composition of different types of waste, is essential for long-term efficient andeconomical waste management planning (Pault, 2005). As the first step to develop an effective SWM
strategy in hotel, it is important to know the waste stream of hotel sector to predict future waste
generation and to evaluate the waste generation trends. This study aims to clarify the parameters which
help to understand the current situation of WG and to assess the future waste amount at hotel sector. The
objectives of this study was to estimate the potential of recycling, composting of waste from hotel sector,
to develop the predictive model on waste generation to estimate the total waste flows on hotel sector in
Hue. Furthermore, the interval estimation of total waste amount and potential amount also estimated.
2.1.2 Methodology
Table 2- 1Thetotal hotels in Hue

2.1.2.1 Sample selection
Hue city, the city of Thua Thien
Hue province situated in the center ofVietnam was selected as the study area.
Hue city comprises 27 wards with342,556 people and a population densityof 4,779 persons per km2. Hue is wellknown for its historic monuments, whichhaveearned

it

a

place

Category

Hotel

Room

Bed

Guest house
1-star hotel
2-star hotel
3-star hotel
4-star hotel
5-star hotel
Uncategorized hotel
Total number

242
46
26
10
9
4
65
402

2049
874
943
708
1231
648
1309
7762

3214
1643
1832
1319
2235
990
2427
13660

Sample
size
8
10
7
8
7
3
43

in

UNESCO's World Heritage Sites. As a tourism center of the central Vietnam, Hue city considers tourism
as a key economic sector. The number of tourist arrivals in Hue has continued to rise in recent years. In
122014, Hue city received 1.84 million international and domestic tourists that were a 5.6% increase from2013 (Hue city, 2015). Regarding solid waste management, the amount of collected waste in Hue city isreported as approximately 210 tons/day. The general collection rate in the whole city was about 89%,
and 90–95% in urban areas (HEPCO, 2012). Hue city has 402 hotels with different scales that areclassified into 6 classes in considering ‘the Decree TCVN 7799:2009 on the standards of tourist guest
house’ [3] and ‘the Decree TCVN 4391:2009 on Hotel classification’ [4]. Table 2-1 showed the total
hotel in Hue by class and the sample size of targets in this study. Among them, 4 hotels belong to the 5star hotel, 9 hotels belong to 4-star hotel, 10 hotels belong to 3-star hotel, 26 hotels belong to 2-star hotel,
46 hotels belong to 1-star hotel and the total guest house was 242. Moreover, there were 65
uncategorized hotels that might lack of some criteria to be certificated by authority. For simplicity, inthis study, “Hotels” refers to both “Hotels” and “Guest house”.
To acquire an accurate representation of hotels by size, the random sample of hotels was
stratified according to 6 abovementioned hotel classes, ranging from guest house to 5-star hotel. Basedon the total number of bed on each hotel class, the total list of hotel by class was prepared and sorted bynumber of bed. Then, the 43 target samples were selected systematically from the list. The number ofsamples by hotel class and separation status of hotel was shown in the Table 2-2.
Three types of waste generated were measured based on the original customs of target.
‘Recyclables’, ‘Food residues’ and ‘General waste’ defined as follows: Recyclables: items kept for recycling or sale to informal sectors or given to
somewhere/someone by owners.
 Food residues: waste items kept for livestock (e.g. pigs) feeding; generally collected bylivestock breeders.
 General waste: all remaining waste items excluding separated waste items describedabove. This type of waste is collected daily by an environmental company in Hue (HEPCO).
From Table 2-2, the survey also indicated that a majority of hotels engaged in recycling activity.
There were 41 of 43 target hotels participated in some form of recycling, which is contrasted with the 2of 43 hotels did not participate in any recycling activity. Of total hotels, most of hotels in 3-star, 4-starand 5-star class had separation of both recyclable and food residue (hereinafter referred to as “R&F
Separation”). However, in the “lower class”, the separation status was different. There was 86% of 2star hotel having separation for both (recyclable and food residues). The rest 14% separated forrecyclable only. Regarding the 1-star hotel, 40% hotel having separation for both, 30% separated
13

recyclable only, 20% separated for food residues only and the rest part (10%) did not separate waste in 3
parts. Regarding the guest house, due to no food service at this category, all of guest house did notseparate food waste. 88% guest house separated recyclable only and 12% having no separation The
recycling inactivity of this group was explained by the fact that recyclable or food residues amount were
too small or it due to the time consuming, storage place and behavior of manager and staff at hotel.
2.1.2.2 Outline of survey
Table 2- 2 Waste separation status at the target samples
Hotel class

N

Guest house 10
Lower class 1-star hotel 10
2-star hotel
7
3-star hotel
8
7Higher class 4-star hotel
5-star hotel
3

R&B
Recyclable
FR
No
Separation Separation Separation Separation
1
4
6
8
7
3

7
3
1
-

1
2
-

1
1
-

R&B separation refers to hotels have separated both recyclable and food residue
Recyclable separation refers to hotels separated the recyclable only
FR separation refers to hotels separated the food residue only
No separation refers to hotels didn’t engaging separation activity

The procedure of waste generation survey followed the methodology by Matsui et al. (2014).
The authors conducted three surveys for all target samples: a waste generation survey by actual
measurement, a waste composition survey, and a questionnaire survey. Surveys were conducted during
2-11 June, 2012. The waste generation survey was administered to acquire data on the amount of waste
generation for 10 consecutive days. Of them, the first three days were spent for practice; the authors
used the data for the latter seven consecutive days.
A waste composition survey was also conducted during the survey period. To provide information
related to the recycling and composting potentials, the authors analyzed details of the waste compositionof ‘General Waste’ for some representative targets. The waste was classified into 10 physical categories
and 77 sub-categories. Descriptions of waste classification categories as well as potential for recycling
and composting are presented in Table 2-3. The authors also conducted a questionnaire survey to assess
attributes and the current status of hotel sectors in Hue.
By observation in study on hotel scale and service, the authors categorized 6 hotel classes into 2
14

categories: “Lower class” guest house, 1-star hotel and 2-star hotel and the remained classes belong to“Higher class”.
In the “Higher class”, most of hotels have large number of room (50 rooms or more) with a lot ofservices such as restaurant, bar, spa and meeting room to organize the events. In the “Lower class”,
normally the number of room is smaller than that of “higher class”, some hotels have restaurants whilesome did not had. Most of hotels in this category did not organize the event due to the limited space
excepting some special cases.
In our study, some target samples separated waste into 3 types: general waste, recyclable andfood residue. On the other hand, some separated recyclable only, some separated food residue only whilesome did not separate recyclable and food residues. Most of hotels in “higher class” separated waste in 3
above-mentioned types while the hotels in the “lower class” had different styles in separating waste.
2.1.2.3 Analytical procedure
The authors calculated basic statistics related to waste generation rates by some indicators suchas hotel class, number of room, number of check-in guest and number of staff. The authors also assessed
the mean difference among hotel classes using analysis of variance (ANOVA) to check the differences15

Table 2- 3. Classification category of waste from hotel
Code

Details

Recycling
potential

101
102
103
103*
104
104*
105
105*
106
107
108
108*
109
109*
110
110*

PET bottle
Other plastic bottle
Tray
Tray
Tube
Tube
Other shape
Other shape
Shopping plastic bags
Other plastic packaging
Other C&P (e.g.: buffer)
Other C&P
Plastic product
Plastic product
Other plastics
Other plastics

Re
Re
Re
NRe
Re
NRe
Re
NRe
Re
Re
Re
NRe
Re
NRe
Re
NRe

201
Carton202
Containers
Container &203
Cardboard
Packaging204
Packaging
205
Other C&P
206
Newspaper/poster
207
Books
208
Notebooks
209
Photocopy
Product
210
Disposal paper products
210* Nappies/Diapers
211
Other paper product211* Other paperproduct212
Other Paper
Other Paper
212* Other Paper
3. Kitchen waste
Compostable 301
Kitchen waste
301* Coconut/Durian shells
Noncompostable 302
Hard bones of animal
4. Rubber and leather
401
Rubber and leather
5. Grass and wood
501
Garden waste
Garden501* Garden waste
waste
502
Flower

Re
Re
Re
Re
Re
Re
Re
Re
Re
NRe
NRe
Re
NRe
Re
NRe

Category

1. Plastic

Container &Packaging

Product
Other
plastics
2. Paper

Co
NRe
NRe
NRe
Co
NRe
Co

CategoryCode

5. Grass and wood
503
Container&
Packaging
503*
Product&Other
504
s
504*
6. Textile
601
602
603
604
7. Metal
701
702
Aluminum
702*
703
703*
704
704*
Steel
705
706
Stainless
707
Lead
707*
708
Other metals
708*
8. Glass
801
Container
802
803
Products and 804
others805
9. Ceramic
901
902
10. Miscellaneous
1001
1002
1002
*
1003
1004
1005
1006
1007
1008

Details

Recycling
potential

Containers & packaging
Containers & packaging
Grass and wood products
Grass and wood products

Co
NRe
Co
NRe

Clothes
Daily commodities
Disposed commodities
Other product

Re
NRe
NRe
Re

Containers
Other containers and packaging
Other containers and packaging
Products and others
Products and others
Containers
Containers
Other containers and packaging
Products and others
Products and others
Products and others
Other metals
Other metals

Re
Re
NRe
Re
NRe
Re
NRe
Re
Re
Re
NRe
Re
NRe

Returnable bottle
Disposal bottle
Other containers
Thermometers,Fluorescentlamp
Products and others

Re
Re
Re
NRe
NRe

Containers
Products and others

NRe
NRe

Combustibles
Liquids_ edible
Liquids _ inedible
Incombustibles (excluding ash)
Ash
Medical care (syringe, needle,…)
Batteries

NRe
Re
NRe
NRe
NRe
NRe
NRe
NRe
NRe

E-wasteOthersa
Re, Recyclable; Co, Compostable; NRe, Non-recyclable & non-compostable item.The recycling potential of each item was defined based on reports
from two junk-shop owners. The compostable item and non-compostable item weredefined based on the acceptable items in some composting plants.

16

among hotel class. Moreover, the important variables that affect the quantity of waste generated
from the hotels have been identified.
In this study, the authors developed predictive models for waste generation amount (WGA) on
general waste, separated recyclable, separated food residues and total waste amount using the multiple
regression analysis. The potential of recycling and composting that still remains in general waste wasestimated based on the general waste amount and percentage of recycling part and composting part fromcomposition survey.
By a Monte Carlo simulation, the interval estimation for total waste generation in Hue in 2012
and 2020 was simulated and estimated. The Uncertainty analysiswas conducted to evaluate the effects ofeach parameter on the total waste amount.
2.1.3 Results and discussion
2.1.3.1 Total waste amount by hotel class
The authors calculated and analyzed the measured data over 7 consecutive days. The averages of
total waste amount for each hotel class were calculated by the average of each hotel in the same class.
The standard deviation was also considered to show how the individual data of each hotel vary aroundthe mean of hotel in the same class. The table 2-4 summarized the total waste amount by 6 hotel classedwith mean, standard deviation and ANOVA results.
Table 2- 4Total waste generation amount of hotel
by day (kg/day)
Class
Lower
Class
Hotel

n

Mean

Guest House 10
5.25
1 star Hotel10
7.81
2 star hotel
7
29.43
Higher
3 star hotel
8
68.75
Class
4 star hotel
7 190.49
Hotel
5 star hotel
3 271.75*p<0.05, ** p<0.01, *** p<0.001

Standard
deviation
3.25
4.89
18.84
36.72
57.3
13.49

ANOVA
F

The ANOVA results showed that
there was a significant difference among 6
hotel classes (p<0.001). The reason for thatdifference is the fact that the services, scale
and area were so different among hotel

76.96***

classes. Hence, it is important to discuss
waste generation rate of hotel class. Thisfinding also suitable with some previous

studies that also discuss on waste generation rate by hotel class. For instance, the study on hotel sector in
Halong, Vietnam by Byer el at (2006) chose the target samples of hotel by hotel rating such as 2-star, 3star, 4-star hotel. And another study by Trung el at (2003) discussed the waste generation rate by 4 hotel
categories (4-star, 3-star, 2-star and resort).
Interestingly, there was a link between the rating and the waste generation amount. Since thehigher rating hotels have tendency to produce higher waste generation amount. In which, the total waste17

generation amount of 5-star hotel was considerably high with 271.75 kg/day, followed by 4-star hotel(190.49 kg), 3-star hotel (68.75 kg), 2-star hotel (29.43 kg), 1-star hotel (7.81 kg), and the lowest
amount of 5.19 kg was from guest house.
These differences can be due to most of the hotels of “3-star hotel”, “4-star hotel” and “5-star
hotel” have food services such as restaurants and coffee shops/bars that serve foods and drinks for
Table 2- 5 Relevant factor influencing hotel waste generation
Number ofroom

Number of bed

Total waste
General waste
Recyclable.863***.811***.707***.811***.762***.650***

Number of CheckinGuest.584***.517***.527***

Food residues.812***.780***.643***

Waste type

Number of
event guest

Number of staff.713***.698***.511**.863***.840***.692***.764***.789***

Pearson correlation; *p<0.05, ** p<0.01, *** p<0.001

breakfast, lunch, and dinner. These services were randomly appeared at “2-star hotel” and rarely at “1star hotel”. The author could not find any restaurant services at guest house in survey. The result of 4star hotel in this study was similar with the result of study in Halong for 4-star hotel with 203.3 kg/day,and significantly lower than the amount for 3-star hotel (114.3 kg/day) and 2-star hotel (89 kg/day) ofsurvey in Ha long [2]. However, the sample size in their study was too small with 1 sample at each hotel
class, so it is difficult to compare the result of 2 studies.
The SD was very small at 5-star hotel class while that of 4-star, 3 star, 2 star, 1 star hotel andguest house were relatively large, respectively. It revealed that the slightly difference of average of total
waste among 5 star hotels and the fluctuation among hotels in the other hotel classes.

2.1.3.2 Relevant factor influencing waste generation amountTo interpret the relationship between the waste amount of general waste, separated recyclable,
separated food residues and total waste (4 waste categories) with relevant factors such as hotel class,number of room, net-sale, number of bed, staff, check-in guest and event guest, various statisticalanalysis procedures were employed. Table 4 showed the correlation of 4 waste categories with theabove-mentioned factors.
The Pearson correlation was used to check the correlation between waste generation rate withsome relevant factors such as number of room, number of bed, number of check-in guest, number of
event guest and number of staff. The analyses were implemented by 4 waste types: general waste,18

recyclable waste, food residues and total waste.The results indicated that significant differences were
found in most of waste types and relevant with strong significant (p<0.001) except the correlation
between recyclable waste with number of event guest with p<0.01. The results also revealed that the
number of room, bed, staff, event guest and check-in guest were affected to the amount of general waste,
recyclable, food residues and total waste.
2.1.3.3 Daily waste generation rate by category
Table 2-6 showed the detail waste generation rate by room, check-in guest and staff on total
waste, general waste, separated recyclable and separated food residues.
The average amount of waste generation rate was still highest at 5-star hotel with 1.61
kg/room/day, followed by 4-star (1.46 kg/room/day), 3-star (1.16 kg/room/day), 2 star (0.76kg/room/day). The waste generation rate by hotel at 1-star hotel was higher in guest house but the waste
generation rate by room at 1-star hotel (0.39 kg/room/day) was less than guest house (0.48 kg/room/day).
The difference in result may be due to the number of room in 1-star is larger than in guest house and thenumber worker, one of some factors affecting to waste generation rate, in 1 star hotel is also higher inguest house.
Table 2- 6 Daily waste generation rate by category
CategoryN

Guest house
1-star hotel
2-star hotel
3-star hotel
4-star hotel
5-star hotel

10
10
7
8
7
3

Guest house
1-star hotel
2-star hotel
3-star hotel
4-star hotel
5-star hotel

5
4
4
7
6
3

Guest house
1-star hotel
2-star hotel
3-star hotel
4-star hotel
5-star hotel

10
10
7
7
7
3

19

Total waste
General waste
Recyclable
Min-Max
Mean
Min-Max Mean
Min-Max Mean
Waste generation rate by room (g/room/day)
196-1127534
117-1032
398
0-95
42
175-717389
140-478
2940-95
35
291-1165757
250-575
415
26-96
45
563-1998
1155
258-9046062-86
42972-22001455
531-1240
769
39-14786
1197-2151
1607
685-1560
995
48-12285
Waste generation rate by check-in guest (g/guest/day)
361-775557
89-6333890-63
32465-717594
321-717484
0-73
35
312-639485
236-385
283
16-44
26
782-2009
1335
425-1036
713
27-58
48
1520-2558
1897
627-1560
1023
82-181120
2012-7969
5538
935-4939
3562
166-317
249
Waste generation rate by staff (g/staff/day)
992-63772133
245-5593
1744
0-784
216
395-1238793
331-973
604
0-165
67
582-2562
1392
414-1417756
30-164
82882-1860
1403
516-1012
738
29-111
53665-1707
1151
405-962606
25-114681063-1401
1263
652-961757
42-11670

Food residues
Min-Max Mean
0-560
0-155
0-653
241-1012
356-822407-666

95
61
297
507
600
526

0-392
0-155
0-321
298-992594-900
910-2713

137
75
176
575
754
1727

0-932
0-466
0-1437303-843234-702311-634

172
122
555
611
476
436

2.1.3.4 Waste composition, potential of composting and recycling
Table 2-7 presented the hotel waste composition (in percentage) of general waste by 10 physicalcategories. It was revealed that food waste accounted for the largest proportion in all hotel categories.
That proportion of food waste was the highest in 5 star hotel (56.6%), followed by 3 star hotel (54.6%),
guest house (49 %), 1 star hotel (47.0%), 4-star hotel (43.6%) and 2 star hotel (41.7%). Another study on
hotel in Danang conducted by Otoma et al. (2011) found that 65.5% of waste was food waste. However,
it is unfeasible to compare two studies because the target hotels in 2 surveys were different. The
composition result also showed that paper was the second largest ranging from 10.1% (5-star hotel) to
25.4% (1-star hotel), followed by plastic
Table 2- 7Physical composition of hotel (%)

ranging from 10.1 % (3-star hotel) to
15.5% (guest house).
Although the hotel sector in Huecity was doing recycling intensively by
informal sector such as recycling market
and livestock breeders, the large amount ofcompostable and recyclable parts were still
remainedingeneralwaste.

Becauserecycling activities were only performed on
the material that easy to be separated, theitems that were small and other organic
waste were often put into general waste todischarge. Table 2-7 also presented waste
composting and recycling potentials fromgeneral waste by hotel class according to
the definition shown in the Table 5. It is

PhysicalGuest 1-star 2-star 3-star 4-star
Plastic
15.5% 12.5% 11.1% 10.1% 11.6%
Paper
17.5% 25.4% 15.6% 14.1% 20.2%
Food waste
49.0% 47.0% 41.7% 54.6% 43.6%
Rubber &
0.1%
0.2%
3.4%
0.4%
0.2%
leather
Grass & Wood
2.8%
7.2%
9.7% 11.4% 6.7%
Textile
2.9%
2.9%
7.1%
2.0%
4.1%
Metal
0.9%
0.2%
1.8%
0.9%
0.4%
Glass
3.9%
1.4%
3.5%
0.5%
1.4%
Ceramic
1.3%
0.5%
0.6%
Miscellaneous
6.1%
3.2%
6.1%
5.5% 11.1%
52.3% 47.7% 41.1% 62.2% 50.7%
Compostable
28.9% 28.3% 29.8% 17.0% 20.5%
Recyclable
18.8% 24.0% 29.1% 20.8% 28.8%
Other residue
Contribution of components in recyclable part
Plastic_C&P
36.0% 30.5% 24.9% 45.3% 34.3%
Plastic_product 8.2%
4.7%
5.3%
5.8%
6.9%
Plastic_other
2.7%
2.6%
2.7%
Paper_C&P
23.4% 35.3% 13.5% 25.8% 19.8%
Paper_product6.1% 11.8% 11.8% 4.8%
6.5%
Paper_other
4.4%
3.6%
5.3%
1.8%
4.1%
Other material
21.8% 11.4% 36.7% 16.5% 25.7%
Contribution of components in compostable part
Kitchen waste
92.0% 79.5% 63.3% 80.6% 83.9%
Garden waste
3.9% 15.0% 22.9% 17.4% 12.8%
Other item
4.1%
5.5% 13.7% 2.0%
3.3%
C&P: container and packaging

5-star
12.5%
10.1%
56.6%
7.0%
0.9%
0.3%
1.1%
7.4%
4.2%
65.4%
12.4%
22.2%
36.6%
9.2%
0.1%
26.7%
5.3%
8.0%
14.2%
86.5%
10.5%
2.9%

apparent that the composting potential wasvery high in most of categories, especially at 5-star hotel (65.4%) and 3-star hotel (62.2%). The
recycling potential was highest in the 2-star hotel with 29.8% followed by guest house (28.9%) and 1star hotel (28.3 %), This results were similar with the study of Byer et al., 2006

and Otoma et al.,2013 in having high percentage of compostable.
The composition suvey also showed which components have largest contribution to the20

recyclable part and compostable part in general waste.

2.1.3.5 Predictive model of waste generation from hotel sector in Hue
The authors assumed that total waste amount of hotel waste divided in 3 parts: waste generated
from check-in guest who stayed in hotels, waste generated from event activity (i.g. wedding party or
conference) and waste from other services at hotels (i.e. Spa, restaurant, bar, staff, etc)
Among those factors showed in the table 2-5, the authors chose the number of room, number ofcheck-in guest and number of event guest as indicators for predictive model, because total number ofthese indicators was available in Hue city.
Regarding the “Higher class”, most of hotels in this class normally hold some events, so themodel for this category was developed by number of check-in guest, room and event guest. Regarding
the “Lower class”, there were rarely cases having events. So the author assumed that there were no event
guest in this category, and the model for waste generation amount in this category was developed by
Table 2- 8Predictive model of waste generation
Class

Items

General waste
Lower
class

Separated
Recyclable
Separated Foodresidue

Highercla
ss

Separation

Room(1) R&B separation(2) Recyclable
separation(3) FR separation(4) No separation(5) R&B separation(6) Recyclable
separation(7) R&B separation

173.3**

Check-inGuest
144.3**

278.7***

Event Guest

R2-

0.86***--

0.692***

159.1***
425.7***
52.6***--

0.902***
0.924***
0.875***-

63.4***-

0.958***-

209.9***-

0.872***(8) FR separation

75.3*

90.7*

-

0.834***(9) General waste(10) Recyclable(11) Food residue

530.3***
62.5***
356.5***

194.7***
23.8*
231.3***

313.5***
27.7*
223.0***

0.904***
0.737***
0.850****

p<0.05; **p<0.01; ***p<0.001

number of room and check-in guest. The results of multiple regression analysis for general waste,
separated recyclable and separated food residues are shown in the table 8.
Regarding “Lower class”, the author developed the models of general waste for 4 cases: R&B
separation, recyclable separation, FR separation and no separation. Results indicated that significancewas found at all models. In the equation 1, the amount of total waste is possibly correlated with thenumber of room (p<0.001) and number of check-in guest (p<0.001). The R-square value of model 1 is21

0.86, meaning that the abovementioned two variable explain about 86% of the variation of total waste
amount. The result of this model showed that, on average, each room generate 182.2 g total waste per
day and each check-in guest generate 208.0 g total waste per day
In the equation 2, there were significant correlation between general waste and number of room
(p<0.01). The coefficients of determination R-square is quite high, around 89%. The results of thismodel showed that everyday each room generated 278.7 g general waste.
The equation 3 showed that the GW only correlated with number of room (p<0.001). The Rsquare value in this model is relative high with 0.92. Similarly, the model on food residues andrecyclable also showed the correlation and the R-square in that table.
Regarding “Higher class”, the results showed that 4 models had the correlation between 4 types(GW, SR, SFR) and almost predictor variables (number of room, number of check-in guest and numberof event guest). The equation (9), (10) and (11) showed the model to predict the waste amount of GW,
SR, SFR, Respectively.

2.1.3.6 Estimation of total waste generation from hotel sector
To draw the waste flow of solid waste generation from hotel sector in Hue in 2012, theestimation of waste generation was applied. At first, the authors estimated the total amount of generalwaste, separated recyclable and separated food residue. Then, using the results of composition survey
with the estimated general waste amount, the authors estimated the potential of composting, recycling
and other residue in general waste. With the estimated results of 5 components: recyclable, food residues,
potential of composting, potential of recycling and other, the author draw the waste flow of waste
generation amount from hotel sector in Hue.

22

The detail procedure such as definition of 5 components, calculation base for estimation andmethod of estimation was shown as follows:
Table 2- 9 Total number of room, check-in guest and event guest in Hue city in 2012 and
2020
Items

Year

Lower
class

Higher
class

2012

4650

3112

2020

17177

11494

2012

5543

3426

2020

13137

8791

2012

0

572

2020

0

1468

Total room

Total checkin guest/day

Total
Number of
event
guest/dayCalculation base
The total number of room for “Lower class”was 3,866, that for “Higher
class” were 2587, and that for uncategorized hotels was 1309. For
uncategorized hotels, the author allocated the number of room according to
the ratio by Hue statistics in 2012 (lower class : higher class = 4:6)
Total room in Hue province in 2020 was estimated is 34,748. The
contribution of total room in Hue city to Hue province in 2012 was 82.5%.
The author used this ratio to calculate the total room of hotel in Hue city
Total number of check-in guest/year in Hue province in 2012 was
3,343,711, and the contribution of Hue city was 97.9% in Hue Province. The
check-in guest/night in Hue city was estimated as
3,343,711*97.9%/365=8,970. The total number of guest by hotel class wasallocated according to the abovementioned total number of room.
The total guest in Hue province per night in 2020 was estimated is8,174,200. The number of guest class was allocated by using the similarratio with 2012 case
Event guests were only found at higher class. The total number of event
guest was calculated based on the percentage of number of event guest to
check-in guest (16.7%) from the survey result.

Table 2- 10 The calculation base of estimation
Lower Class
Component

Items

Waste Separation

R&B
Separation

Recyclable
Separation

FR
Separation

NoSeparationHigher
Class

40%

44%

8%

8%

100%

Waste
Composition

Recyclable
Compostable
Other residue Portion

37.4%
40.1%
22.5%

40.2%
41.3%
18.5%

30.4%
50.0%
19.7%

31.1%
60.4%
8.5%

16.0%
61.0%
23.0%

Total
predictors

Number of room
Number of check-in guest
Number of event guest

1860
2217
-

2046
2439
-

372
443
-

372
443
-

3112
3426
572

Estimation method of waste generation amount and potential of hotel sector in Hue
By assigning the total numbers of room, check-in guest and event guest into the predictivemodels for waste generation, the authors estimated the total amounts of “General waste”, “Separated
recyclable” and “Separated food residue” by the following equation:

23

Recyclable Potential in GW = ∑ AGWi × %Ri(1)

Compostable Potential in GW = ∑ AGWi × %Ci(2)

Other residue in GW = ∑ AGWi × %Oi(3)

Where,
AGWi: Total amount of General waste at the separation category i%Ri: Percentage of recyclable portion in General waste at the separation category i%Ci: Percentage of compostable portion in General waste at the separation category i%Oi: Percentage of other residual portion in General waste at the separation category iwhere the separation category i was defined as follows:
1: Both separation at Lower class hotel; 2: Recyclable separation at Lower class hotel; 3: Food
Table 2- 11Total estimation of waste generation from hotel sector in Hue city
(Unit:ton/day)General Waste
Category

Separation
type

Both Separation
Recyclable only
Food residues only
No Separation
Total in Lower Class
Total in Higher Class
Total hotel in Hue (tons)

Lower
Class

%

Recycling
Potential
0.24
0.23
0.02
0.05
0.53
0.40
0.93
13.1%

Composting
Potential
0.25
0.24
0.03
0.10
0.61
1.55
2.17
30.5%

Other
Residue
0.14
0.11
0.01
0.01
0.27
0.59
0.87
12.2%

Separated
Recyclable

Separated
Food
residueTotalamount

0.10
0.15
0.25
0.30
0.54
7.7%

0.45
0.07
0.52
2.07
2.59
36.5%

1.18
0.72
0.13
0.16
2.19
4.91
7.10
100%

residue separation at Lower class hotel; 4: No separation at Lower class hotel, and 5: Higher class hotel.

Regarding the “Lower class”
hotel, there are 4-0% hotels wereHigher class

2.95

1.62

Lower class

1.24

0.95

0.34

separated waste into 3 categories.
However,

44%

hotelshave

Room
Check-in guest

separated only recyclables, thefood residues were put into generalwaste in these hotels. Besides,

Total hotel

4.19
0%

20%

40%

2.57
60%

0.34

event guest

80% 100%

there was only 8% hotels have
Figure 2- 1 Contribution to total WGA
24

separated food residue, the recyclables in these hotels waste put into general waste for discharge. And
there were 2 target hotels accounted for 8% hotels have not separated waste, all of waste generated inthese hotels was collected by environmental company.
Hue city has 413 hotels. By the above predictive models, the authors calculated the total amountof general waste, recyclable, food residue, and total waste of hotel sector in Hue. Table 2-11 presents
details of waste generation from a hotel sector in Hue: non-recyclable, recycling potential, composting
potential contained in general waste, recyclable, food residues, and total waste by weight (tons/day). The
total waste generation amount was 7.1 tons, of which 7.7 % of waste was separated at the source asrecyclable, 55.8 % was general waste, and 36.5 % was food residues. In general waste, the recycling
potential accounted for 13.1%, composting potential accounted for 30.5%, and the remaining waste
accounted for only 12.2 %.
Since the composting potential in general waste accounted for the large portion (30.5%) in total
generation waste, targeting the compostable part would markedly reduce waste produce across hotel
sector. Should all presently recyclable and compostable in general waste be diverted, the waste stream in
hotel sector would decline from 55.8% to 12.2%.

2.1.3.7 Interval estimation and Uncertainty analysisfor hotel
As a validation of the unit waste
generation rates, the author estimatedthe 95% confidence interval of waste

Table 2- 12Calculation condition for interval
estimation
Class

Room

Check-in Guest

Simulation based on the means andLower class112.9**(40.2)

409.5***(24.6)

standard errors of waste generation rates.

Higherclass

971.0***(70.3)

433.7***(79.2)

generation amount by a Monte Carlo

The results showed that the 95% CI of
total waste in Hue range from 6.51
tons/day to 8.23 tons/day.

25

Event
Guest

R2
.933***

349.9***(92.4).924***

0%

10%

20%

30%

40%

Check-in Guest in higher class

45.1%
25.4%

Room in higher class
18.5%

Room in lower class
9.1%

Check-in guest in lower class
Event guest in higher class

50%

1.5%

Figure 2- 2 Uncertainty analysis of total waste generationfrom hotel sector
Uncertainty analysiswas conducted to evaluate the effects of components on the overall results.
Fig.2-2 showed the Uncertainty analysisresult of total waste generation from hotel sector in 2012. The
results indicated that the total waste amount from hotel was mostly affected by the higher class hotel. In
which, the waste generation by check-in guest had high influence (44.5%) on the total waste, followed
by waste generation from room part in higher class (24.5%). The lower class had less impact to the total
WGA of hotel.
Conclusions
The general waste was classified into 10 physical categories and 77 sub-categories. Food waste
accounted for the largest proportion in all hotel classes, followed by plastic and paper. The results fromsub-categories demonstrated the potential for composting and recycling.The authors also estimated thetotal generated waste, the recycling and composting potential for 269 hotels in Hue. The total waste
generation amount was 6,750 kg/day. In general waste, the recycling potential accounted for 12.6 %,composting potential accounted for 32.3%, and the remaining waste accounted for only 14.2%. The total
disposal amount sent to the landfill site can be reduced from 59.1 % to 14.2%.

26

References
1. http://www.huecity.gov.vn/?cat_id=49
2. Byer, P.H., Hoang, C.P., Nguyen, T.T.T., Chopra, S., Maclaren, V., and Haight, M. (2006).
Household, hotel and market waste audits for composting in Vietnam and Laos, Waste
Management Research 24, 465–472, 2006.
3. The Decree TCVN 7799:2009 on the standards of tourist guest house
4. The Decree TCVN 4391:2009 on Hotel classification
5. Hue Statistical Yearbook, 2012. Statistical Yearbook, Hue city’s Statistical Office, Hue, Vietnam.
6. HEPCO, 2011. Report on solid waste management of Hue city. Hue Urban Environment andPublic Works State Company (HEPCO) (in Vietnamese)
7. Matsui, Y., Trang, D.T.T., and Thanh, N.P. (2014). Estimation of waste generation and recycling
potential from traditional market: A case study in Hue city, Vietnam. Journal of Environmental
Protection
8. Otoma, S., Hoang, H., Hong, H., Miyazaki, I., and Diaz, R. (2013). A survey on municipal solid
waste and residents’ awareness in Da Nang city, Vietnam. Journal of Material Cycles Waste
Management 15, 187–194.
9. Lieu, P. K., Tuan, T.N., Hoa, L.V.K. (2012). A study on material flow of recyclables handled byinformal sectors and their operational efficiencies in Hue (Report)
10. http://www.stat.yale.edu/Courses/1997-98/101/binom.htm
11. The Decision 2473/QĐ-TTg on the “Strategy on Viet Nam’s tourism development until 2020,
vision to 2030”, 2011

27

2.2

Solid waste generation from restaurant and beverage sector

2.2.1 Introduction
Viet Nam, after two decades of rapid economic growth, the living standard of citizens has been
improved dramatically. As the eating behavior changed, the opportunity for consuming food in therestaurant has been increasing. Besides, Vietnam has also been a growing tourist destination for not only
travelers within Asian, but also for increasing ones from the West. To fulfill the high demands fromlocal people as well as foreigners, the food and beverage service sectors have been expanded rapidly inrecent years. In FY 2014, the consumer price index of hospitality service in Vietnam has increased 39%
compare with FY2010 [9]. In consequence, the waste generation from these sectors has become agrowing problem. In recent years, 15 million tons of municipal solid waste is generated annually fromvarious sources such as industry sector, household, food service, market, and other business. Amongthem, more than 80% (12.8 million tons/year) comes from municipal source [1]. Understanding thecharacteristic of waste (waste generation, waste composition, and waste flow) deriving from municipal
sources, included food and beverage service sectors, is very necessary for designing the solid waste
master plan. However, there are few studies have specifically examined on waste generation andcharacteristics (e.g. recyclable, compostable) from food and beverage service sectors.
In 2003, one study on municipal solid waste was conducted in Phnom Penh, Cambodia by Japan
International Cooperation Agency (JICA). In this research, 70 samples from 5 restaurants were selected
for waste amount and waste composition survey, which were conducted for seven consecutive days inthe dry season and the rainy season. By multiplying the waste generation rate by table with the numberof generation sources (table), the authors estimated the total waste generation amount from restaurants in
Phnom Penh was 38.6 ton/day in the rainy season to 54 ton/day in dry season. Regarding the waste
composition results, the authors also estimated the amount of combustible and noncombustible wastefrom restaurant sector in Phnom Penh [6].
In 2010, one survey on solid waste generation was conducted in Danang City, Vietnam by
Otoma et al. For this study, six restaurants were chosen to measure the waste generation amount for one
week. Daily wastes from the restaurant were required to put into baskets for segregating and measuring,
without considering about waste sorting habit of owners. By multiplying with number of restaurant inDanang city, the authors estimated total waste generation amount was 6,314.4 kg/day [5].
In earlier studies mentioned above, the authors did not address the factors which might affect thewaste generation amount, such as business scales, business type, etc. Besides, the waste sorting behavior
28

was not mentioned; therefore, the boundary of waste generation did not include separated recyclable
waste and food residue, which were often kept and used for a particular purpose. Finally, the recycling
potential and composting potential in the disposal waste, which are valuable information for 3R
promotion, were not reported.
This study was undertaken to present a detailed description of waste flow in food and beverage
service sectors in Hue city – a representative tourism city in Central of Vietnam. The authors surveyed
108 restaurants and cafe shops with different scales for ten consecutive days to identify the waste
generation rate as well as their characteristics. The authors also estimated the total waste amount, therecycling and composting potentials from food and beverage sectors in Hue as the basis of rational
municipal solid waste planning including waste reducing and recycling.
2.2.2 Methodology
2.2.2.1 Sample selection
Hue city, which is located in the center of Vietnam, comprises 27 wards with an area of 71.69
km2 and a population of 432,556 people [2]. Hue city is well known as the most famous tourism
destination in the middle of Vietnam. In 2014, there were 1.84 million foreign and domestic tourists
visiting Hue [2]. Regarding solid waste management, the amount of collected waste in Hue city isreported as approximately 210 ton/day. The general collection rate in the whole town was about 89%,
and 90–95% in urban areas [3]. In 27 wards of Hue city, sampling points were selected based on
urbanization level and geographical distribution. The total list of each sub-category was prepared andsorted by the number of workers. The target samples have been chosen systematically from the sorted
list. The information of target samples was shown in Table 1.
2.2.2.2 Definitions and terminology
In Hue city, the food & beverage service sectors are divided into four main groups:
 Café (CF): a cafe shop serves coffee and/or other beverages. Based on the business scale, thetarget samples in beverage service category were classified into three subcategories: CF1, CF2,
and CF3, which has one worker, two workers and three or more workers, respectively; Small scale restaurant (SR): a restaurant serves various kinds of food in a small scale, e.g.
noodles, steamed rice, a various disk with drinks. The number of employees is less than ten ingeneral. Based on the business scale, the target samples in small scale restaurant category wereclassified into four subcategories: SR1, SR2, SR3 and SR4, which has one worker, two workers,
three workers, and four or more workers, respectively;29 Large scale restaurant (LR): a restaurant serves various kinds of food in a large scale.
Sometimes it serves events, such as party, buffet. Number of workers is more than ten;
 Vendor (VR): Someone who sells goods without a contract for a particular period of time,
generally located on the pavement. There is no statistical data about this sector.
During the survey, the target restaurant/cafe shops/vendors were requested to keep their waste inthree categories by their original customs: Recyclables, Food residues, and General waste defined as
bellows:
 Recyclables waste (RW): separated recyclable waste is the items kept for recycling or sale toinformal sectors or given to somewhere/someone by owners;
 Food residues (FR): separated food residue is the waste items kept for livestock (e.g. pigs)
feeding (generally collected by livestock breeders).
 General waste (GW): all remaining waste items excluding separated waste items describedabove. This type of waste is collected daily by an environmental company in Hue (HEPCO).
For general waste, some amounts of recyclable and compostable were still remained. The authors
defined 3 sub categories as bellows: Recycling potential (RP): the recyclable portion discharged in general waste; Composting potential (CP): the compostable portion discharged in general waste; Remains: The residual portion excluding recyclable and compostable portions discharged ingeneral waste.
Table 2- 13 Information of target samples
Cafe
Code
Number of worker

Small scale restaurant
SR1

SR2 SR3 SR4

LR

VR

1

2

≥3

1

2

3

≥4

>10

-

9

7

7

14

24

15

12

15

5

921

793

211

1068

836

212

190

131

N/A

Total number in Huecity [2]Vendor

CF1 CF2 CF3

Number of target
samples

Large scale restaurant

2.2.2.3 Outline of survey
The procedure of waste generation survey followed the methodology by Matsui et al. (2014) [4].
The authors conducted three surveys of all target samples: a waste generation survey by actual30

measurement, a waste composition survey, and a questionnaire survey. Surveys were conducted during
July 11th to July 21st 2012. The waste generation survey was administered to acquire data on theamount of waste generation for 10 consecutive days. Of them, the first three days were spent onpracticing; the authors used the data for the latter seven consecutive days.
A waste composition survey was also conducted during the study period. To provide information
related to the recycling and composting potentials, the authors analyzed details of the waste compositionof ‘General waste’ for some representative targets. The waste was classified into 10 physical categories
and 77 subcategories, as shown in Table 2. This classification category was based on Materials, Plastic,
Paper, Kitchen waste, Rubber & Leather, Grass, Textile, Metal, Glass, Ceramic, and Miscellaneous,
Types (Container/Packaging, Product and Other), Recycling potential (recyclable and non-recyclable)
and Composting potential (compostable and non-compostable). The authors also conducted aquestionnaire survey of the attributes and the current status of food & beverage service sectors in Hue.
2.2.2.4 Analytical procedure
The authors calculated key statistics related to waste generation rates by some business scale
indicator such as the number of workers, the number of tables, and the business area. The authors also
assessed the mean difference by business scale by analysis of variance (ANOVA) as well as rankcorrelation analysis. The waste composition by percentage (%) was calculated according to the physicalcategory and by recycling and composting potentials.
By multiplying the waste generation rate by worker and total number worker of each restaurantand cafe shop in Hue, the authors estimated the total waste generation amount from food and beverage
service sectors in Hue city. The 95% confidence interval of total waste generation amount wascalculated by the Monte Carlo simulation (100,000 times). The authors also analyzed the sensitivity as apercentage of the contribution from each parameter to the variance of the final result.

2.2.3 Results and discussion
The valid response rate was 88% (95/108) in the survey, excluding the responses with errors anddata lacks.
2.2.3.1 Total waste generation amount
The waste generation amount of food and beverage service sectors were calculated by eachcategory. Table 14 presents the number of target samples, mean and standard deviation of waste
generation amount (g/day) of café shop, small scale restaurant, large scale restaurant, and vendor in Hue
31

city. There was a significant difference among the category. In food service sectors, large scale
restaurant (LR) discharged a large amount 45 kg/day, three times higher than small scale restaurant (SR)with only 15 kg/day. Regarding Otoma et al., the waste generation amount of restaurant in Danang city
was 35 kg/shop/day, but the scale of the restaurant was not mentioned [5].
Table 2- 14 Total waste generation amount by each category by day
CF
WGA
g/day

n

SR

Ave±SDnLR

Ave±SDn

Ave±SD

VENDORn

Ave±SDF(ANOVA)

19 6,326±6,659 58 15,162±13,201 13 44,962±24,479 5 1,779±1,364 23.141****: p<0.05, **: p<0.01, ***: p<0.001
2.2.3.2 Total waste generation rate
Table 2- 15 Total waste generation rate of each category
CF

SR

LR

VENDOR

Total WGRn

Ave ± SDn

Ave ± SDn

Ave ± SDn

Ave ± SDF
(ANOVA)g/worker/day 19 2,764±1,407 58 5,686±3,758 13 3,877±1,386 5 1,779±1,364 6.226**
g/table/day

19 591±414

58 1,358±1,220 13 1,681±888--7.197***

g/m2/day

15 146±136

38 318±289--

3.964*

6345±109*: p<0.05, **: p<0.01, ***: p<0.001
In Table 15, the authors showed waste generation rate by the following three business scale
indicators: the number of workers, the number of tables and the business area. The number of samples,
averages and standard deviations (Ave ± SD) of waste generation rates of 95 cafe shops and restaurants
were shown. The average waste generation rate by worker from cafe shops was 2,764 g/worker/day. In
food service sector, the waste generation rate by worker from small scale business (SR) and large scalebusiness (LR) was 5,686 g/worker/day and 3,877 g/worker/day, respectively. There were significant
differences among these categories. The average waste generation rate by table was from 1,358 to 1,681
g/table/day, which was similar to the rate 1,560 g/table/day reported in the study of Otoma et al. (2013)
in Danang City, Vietnam [5]. Another study in Cambodia reported that the waste generation rate by tablein restaurant sector were 1,387 - 1,940 g/table/day [6].
2.2.3.3 Waste separation status

32

Table 2- 16 Waste separation participation of target samples
Bothseparationn%

Recyclable
separation onlyn%

Food residue
separation onlyn%Without
separation
n
%

CF

2

11%

11

58%

4

21

2

11%

SR

30

52%

4

7%

22

38%

2

3%

LR

11

85%

0

0%

1

8%

1

8%

VR

0

0%

2

40%

1

20%

2

40%

Business
Category

The waste separation activity of target samples was categorized in 4 cases: Both separation,
recyclable separation only, food residue separation only, and without separation. The waste separation
activities of 95 target samples were shown in Table 16 with the number of cases and the percentage. The
large scale restaurants (LR) were the most positive in waste separation with 85% cases separating bothrecyclable and food residue, followed by small scale restaurants (SR) (52%) and Café shop (CF) (11%).
In Café shop, the food residue (considered as the kitchen waste) was mainly green waste, mostly coffee
ground, which was sometimes used as compost. Typically, the shop owners discharged it with generalwaste.
2.2.3.4 Detail waste generation rate by business category
The basic stats (number of cases, Min, Max and Average) of general waste, recyclable waste andfood residue by each category were shown in Table 17. The waste sorting affected the recyclable waste
and food residue data with “0 value”, therefore, the authors did not show the SD for these categories.
However, the total waste generation was not affected by waste separation activity.

33

Table 2- 17 The basic stats waste generation rate of food and beverage service sector.
General waste

Business
Category

n

CF
SR
LR
VR

19
58
13
5

631 – 4,637
199 – 5,814
627 – 2,391
0 – 1,759

CF
SR
LR
VR

19
56
11
–

158 – 1,546
81 – 2,515
322 – 2,389
–

CF
SR
LR
VR

15
38
6
–

23 – 464
17 – 580
77 – 287–

Min – Max

Recyclable waste
Food residue
Min –
Ave
Ave
Min – Max
Ave
Max
Waste generation rate by worker (g/worker/day)
2,100
0 – 700110
0 – 3117553
1,808
0 – 41670
0 – 11,234
3,808
1,550
0 – 504
195
0 – 4,193
2,132
782
0 – 2,452754
0 – 1,216
243
Waste generation rate by table (g/table/day)449
0 – 7821
0 – 889
121553
0 – 347
23
0 – 5,632
1,137
849
0 – 31590
0 – 1,782
1,047
–
–
–
–
–
Waste generation rate by m2 (g/m2/day)
110
0 – 237
0 – 21129
137
0 – 645
0 – 1,043382
162
0 – 3713
0 – 335
345
–
–
–
–
–

Total waste
Min – Max

Ave

768 – 4,871
508 – 17,186
1,996 – 5,915
555 – 3,406

2,764
5,686
3,877
1,779

192 – 1,624
0 – 8,494
0 – 2,664
–

591
1,358
1,681
–

39 – 487
34 – 1,573
212 – 472
–

146
318
345
–

2.2.3.5 Factors influencing waste generation rate
Rank correlation analyses were conducted on the business scale (number of employees) with thewaste generation rate by worker with different waste types (general waste, recyclable waste, foodresidues, and total waste). In beverage service sector, there was a negative correlation between the waste
generation rate by worker of general waste, total waste and number of worker (F = - 0.542, p<0.05).
The authors also conducted analysis on variable on the waste generation rate by worker andseveral factors, such as independent business/household with business, fuel with ash/without ash, foodtype, with/without waste sorting. However, there was no significance different among them. In food type,there was a significant difference on recyclable waste generation by worker between a restaurant serving
various dishes and alcohol drinks (181 g/worker/day) and others (53 g/worker/day) (p<0.001). It was
responsible due to the drinking restaurant regularly discharged more cans and glass bottles than others.

34

2.2.3.6 Waste composition
Table 19 presents the food and beverage sectors’ waste composition of general waste by ten
physical categories. The food waste was the largest

Table 2- 18. Physical composition, composting andproportion in all categories, 37.1% - 49.8%. In therecycling potential

cafe, the food waste was quite high due to the

Physical categoryamount of coffee grounds, which is compostable. In
small scale restaurant category, the percentage of
grass and wood was quite high (22.7%) is
explainable that they frequently offered one-timeused chopsticks than others.
Table 18 also shows the potential forrecycling and composting in general waste of foodand beverage service sectors. Although the easily
separated items or valuable ones had been alreadyseparated, some parts of them remained in general

CF

SR

LR

Plastic
Paper
Food waste
Rubber & leather
Grass & Wood
TextileMetal
Glass
Ceramic
Miscellaneous
Total

8.3%
31.2%
49.8%
0.0%
3.6%
0.0%
1.6%
0.0%
0.1%
5.2%
100%

11.4%
11.4%
49.1%
0.0%
22.7%
2.5%
1.0%
0.8%
0.3%
0.7%
100%

8.5%
34.0%
37.1%
0.8%
8.2%
4.7%
1.8%
1.4%
2.6%
0.9%
100%

Remains
Recycling potential
Composting potential

27.1%
20.2%
52.6%

15.3%
18.6%
66.2%

51.4%
17.0%
31.6%

waste for discharge. The composting potential is the highest in SR category with 66.2%, followed by CF(52.6%) and LR (31.6%).

2.2.3.7 Estimation of total generated waste
To estimate the waste generation amount by five components, the authors recalculated the waste
generation rate by each category by four types of waste separation cases: (1) both separation, (2)recyclable waste separation only, (3) food residues separation only, and (4) no separation.
By assuming that Separated Recyclable Waste (SRW), Separated Food Residue (SFR) and Total
Waste (TW) were in proportion, the ratio (for example: based on data of Small Scale Restaurant
category) was identified as followed:
SRW : SFR : TW = 2 : 75 : 100
From this ratio, the authors calculated the Unseparated Recyclable Waste and Unseparated Food
Residue for cases: (2), (3) and (4).
For General Waste, the authors assumed that Recycling Potential Portion (RP) (except theUnseparated Recyclable Waste), Composting Potential Portion (CP) (except the Unseparated Food
35

Residues) and Remains Portion (RP) were in proportion. Therefore, the ratio (for example: based on datafrom the Small Scale Restaurant category) was identified as followed:
RP : CP : RP : TW = 4 : 15 : 4 : 100
Finally, the authors calculated the Recycling Potential Portion, Composting Potential Portion andRemain Portion for every case. By using these assumptions, the authors recalculated the waste
generation rate by each category. The average and standard error for each class were shown in Table 8.
Table 2- 19The recalculated waste generation rate by worker by each category
Unit: g/worker/day
Business
Category
Ave
SE
Ave
Small
restaurant SE
Ave
Large
restaurant SE
Cafe

Recycling Composting
Separated
Remains
Potential
Potential
Recyclable
154
22
301
30
240
25

591
110
1,071
106
445
46

241
27
247
24
724
75

176
35
111
12
225
44

Separated
Total
Food
Waste
Residue
1,601
2,764
214
323
3,956
5,686
373
493
2,242
3,877
320
384

Hue City has 4,362 restaurants and beverage shops, with different business scale. By multiplying
the waste generation rate by worker and the total number of worker by each category in Hue, the authors
estimated the total amount of general waste, recyclable, food residues and total waste in food andbeverage service sector in Hue city. The authors also determined the potentials of recycling andcomposting in the general waste of food & beverage service sectors in Hue city. The total waste
generation amount was calculated by the following equation:
=

(

+

+

+

+

)

Where:
Total: Total waste generation amount (ton/day);i: Business category (Café, Small Scale Restaurant, Large Scale Restaurant);
RWi: Separated recyclable waste by each category (ton/day);
FRi: Separated food residues by each category (ton/day);
RPi: Recycling potential portion in general waste by each category (ton/day);
CPi: Composting potential portion in general waste by each category (ton/day);
RMi: Remains waste in general waste by each category (ton/day);
36

The separated recyclable waste amount was calculated by the following equation:
=

×

×

× 10

Where:
ni: number of worker by each business category;
RWSi: Recyclable separation participation, (%);
RWGRi: Recyclable waste generation rate by worker, (g/worker/day);The unseparated recyclable waste was calculated by the following equation:=× (1 −)×× 10

Where:
URWi: Unseparated recyclable waste amount by each category, (ton/day);ni: number of worker by each business category;
RWSi: Recyclable separation participation, (%);
RWGRi: Recyclable waste generation rate by worker, (g/worker/day);The separated food residue amount was calculated by the following equation:
=

×

×

× 10

Where:
ni: number of worker by each business category;
FRSi: Food residue separation participation, (%);
FRGRi: Food residue generation rate by worker, (g/worker/day);The unseparated food residue amount was calculated by the following equation:=× (1 −)×× 10

Where:
UFRi: Unseparated food residue amount by each category, (ton/day);ni: number of worker by each business category;
FRSi: Food residue separation participation, (%);
FRGRi: Food residue generation rate by worker, (g/worker/day);For this study, the authors assumed that the unseparated recyclable waste was disposed oftogether with general waste; therefore, the recycling potential portion in general waste was calculated by
the following equation:=
Where:
37

×%

×

× 10

+

ni: number of worker by each business category;%RPi: Percentage of recycling potential portion in general waste, (%);
GWGRi: General waste generation rate by worker, (g/worker/day);
URWi: Unseparated recyclable waste amount by each category, (ton/day);By the same concept mentioned above, the authors also assumed that unseparated food residues
were disposed of together with general waste; therefore, the composting potential portion was calculatedby the following equation:
=

×%

×

× 10

+

Where:
ni: number of worker by each business category;%CPi: percentage of composting potential portion in general waste, (%);
GWGRi: General waste generation rate by worker, (g/worker/day);
UFRi: Unseparated food residue amount by each category, (ton/day);The remains in general waste were calculated by the following equation:
=

×%

×

× 10

Where:
ni: number of worker by each business category;%RMi: percentage of remains portion in general waste, (%);
GWGRi: General waste generation rate by worker, (g/worker/day);Figure 1 showed details of waste generation amount and its’ contribution by each businesscategory of food and beverage service sectors in Hue city. The total waste generation amount was 39.85
ton/day, in which, the highest amount was separated food residues with 20.27 ton/day (50.9%),
following by general waste with 18.60 ton/day (46.7%), and separated recyclable waste with 0.98
ton/day (2.5%). For general waste, the composting potential was the highest amount with 7.86 ton/day(19.7%), following

38

Figure 2- 3 .Waste stream from food and beverage service sectors in Hue cityby remains and recycling potential portion with 7.29 ton/day (18.3%) and 3.44 ton/day (8.6%),
respectively.In food and beverage service sectors, the small scale restaurants contributed the highest
amount of waste with 24.58 ton/day (61.7%), following by café with 9.17 ton/day (23.0%) and large
scale restaurants 6.09 ton/day (15.3%). The small scale restaurants had the highest recycling potential
portions among three categories with 1.27 ton/day (3.2%) while the café had the largest composting
potential part with 4.15 ton/day (10.4%).
Even though the waste separation participation was high among three categories (Table 5), therecycling rates, however, still low according to the total separated amount. For recyclable waste
separation rate, the Café had the highest recycling rate of 29% (0.4/1.38 ton/day), followed by the large
restaurant and small restaurant with 20% (0.3/1.49 ton/day) and 18% (0.28/1.55 ton/day), respectively.
For food residue separation rate, the large scale restaurant and the small scale restaurant had the samerate with 83% (3.25/3.9 and 15.34/18.4 ton/day), followed by Café with 23% (1.68/5.82 ton/day). The
results also showed that the total disposal waste amount delivered to landfill site can be reduced from18.60 to 7.29 ton/day by separation activity (about 60% amount).

39

2.2.3.8 Interval estimation
For this study, the authors conducted the 95% confidence interval (CI) of total waste generationfrom food and beverage service sectors in Hue by a Monte Carlo simulation based on the means and thestandard errors of waste generation rates, the waste composition, and the percentage of separation
activities among the categories.
For simulation assumption, all the variables were defined as Normal distribution, except
variables of the percentage of recyclable waste separation and food residue separations were defined as
Beta distribution. The Beta distribution has been applied to model the behavior of random variables
limited to intervals of finite length in a wide variety of disciplines. In the past studies, Beta distribution
has been widely used for the lot fraction defective in industrial management field [7]. M. Yusof
Sulaiman applied beta distribution to model the sunshine data in Malaysia [8].etc. For this study, theauthors used Beta distribution to represent a probability distribution of probabilities - that is, it
represents all the possible values of probability when the probability of waste separation was unknown.
The 95% CI of total waste generation was wide 36.01 – 43.64 ton/day as shown in Table 9. The results
also showed that the recycling potential and composting potential were 3.08 – 3.81and 6.82 – 8.91
ton/day, respectively. Further study should be conducted on the detailed market value of recyclable
waste from general waste for waste separation promotion in food and beverage service sectors.
Table 2- 20. The 95% confidential intervals of waste generation
95% CI
Waste category

Unit

MedianRecyclable waste
Food residues
Recycling potential portion
Composting potential portion
Remains
Total

Ton/day
Ton/day
Ton/day
Ton/day
Ton/day
Ton/day

0.98
20.26
3.44
7.86
7.30
39.84

Lower
bound
0.78
17.26
3.08
6.82
6.13
36.01

Upper bound
1.18
23.28
3.81
8.91
8.45
43.64

Uncertainty analysis

To estimate the impact of each waste generation rate affecting the CI of the total waste amount, asensitive analysis for all categories of food & beverage service sectors were conducted. Figure 2
presents the result of sensitive analysis of total waste generation amount. The food residue generation
rate of small scale restaurant category contributed the highest effect on sensitivity (69.5%), followed byfood residues generation rate of Café category (12.8%) and food residue generation rate of large scale40

restaurant
estaurant (6.8%). It can be explained that the food waste from small scale restaurant was the highest
amount of 38.5% of total waste generation amount; therefore, it had the largest contribution to theUncertainty analysisresult.
result. However, to improve the reliability
reliability of total estimation, further investigationmust be conducted to clarify the factors affecting waste generation rate in these categories.

Figure 2- 4. Uncertainty analysis of total waste generation amountfrom food and beverage service sector
Conclusion
1)

In order to clarify the waste generation rate from food & beverage sectors andrelevant factors, the authors conducted a survey for 10consecutive days in Hue City, Vietnam
with 108 target samples with different business scale.
2)

There was a significant different among business category on the waste generation

rate (by worker, by table and by area) and the business type. However, there was no significant
difference among categories of the waste generation rate and several
several factors, such as; independent
business/household business, fuel with/without ash, food type, with/without waste sorting. There
was a significant difference in recyclable waste generation by worker between restaurant serving
various dishes and alcoholl drinks (181 g/worker/day) and others (53 g/worker/day) (p<0.001).
3)

The small scale restaurant (SR) had the highest waste separation participation with

97% target samples, followed by large scale restaurant (LR) with 92% target samples. Waste
separation efficiency
ficiency in café and vendor were 68% and 60%, respectively. Based on the separated
waste mount, however, the waste separation rate was not high among categories. Recyclable
waste separation at Café category was the highest with 29%, followed by large resta
restaurant andsmall restaurant with 20% and 18%, respectively. For food residue separation rate, the small
restaurant and large restaurant had the same rate with 83%, followed by Café with 23%.
41

4)

For general waste, food waste accounted for the largest proportion in all

categories, 37.1% - 49.8%. The composting potential and recycling potential percentage were
31.6% - 66.2% and 17% - 20.2%, respectively.
5)

Based on the waste generation rate by worker and the total number of worker of

each category in Hue city, the author estimated the total waste generation amount from food &beverage service sectors in Hue city, which was 39.85 ton/day. For general waste, the recycling
potential, and compostable potential accounted for 3.44 ton/day (8.6%) and 7.86 ton/day (19.7%),
respectively. The total amount of disposal waste delivered to landfill site can be reduced from18.60 ton/day (46.7%) to 7.29 ton/day (18.3 %).
6)

The 95% confidential interval estimation of total waste generated from food andbeverage service sectors in Hue City was 36.01 - 43.64 ton/day. The 95% CI of recycling
potential and composting potential amount were 3.08 - 3.81 ton/day and 6.82 to 8.91 ton/day,
respectively.
7)

The food residue generation rate of small scale restaurant (SR) had the highest

contribution in Uncertainty analysis, due to its highest percentage of discharged amount in total
waste generated amount.

References[1]

Vietnam environment monitor report, 2004. World Bank, MoNRE and CIDA. pp. 6[2]

Hue Statistical Yearbook, 2012. Statistical Yearbook, Hue city’s Statistical Office, Hue,

Vietnam.
[3]

HEPCO, 2011. Report on solid waste management of Hue city. Hue Urban Environment

and Public Works State Company (HEPCO) (in Vietnamese).
[4]

Matsui, Y., Trang, D.T.T., and Thanh, N.P. (2014). Estimation of waste generation andrecycling potential from traditional market: A case study in Hue city, Vietnam. Journal ofEnvironmental Protection.
[5]

Otoma, S., Hoang, H., Hong, H., Miyazaki, I., and Diaz, R. (2013). A survey onmunicipal solid waste and residents’ awareness in Da Nang city, Vietnam. Journal of Material Cycles
Waste Management 15, 187–194.
[6]

The study on solid waste management in the municipality of Phnom Penh in theKingdom of Cambidia (2005), JICA.
42

[7]

George, T., and Hau, L. L. (1987). Optimal Bayesian single sampling attribute plans withmodified beta prior distribution. Naval research logistics, Vol. 34, pp. 789-801.
[8]

Sulaiman, M.Yusof; Oo, W. M. Hlaing; Wahab, Mahdi Abd; Zakaria, Azmi (1999).

Application of beta distribution model to Malaysian sunshine data. Renewable Energy 18 (4): 573–579.
[9]43

General statistics office of Vietnam https://gso.gov.vn/default.aspx?tabid=720

2.3Solid waste generation from home-based business2.3.1 Introduction
Determining the volumes and types of waste generated within a region is of fundamental
significance for waste management planning. Data gathered with regard to waste type, quantity andorigin are important factors when designing collection systems, planning treatment capacities forrecyclables and determining the dimensions of waste disposal facilities such as incinerators or landfills.
In the past decade, strong economic growth and rapid rates urbanization have greatly magnified the
problems with solid waste management system in Vietnam, pushing waste management to the forefrontof environmental challenges with which it must contend. It is important to quantify the volumes of waste
generated according to type and origin. Furthermore, baseline data is important to ensure propermonitoring of results.
In 2007, The Prime Minister of The Government issued Decision No. 10/2007/QD-TTG dated
January 23, 2007. It is called the System of Economic Branches of Vietnam. According to this Decision
when people register the business, they must fill the code of the System of Economic Branches in the
Application for Investment License for easy management. That system has total 642 economic branches
in detail. In order to provide the scientific information that can contribute to promotion of 3Rs at homebased business, this study aimed to clarify the detail description of solid waste generation andcomposition of Home-based business in Hue City, Vietnam. The authors surveyed 245 households thatbelong to 7 business categories at level 1. The authors conducted a survey on the waste collected byformal sectors, and also measured the amount of separated recyclable and food residue collected byinformal sectors. The waste collected by formal sectors was classified into ten physical categories and77 sub-categories to identify potentials on recycling and composting contained in waste. This study also
conducted an interval estimation of total waste amount from home-based business in Hue, and intended
to clarify the reliability of collected data and future tasks to improve it by uncertainty analysis.
2.3.2 Methodology
2.3.2.1 Sample selection
There are totally 11,639 home-based businesses with 18,777 employees in Hue city. The authors
covered 7 Home-based business categories at level 1 according to “The system of economic branches ofVietnam, 2007”. The detail sampling method was described as follows:
The System of Economic Branches of Vietnam
According to this Decision, The System of Economic Branches of Vietnam comprises five Levels as
follows:
 Level 1 comprises 21 branches coded by the letters of the alphabet in alphabetical order from A
to U;
 Level 2 comprises 88 branches; with each branch coded by two numbers after those of thecorresponding Level 1;
 Level 3 comprises 242 branches; with each branch coded by three numbers after those of thecorresponding Level 2;
44




Level 4 comprises 437 branches; with each branch coded by four numbers after those of thecorresponding Level 3;
Level 5 comprises 642 branches; with each branch coded by five numbers after those of thecorresponding Level 4.
Table 2- 21. The System of Economic Branches of Vietnam

35

E
F

36-39
41-43

G

45-47

H
I
J
K
L
M
N

49-53
55-56
58-63
64-66
68
69-75
77-82

O

84

P
Q
R
S

85
86-88
90-93
94-96

T

97-98

U
21

99
88

5th

642 branches, each branch coded by five numbers after those of the corresponding
Level 4D

3rd 4th242 branches, each branch coded by three numbers after those of thecorresponding Level 2437 branches, each branch coded by four numbers after those of the corresponding
Level 3Level
1st 2ndA 1-3
B 5-9
C 10-33

BRANCH

Target category

Agriculture, Forestry and Aquaculture
Mining Minerals
Manufacturing and Processing Industries
HH with business
Production and Distribution of Electricity, Natural Gas,
Hot Water, Steam and Air-Conditioning
Water Supply, Waste and Sewage Management
Construction
Sale and repair of automobiles, motors, motorbikes, etc. HH with business &Wholesale and retailMarket & supermarket
Transport and Warehouse
OfficeAccommodation and Restaurant Services
Restaurant & Hotel
Information and Communication
HH with business & OfficeFinance, Banking and Insurance
OfficeReal-Estate Business
OfficeProfessional Practice, Science and Technology
OfficeAdministrative Services and Assistant Services
HH with business & Office
The Communist Party, Civil society, State Officeadministration, National defense security, etc.
Education and Training
Hbb & education services
Health and social support activities
Hospital & healthcare services
Artistic, recreational activities
HH with business & Office
Other Services
HH with business & Office
Hired Labor services for Households, Producing Home
HH with business
Consumption Material Products
Operation of International Organizations and Agencies Office242 437 642

Based on the total list household with business in Hue, the total list of each sub-category was
prepared and selected systematically from the list. The sub-categories with more than 10 facilities werechosen to survey (only 55 in total 85 sub-categories were considered and surveyed).

45

Table 2- 22. Sample selection of home-based business
Level
1

Level
2
10
11
13
14
15
16

C

17
18
20
22
23
24
25

D
G
J
K
R
S

30
31
32
35
45
46
47
61
77
82
92
93
95
96

Category
Manufacturing and processing foodstuffs
Producing beverages
Weaving
Producing garments
Producing leather and related products
Processing wood and producing products from wood, bamboo andneohouzeaua (except for beds, wardrobes, tables, chairs); producing
products from straw, thatch and plaited materials
Producing paper and paper products
Printing, copying text of various types
Producing chemical and chemical products
Producing products from rubber and plastic
Producing products from other non-metal minerals
Producing metal
Producing products from prefabricated metal (except for machines andequipment)
Producing other means of transport
Producing beds, wardrobes, tables, chairs
Other processing and manufacturing industries
Producing and distributing electricity, natural gas, hot water, steam andair-conditioning
Sale, repair of automobiles, motors, motorbikes and other motor
vehiclesWholesale (excluding categories in 45)
Retail (excluding categories in 45)
Telecommunications
Leasing machines, equipment (without operator); lease of household or
personal
tools; leaseof intangible
non-financial
assetsAdministrative
practice,
office assistanceand otherbusiness assistantservices
Lottery, betting and gambling
Sporting and recreational activities
Repair of computers, personal and household tools
Other personal support services

Total
workerTotal
facility

N

1182
55
107
1655
170

469
29
49
991
79

20
2
2
5
4

650

255

4

477
105
132
119
282
126

223
48
70
45
136
44

5
3
4
2
5
3

772

362

10

2
27
705
24
59
1303
332
7458
515
207

1
6
400
13
23
767
184
5358
315
148

2
5
2
2
16
10
90
6
1

154

102

4

30
453
399
1533

26
220
320
1022

2
3
5
10

2.3.2.2 Outline of survey
The authors surveyed the waste collected by formal sectors, and also measured the amount of
separated recyclable and food residue collected by informal sectors. The procedure of waste
generation survey followed the methodology by Matsui et al. (2014). The authors conducted threesurveys for all target facilities: a waste generation survey by actual measurement, a waste46

composition survey, and a questionnaire survey. And the study also focused on waste generationfrom business and from home separately. A waste measurement survey was conducted to acquire
data on generation amount for seven consecutive days. Before the survey period, the authors spent
3 days for preparation and practice for surveyors and target facilities. The target facilities separated
and kept generated in three categories by their original customs; waste collected by formal sectors(hereinafter referred to as “General waste (GW)”), recyclable sold to informal sectors (hereinafterreferred to as “Separated recyclable (SR)”), and food residue sold/given to pig farmers (hereinafterreferred to as “Separated food residue (SFR)”).
Descriptions of waste classification categories were illustrated in Table 2.1-6. The authors asked
the persons in charge of facility management or waste handling on the attributes and current status
of target facilities by the structured questionnaire and interview. The questionnaire was designed toobtain the detailed information on relevant factors (business scale indicators) influencing waste
generation, recycling activities and attitudes toward solid waste management at each facility
2.3.2.3 Analytical procedure
The authors calculated basic statistics relating to waste generation rate (WGRs) by waste
generation amount from business activity and household separately. The WGRs from business is mainly
discussed by number of employee by detail business category. The mean differences of WGRs of total
waste among detail categories were assessed by Krustall-Wallis. Regarding the WGRs from household,the authors try to find the relationship between WGR and some factors such as household member,number of meal, number of worker, business categories.
The waste composition of GW by percentage (%) was calculated according to the 10 physicalcategories by Hbb category. And, based on 77 sub-categories, the authors also assessed the potentials on
recycling and composting in GW. The total waste generation amount from business activity at Hbb in
Hue was determined by extrapolating by total number of employee in Hue and the waste generation rateby employee.
2.3.3 Results and discussion
2.3.3.1 Waste generation rate
Home-based business activities were classified into 7 business categories at level 1:
Manufacturing and Processing Industries; Production Natural Gas, Hot Water; Wholesale and retail;Information and Communication; Administrative and Assistant Services; Recreational activities; Other
Services. The waste generation rate of business facilities was calculated by g/worker/day, and the waste47

generation rate of household was also separately estimated by g/capita/day in the chapter 4.
Table 2.3-3 presented theaverages and standard deviations (Ave ± SD) of waste generation rates by
g/worker/day from business activities. The significant difference among 7 categories was found by
ANOVA at total waste amount (p<0.05). The waste generation rate was high at category 1(Manufacturing and Processing Industries) and category 3 (Wholesale and retail). Standard deviations in
all categories were relatively wide because of containing various sub-categories.

Table 2- 23. Waste generation rate at level 1
Level 1
1
2
3
4
5
6
7

Categories
Manufacturing and Processing
Industries
Production Natural Gas, Hot Water,
etc.
Wholesale and retail
Information and Communication
Administrative and AssistantServices
Recreational activities
Other Services
ANOVA (F)N

GeneralWaste

Recyclable

Food
residue82828±2127439±1817200±1279 1467±2875

2

39±55

0±0

126
6

696±1644
143±129136±430
31±67

5

65±98

172±3170±0237±3166
18437±685139±19010.9126±112135±295
7.4

0±0
0±0
1.7

563±735274±363
13.0*

0±0

Total

39±55

323±2369 1154±2998
0±0
174±190

To understand more about the waste generation rate in each categories at level 1, the table 2.3-4 showed
the detail of waste generation rate and standard deviations on general waste, recyclable, food residue andtotal waste amount at 27 categories at level 2. The code 1 contained 16 sub-categories, while othercategories included only 1 to 3 sub-categories.

48

Table 2- 24. Waste generation rate at level 2
Level
1

Level
2
10
11
13
14
15
16
17
18
1
20
22
23
24
25
30
31
32
2
35
45
3
46
47
4
61
77
5
82
92
6
93
95
7
96
ANOVA (F)49

Categories

N

Manufacturing and processing foodstuffs
Producing beverages
Weaving
Producing garments
Producing leather and related products
Processing wood and wood products
Producing paper and paper products
Printing, copying text of various types
Producing chemical and chemical
products
Producing rubber and plastic products
Producing other non-metal minerals
products metal
Producing
Producing prefabricated metal productProducing other means of transport
Producing wooden furniture
Other processing and manufacturing
Producing and distributing steam and airconditioning
Sale, repair of motors and other motor
vehiclesWholesale (excluding 45)
Retail (excluding 45)
Telecommunications
Lease of household, personal tools, nonfinancial
assets practice and other assistant
Administrative
services
Lottery, betting and gambling
Recreational activities
Repair of computers and household tools
Other services

19
3
4
6
4
5
5
3
4
3
5
3
10
2
4
2
2
18
11
97
6
1
4
2
4
8
10

GeneralWaste
847±2587
156±270352±30778±99
591±4101666±1146
169±158
131±94256±254444±73191±96
2376±3678
308±346438±6195891±5105
21±29
39±55
205±2552272±4449
609±1053
143±12944±070±1120±0656±768
165±147118±224
47.32*

Recyclable
1451±3611
82±1420±00±00±0
403±76176±15448±60
301±360631±1077
238±1730±0
66±98
320±326
0±0
45±64
0±0
266±9011±4
127±30431±67
2±0
214±350148±209
116±78295±398
6±20
46.99*

Food
residue481±2096
2408±417
0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
419±2695
0±0
0±0
0±0
0±0
0±0
0±0
0±0
16.08

Total
2779±4384
2646±3980
352±30778±99
591±4102069±776
246±134180±80557±3981075±925329±2212376±3678
374±406757±9455891±5105
66±93
39±55
470±1027
2273±4449
1155±3036
174±19046±0284±343148±209771±845460±431125±22348.41**

Table 2- 25. Waste generation rate at level 4
Level 4
1020
1061
1062
1071
1073
1079
1079a
Lv
2

45

46

47

50

Category
Aquaculture products
Grinding and producing coarse flour
Producing refined flour and starch
Producing bread from flour
Coco, chocolate and jam production
Producing other foodstuffs
Producing tofu

Level
4
4520
4530
4541
4542
4543
4610
4620
4669
4711
4719
4721
4722
4723
4724
4741
4742
4751
4752
4759
4761
4762
4763
4764
4771
4772
4773
4774

N
3
3
2
5
3
2
1

GeneralWaste
3839±65940±00±0
318±214537±247691±5440±0CategoryN

Maintenance, repair of automobiles
Sale parts of automobiles
Sale of motors and motorcycles
Maintenance and repair of motors
Sale parts of motors and motorcycles
Agency, intermediary, auction
Wholesale of agricultural and forestry raw
materials
Other
wholesale
Foodstuff, beverages, cigarettes and other
Goods in department stores
Foods
Foodstuff
Beverages
Cigarettes, rustic tobacco.
Computers, software andtelecommunicationequipment
Audiovisual equipment
Cloth, wool, fibre and other textile goods
Gold, silver, copper, iron and tin, andglass
Household interior furniture andappliances
Books, newspapers & magazines
Audio, video tapes and discs
Sports equipments
Games and toys
Apparel, footwear, leather goods
Medical equipment, cosmetic products
Other commodities
Second-hand commodities

2
3
3
7
3
2
2
7
2
6
3
22
3
2
3
1
3
13
6
4
2
1
2
6
3
12
3

GeneralWaste
439±2598±13
37±63
246±287317±295
89±62
93±1313518±5291
847±91120±104
85±1021085±1068
54±50
546±73744±32
28±0
213±87872±1912
268±441281±36857±14
221±0
105±12360±319
115±79785±1091
1305±1839

Recyclable
53±59
8938±4183
0±077±123
49±14
32±45
0±0

Recyclable
83±66
0±0
85±14672±1081286±2227
0±0
0±0
2±5
46±65
393±4376±11101±229151±261
0±0
9±15
0±0
28±48
319±63065±107251±241
3±5
37±0
0±0
51±125209±78
31±99
47±82

Food
residue
0±0
0±0
0±0
0±0
0±0
0±0
9138±0Food
residue
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
14±24
1847±5518
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0

Total
3892±6548
8938±41830±0
395±228586±237722±4999138±0Total
522±1938±13121±127318±2581603±2494
89±62
93±1313520±5291
893±26513±377105±813033±5658
205±260546±73753±25
28±0
241±1191191±2523
332±428533±51260±18257±0105±12411±382323±25816±11171352±1806

Table 2- 26. Waste generation rate at level 573
5GeneralWaste
1628±845
236±3112087±10584

123±123145±1800±0269±230

3
2
3
2
3
3
5
2
5

282±224292±29938±15
383±3452495±3944
796±8401241±1112
1496±182544±48

131±122169±23125±2170±0828±1211315±54669±154
12±17
1±1

0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0
0±0

414±102461±321163±209383±3453323±5155
1111±1370
1310±1149
1508±1808
45±48

N
47221
47222
47223
47224
47229
47521
47522
47523
47524
47529
47731
47735
47739

Meat and products from meat
Retail of aquatic products
Vegetables and fruits
Dairy products, and products from grain,
flour
Other foodstuffs
Gold, silver, copper, iron and tin
Paint, colour and varnish
Construction glass
Construction materials
Installing construction equipment
Flowers, ornamental plants
Gas, fuel coal
Other new commodities

0±00±0251±434

Food
residue5804±8931
0±00±0

7431±8715
236±3112338±962

Recyclable

Total

2.3.3.2 Waste composition and potential
Table 2.3-7 presents the composition of GW by 10 physical categories per business facility. It
was revealed that plastic, paper, and food waste were dominant in most business categories. However,the composition was relatively different among categories in level 1. The results showed that food waste
accounted for the largest part at category 3, 6, 7 in comparing to other physical categories. Paper was
dominant at category 2, 4, 5. Rubber & leather was found at the category 1 with 15% because in
category 1, there was a sub-category produce this material.
Table 2- 27. Waste composition and potential
Level
1

N

Plastic

Paper

Food
waste

Rubber

Grass

&

&

leather

Wood

Recyclin
Textile

Metal

Glass

Ceramic

Othergpotential

CompostinOtherg potential

residue

1

24

12%

6%

29%

15%

5%

7%

8%

7%

0%

11%

25%

31%

44%

2

1

7%

48%

22%

0%

2%

21%

0%

0%

0%

0%

48%

18%

34%

3

49

12%

17%

36%

3%

5%

4%

2%

1%

4%

17%

27%

40%

33%

4

4

20%

43%

17%

0%

0%

0%

7%

0%

0%

13%

56%

17%

27%

5

3

21%

58%

22%

0%

0%

0%

0%

0%

0%

0%

27%

22%

52%

6

2

7%

10%

63%

0%

1%

5%

3%

6%

0%

5%

22%

64%

14%

7

4

13%

4%

58%

0%

0%

0%

20%

0%

0%

5%

30%

59%

11%

Although the some business facilities in Hue separated some recyclables and food residue collected51

by informal sectors, some recyclable and compostable portions were discharged as GW. The authors
categorized each component of GW according to the potential shown in Table 2.3-4, and aggregated thedata by the following components: “Recycling potential in GW,” “Composting potential in GW,” and“Other residue in GW”.Table 2.3-7 also showed the recycling and composting potential of 7 categories
at level 1.

2.3.3.3 Estimation of WGA from home-based businessTo draw the total waste flow diagram of solid waste generated from business part in Hue, theauthors estimated the total amount of three components (GW, SR, SFR) from the business facilities in
Hue by each category. Additionally, the authors estimated the total recycling potential, composting
potential, and residual food amount in GW.
Table 2.3-8 presents the estimation of total waste and the breakdown of the waste generated frombusiness facilities of home-based business. The total waste generated from business part in Hue was25,151 kg/day, of which 18,257 kg (73%) was GW, 4,767 kg (2.6%) was SR, and 2128 kg (8%) was
SFR. The low percentage of SFR revealed that pig farmers were not played an important role in MSW
systems in this sector.

52

Table 2- 28. Total estimation of WGA from home-based business
Leve
l2
10
11
13
14
15
16
17
18
20
22
23
24
25
27
29
30

M & Pc foodstuffs
Pd beverages
Weaving
Pd garments
Pd leather andrelatedPc& Pdproducts
wood,bambooproducts
Pd paper and paper
products copying
Printing,
text
Pd chemical
products
Pd rubber andplastic
products
Pdproducts
fromothernon-metal
Pd metal
Pd prefabricatedmetalproducts
Pd electric
equipment
Pd motor vehicles
Pd other means oftransport
Pd beds,
31
wardrobes, tables,
Other M & Pc
32
industries
Pc
and distributing
35
naturalgas, ofice.
Sale, repair
45
automobiles
Wholesale other46(excluding
Retail(excluding
47
categories
in 45)
Telecommunication
61s
Leasing77
equipment, leaseAdministrative
82
practiceand other
Lottery, betting
and92
gambling
Sporting and93
recreational
Repair of95
computers,
Other personal
96
support services
Total (kg)%workerconver
t
1859
83
161
2483
255
975
716
158
198
179
423
189
1158
3
3
41

RESIDU
E
655
6
28
95
74
563
59
10
25
39
19
219
174
0
0
9

Re
potentia
l
407
4
17
59
46
350
37
6
15
24
12
136
108
0
0
5

Food
potentia
l
280
3
12
41
31
241
25
4
11
17
8
94
75
0
0
4

Separate
d re
1573
7
0
0
0
393
54
8
60
113
101
0
76
0
0
13

Separated food
781
199
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Total
3696
218
56
195
151
1547
176
28
110
192
139
449
433
0
0
31

1058

3041

1887

1301

0

0

6229

36
89
1955
498
11848
773
311
231
45
680
599
2300
29299
116%

0
1
134
382
1834
27
7
8
0
61
11
30
7512
30%

0
3
108
308
1479
57
4
4
0
100
29
81
5286
21%

0
0
158
451
2167
26
3
4
0
285
58
161
5459
22%

2
0
519
1
1498
24
1
49
7
79
176
15
4767
19%

0
0
0
0
1148
0
0
0
0
0
0
0
2128
8%

2
3
919
1141
8126
134
14
66
7
524
275
287
25151
100%

14.7
%
0.9%
0.2%
0.8%
0.6%
6.2%
0.7%
0.1%
0.4%
0.8%
0.6%
1.8%
1.7%
0.0%
0.0%
0.1%
24.8
%
0.0%
0.0%
3.7%
4.5%
32.3
%
0.5%
0.1%
0.3%
0.0%
2.1%
1.1%
1.1%

2.3.3.4 Interval estimation and Uncertainty analysis
The 95% CI of the total waste amount from home-based business sector Hue was also estimatedby Monte Carlo simulation (100,000 times) assuming normal distributions based on the means andstandard errors of the WGRs shown in Table. The results showed that the range for a 95% CI was 15.0727.78 tons/day.

53

The authors also examined the sensitivity as a percentage of the contribution from the WGR of
home-based business category to the variance of the total waste amount. Figure presents the results of

Figure 2- 5. Sensitivity
ensitivity analysis from home-based
based business sector

Uncertainty analysisonon the total waste generated at home-based business sector. “Retail
Retail” was identified
as the category with the largest impact on the CI of the total waste amount (5(50.2%), followed by“Producing wooden furniture” (27.1%),%), “Manufacturing“
and processing foodstuffs”” ((9.1%), and “wholesale” (0.9%). To
o improve the reliability of the total estimation, the authors should start by improving thedata reliability at “Retail” by a further survey to increase the sample size and clarify the influencing
factors on WGRs of SFR and GW.
Conclusion
1) This study aimed to provide a detailed description of the solid waste generation and composition
from business part at home-based
based business in Hue, Vietnam. The authors surveyed 245 households
for seven categories at 1st level over seven consecutive days.
2) The WGRs by worker were assessed by business category by three waste categories: general waste(GW), separated recyclables (SR), and separated food residue (SFR).
3) The mean differences of WGRs of the total waste among the school categories were assessed by
ANOVA test.. There were significant differences in WGRs among categories.
4) GW was classified and measured by 10 physical categories and 77 sub-categories.
sub categories. Plastic, paper, andfood waste were the dominant forms of waste in all categories.
5) The total waste generated
erated from business part in Hue was 25,151kg/day,/day, of which 18,257 kg ((73%)
was GW, 4,767 kg (2.6%) was SR, and 2128
2
kg (8%)%) was SFR. GW still contained 21% that could
potentially be recycled and 22% that could potentially be composted in the total waste amount. The
total disposal amount sent to the landfill could be reduced from 18,257 kg7512 kg
546) The 95% CI of the total waste amount was estimated to be 15.07-27.78 tons/day. According to theUncertainty analysis, the WGRs of the “Retail” category were identified as having the largest impact
on the CI of the total waste amount.

References[1] Vietnam’s Prime Minister (2007) Decision 10-2007-QD-TTg of the Prime Minister: The System
of Economic Branches of Vietnam.
[2] Hue Statistical Yearbook (2011) Statistical Yearbook, Hue city’s Statistical Office, Hue, Vietnam(in Vietnamese).
[3] HEPCO (2011) Report on Solid Waste Management of Hue City. Hue Urban Environment andPublic Works State Company (in Vietnamese).
[4] Matsui Y, Do TTT, Nguyen PT (2015) Estimation of Waste Generation and Recycling Potentialfrom Traditional Market: A Case Study in Hue City, Vietnam. Journal of Environmental
Protection, 6, 308–320.

55

2.4

Solid waste generation from traditional markets and supermarket

2.4.1 Introduction
In economically developing countries, the amount of municipal solid waste is approaching thecapacity of existing waste facilities. It is impossible to continue waste disposal that is heavily dependenton landfill. Central and local governments must develop solid waste management (SWM) plans that
include waste reduction and recycling.
Municipal solid waste is a growing problem in Vietnam, which is showing rapid economic
growth year by year. Vietnam produces over 15 million tons of municipal solid waste each year fromvarious sources. More than 80% (12.8 million tons/yr) derives from municipal sources: households,
restaurants, markets, and businesses [1] As the first step in designing integrated waste management
systems, it is indispensable to ascertain detailed and reliable information related to waste generation,
waste composition, and waste streams [2]. However, Vietnam lacks reliable and detailed data related toSWM. The latest report on SWM in Vietnam published in 2011 presented information related to overall
waste generation and physical composition of MSW around Vietnam, but did not clarify details related
to waste generation from different sources or details related to waste composition such as recycling orcomposting potential.
Regarding the municipal solid waste sources, traditional markets are known to be a considerable
source of waste generation in economically developing countries. One earlier study [3] found thatmarkets contributed about 20% of total waste generation in Indonesia. According to the Vietnam Retail
Association, traditional markets still constitute the major channel of the retail sector. About 8,550
traditional markets existed in Vietnam in 2011. However, few studies have specifically examined wastes
from traditional markets. In Lao PDR, Byer et al[4] surveyed one early morning market (EMM), and clarified the waste generation rate by six
business categories: fruits and vegetables, packaged goods, meat/ fish/ eggs, food stalls, noodles andblood, rice, and charcoal. They assessed the physical composition, but reported no potential forrecycling and composting. In Cambodia and Vietnam, two surveys [5][6] have assessed the waste generation rate by the total waste amount divided by the total numberof stalls in target market, but they did not address differences in waste generation rates among businesscategories.
This study was undertaken to present a detailed description of waste generation andcharacteristic of traditional market in Hue city, Vietnam. The authors chose five markets from three56

market classes, and allocated the targets by 17 business categories considering ‘The System of Economic
Branches of Vietnam’. The authors also estimated the total waste amount, the recycling and composting
potentials from all markets in Hue as the basis of rational SWM planning including waste reduction andrecycling.
2.4.2 Methodology
2.4.2.1 Sample selection
Hue city, the latest imperial capital of Vietnam under the Nguyen dynasty, is located in thecentral region of Vietnam [7]. Hue city comprises 27 wards with area of 71.69 km2 and a population of
350,345 people. There are two distinct seasons in Hue city: the dry season comes with the hot southwest
wind for four months during April– August; the rainy season comes with high and unevenly distributed
rainfall during September–March [8].
Regarding solid waste management, the amount of collected waste in Hue city is reported as
Table 2- 29. Outline of five target markets
Name
Market class
Number of cleanings
Number of stalls
Number of vendors
Number of target samples

Tay Loc
1st
4
682
168
91

Vy Da
2nd
2
280
21
66

Xep
2nd
1
365
75
63

Phuoc Vinh
3rd
2
179
34
58

Thong
3rd
1
112
3-5
30

approximately 210 tons/day. The general collection rate in the whole city was about 89%, and 90–95%
in urban areas [9]Hue city has 23 traditional markets with different scales. According to Government Decree No.
2/2003/NĐ-CP [10] on market development and management, 23 markets in Hue city are classified into
three classes in terms of their scale, trade volume, and facility conditions. Among them, 3 markets
belong to the first class, 6 markets belong to second class, and 14 markets belong to the third class.
Markets sell widely diverse items such as food, vegetable, meat, clothes, and household equipment.

57

Table 2- 30.Definition of business category
There are businesses of two types in Hue,defined as follows.

ID

Business category

1

Rice & powder Stall: a shop selling goods at a2

Meat and meat products

designated place with a contract for a3

Chicken & Duckcertain period of time, normally located

4

Eggs

inside a market building

5

Fish & fish products

6

Vegetable

7

Betel and areca

8

Fruits9

Coconut

10

Spice, Grocery, Cakes & candy

11

Food stalls

12

Beverages

Stalls and vendors in markets generate

13

Textiles, apparel, footwear

waste of many kinds such as rubbish from

14

Fresh flowers, ornamental plantscommodities, containers and packaging. Some of15

Daily commodity, incense, porcelain

16

Service (hair cutting, foot repairing, other)17

Recyclable, Second-hand clothes

 Vendor: someone who is selling
goods without contract for a certain periodof time, normally located outside of market
building

them separate recyclable items for recycling
markets and food residues for animal feed in Hue.

For this study, the authors chose Tay Loc market from first class, Vy Da and Xep markets fromsecond class, and Phuoc Vinh and Thong markets from third class as the target considering location andscale. An outline of the five markets is shown in Table 2-29.
Markets have widely diverse business categories. Prime Minister of the Government of Vietnam(VPM, 2007) issued ‘The System of Economic Branches of Vietnam’ [11] and defined the businesscategory in 642 branches. By referring the official definition, the authors defined 17 business categories
for market as shown in Table 2-30. For target selection, the authors allocated target kiosks and vendors
to cover 17 business categories.

2.4.2.2 Outline of survey
The authors conducted three surveys for all target samples: a waste generation survey by actual
measurement, a waste composition survey, and a questionnaire survey. Surveys on Tay Loc, Xep andPhuoc Vinh market were conducted during 3 - 12 September. Surveys on Vy Da and Thong market were58

conducted during 9 - 17 September. The waste generation survey was administered to acquire data on
the amount of waste generation for 10 consecutive days. Of them, the first three days were spent forpractice; the authors used the data for the latter seven consecutive days. The target stalls and vendors
were requested to keep their waste in three categories by their original customs: ‘Recyclables’, ‘Food
residues’ and ‘General waste’,defined as follows.
 Recyclables: items kept for recycling or sale to informal sectors or given to
somewhere/someone by owners.
 Food residues: waste items kept for livestock (e.g. pigs) feeding; generally collected bylivestock breeders.
 General waste: all remaining waste items excluding separated waste items describedabove. This type of waste is collected daily by an environmental company in Hue (HEPCO).
A waste composition survey was also conducted during the survey period. To provide information
related to the recycling and composting potentials, the authors analysed details of the waste compositionof ‘General waste’ for some representative targets. The waste was classified into 10 physical categories
and 77 sub-categories. The classification categories were based on Materials (Plastic, Paper, Kitchen
waste, Rubber & Leather, Grass, Textile, Metal, Glass, Ceramic, and Miscellaneous), Types(Container/Packaging, Product and Other), Recycling potential (recyclable and non-recyclable), andComposting potential (compostable and non-compostable). The recycling potential was defined based on
the practical trading status of recycling market in Hue city. Recyclable items contained plastic, paper,
glass, metal, and textiles that can be bought and sold at a recycling market. The composting potential
was defined based onthe acceptable items of some composting plants. Organic wastes are divisible into
compostable and non-compostable wastes. Compostable items consisted of vegetables, food residue,
grass, leaves, flowers, egg shells, fish bones, fruit, and fruit skins. Non-compostable items consisted of
coconut shells, hard bones of animal, seashells, bamboos, large tree branches, and wood products. In thisstudy, non-recyclable items were all items that could not be recycled or composted. Descriptions ofwaste classification categories are presented in Table 2-3. The authors also administered a questionnaire
survey to assess attributes and the current status of businesses of target stalls and vendors.

2.4.2.3 Analytical procedure
59

The authors calculated key statistics related to waste generation rates by business category. The
authors also assessed the mean difference among market classes using analysis of variance (ANOVA).
The waste composition by percentage (%) was calculated according to the physical category and byrecycling and composting potentials.
By multiplying the waste generation rate by stall/vendors by total number of stall/vendors in Hue,
the authors estimated the total waste generation amount from traditional market in Hue. The authors also
calculated the 95% confidence interval of total waste generation amount from a Monte Carlo simulation(100,000 times) based on the mean and standard error of waste generation rate by business category.
Monte Carlo simulations are used widely to assess error propagation for model parameters [12] The
authors inferred the sensitivity as a percentage of the contribution from each parameter to the variance ofthe final result [14]2.4.3 Results and discussion
2.4.3.1 Waste generation rate of traditional markets(1) Waste generation rate by stall/vendor

60

Table 2- 31. Waste generation rate by stall (g/stall/day)CategoryN

Rice, powder
Meat/meat product
Chicken &duck
Egg
Fish& fish product
Vegetable
Betel & areca
Fruit
Spice & Grocery
Food stall
Beverages
Textiles & ApparelFresh flowers
Daily commodity
ServiceSecond-hand shop

6
13
12
4
32
40
2
31
37
21
10
16
11
18
14
4

General waste

Recyclable

Food residues

Total waste

Mean ±Standard deviation
8±20
13±31
20±40
2664±3264
291±3142513±2445
250±727140±37520±853184±1023
149±301
218±9399±422754±3594
170±271324±1023
43±166
182±5148±27124±152-89±89
268±305102±165445±644248±2694893±5525
1138±597
3865±2921
392±866762±7172829±3023
195±157
3209±4009
271±283
190±114292±491

110±95289±2992766±3194
737±5552761±2486
5143±5657
1278±634
4568±3343
759±1342
3525±3801
3323±2955
238±2423209±4009
453±566
198±116416±409

Table 2- 32. Waste generation rate by vendor (g/vendor/day)CategoryN

Rice, powder_vendor
Egg_vendor
Fish& fish product_vendor
Vegetable_vendor
Betel and areca_vendor
Fruit_vendor
Coconut_vendor
Spice & Grocery_vendor
Food stall_vendor
Textiles & Apparel_vendor
Fresh flowers_vendor

1
2
2
11
2
7
2
3
5
1
2

General waste

Recyclable
Food residues
Mean ±Standard deviation
1628±462
88±21271±123876±1206-

268
412±1973918±3117
4226±4848
1811±1911
91801±1040
291±375493±418231451±84

Total waste268
412±197
1628±462
4006±3039
4226±4848
1882±1867
91801±1040
291±375
1369±86823
1451±84

Table 2- 33. Waste generation rate by floor area
Tay Loc
Category

Area

GW

Re

m2

FR

Total

g/m2/day

Area

GW

Re

m2

FR

Total

g/m2/dayRice&powder

3.2

42±24

8±11

-

49±34

1.69

1

1

-

2

Meat&meat product2

250±247--250±2471.69

272±266--272±266

Chicken &duck

3.6

-

-

178±7

178±7

2.25

40±63

-

2050±1670

2090±1622

Egg

-

-

-

-

-

2.25

338±408167±189-505±218

Fish&fish product3.6

126±115-1209±913

1335±940

1.69

163±164-739±925903±8611042±557

Vegetable

3.6–10.82274±1531

-

49±63

2324±1518

1.69–3.38964±575-78±100Betel & areca

-

-

-

-

-

2.25

693

74

-

767

Fruit

3.6

1248±857--

1248±857

1.69–3.381362±886402±625211±5961975±1288

Spice & Grocery

2–6.4

73±47

80±129-153±159

2.25–4.5

23±12

73±90

-

96±86

Food stall

3.6–7.2

199±207-1085±1030

1283±949

2.25–4.5353±35114±35

1252±2149

1619±2464

Beverages

3.62095±23569±11-

2163±246

2.25–4.5

397±400147±69-544±469

Textiles & Apparel

3.6–12

66±38

-

-

66±38

4.5

13±9

-

-

13±9

Fresh flower

3.6–7.21224±1465--

1224±1465

2.25–7.29444±269--444±269

Daily commodity

2–4

125±12629±64-154±182

1.69–14.58

83±70

4±7

-

88±73

Service

2–3.6

53±22

-

-

53±22

7.29

23

14

-

37

Second-hand shop

3.2

91±15339±48-130±128

-

-

-

-

-

GW, General waste; Re, Recyclable; FR, Food residue

61

Xep

The waste generation rates of traditional market were calculated using business categories. Table
2-31 presented the mean and standard deviation of waste generation rate (g/stall/day) of stall in threemarket classes. Regarding the waste generation rate by business category, ‘Vegetable’, ‘Fruits’,‘Beverage’ and ‘Fresh flowers’ were higher in all wastes, whereas ‘Rice’, ‘Meat’, ‘Service’ and ‘Secondhand shop’ were identified as having lower generation rates. The result resembled that reported from aprevious study by Byer et al[4]. They reported that ‘Fruits and vegetables’ generated the largest amount (6.49 kg/day)because of their high moisture contents.
Regarding fresh items that are easily be perishable after 1–2 days under normal conditions suchas ‘Meat & meat product’, ‘Chicken & duck’, ‘Fish & fish product’, ‘Vegetable’, ‘Fruit’, ‘Fresh flowers’,
the category of ‘Meat & meat product’ generated the smallest amount with 289 (g/stall/day), whereas theother categories generated much higher waste amounts.In the category of ‘Vegetable’ and ‘Fruit’, eventhough the total waste amount was rather large, the amounts of food residues were quite small because
the rotten or leftover vegetables and fruits were normally unsuitable for feeding animals. Conversely, thecategories of ‘Chicken & duck’, ‘Fish & fish product’ generated a large amount of food residues because
these categories had processing services on site. Therefore, normally the internal organs and unnecessary
parts were separated as food residues.
Some dry-food items such as ‘Rice/powder’ and ‘Spice and grocery’ produced smaller amounts
with 110 and 759 (g/stall/day), respectively. Regarding food services, ‘Food stalls’ & ‘Beverage’
respectively produced similar total waste amounts, with 3,525 and 3,323 (g/stall/day). However,‘Beverage’ generated small amounts of food residues, whereas a major amount of wastes in ‘Food stalls’
were food residues. Table 4 also shows that some business categories such as ‘Meat’, ‘Chicken & Duck’,‘Fish’, ‘Vegetable’, and ‘Freshflowers’ did not separate recyclables.
Table 2-32 presents the mean and standard deviation of waste generation rate (g/vendor/day) of
vendors in three market classes. The ‘Coconut_vendor’ category was separated from ‘Fruit_vendor’
because the waste generation rate of ‘Coconut_vendor’ was extremely high, with 91,801 g/day, which
was much higher than that of ‘Fruit_vendor’ with 1,882 g/vendor/day. Comparing the waste generation
rate of a stall with that of a vendor, some categories with higher waste generation rates such as ‘Fish &fish product’, ‘Vegetable_vendor’, ‘Fruit’, ‘Food stall’ and ‘Fresh flowers’, the waste generation rate ofa stall was higher than that of a vendor. That result is explainable that the space and business scale ofvendors were normally smaller than those of stalls; the resultant waste generation rate was lower. The
62

vendors did not separate recyclables in most business categories excluding ‘Fruit_vendor’; the foodresidue amount was small excluding ‘Fish_vendor’ and ‘Food_vendor’ as stalls, probably because they
had insufficient space to keep such wastes.

(2) Waste generation rate by floor area
In the traditional market, the stall areas differ among market classes and business categories. The
authors calculated the waste generation rate by floor area for two markets for which the managers ofmarkets provided the official data on floor area of each stall, Tay Loc as a first class market and Xep as asecond class market. Table 2-33 presents the means and standard deviations of waste generation rate of
17 business categories by floor area (g/m2/day) in two markets.
The floor areas in most business categories in the first class market were larger than those of thesecond class market. For ‘Rice & powder’, ‘Fish & fish product’, ‘Vegetable’, ‘Spice & Grocery’,‘Beverage’, ‘Textile & Apparel’, ‘Fresh Flowers’, ‘Daily commodity’ and ‘Service’ categories, the waste
generation rate of total waste by square meter was higher for the first class market than for the secondclass market. For ‘Meat & meat product’, ‘Fruit’ and ‘Food stall’ categories, the waste generation rateof total waste by square meter was higher for the second class market, but the waste generation rate of
total waste by stall was higher for the first class market. In most of business categories, the stalls in firstclass market discharged more waste than those in the second class. In the first class market, no stalls
existed for ‘Egg’ and ‘Betel & areca’ categories. These items were sold only at vendors in the first classmarket.
Regarding the ‘Chicken &duck’ category, the waste generation rate of total waste by square
meter in the first class market was 178 g/m2/day, which was much lower than that in the second classmarket at 2,090 g/m2/day. By the observation in both markets, the difference noted above can be
explained by the habit of stall owner in keeping internal organs of ‘Chicken & duck’ in each market. In
the first class market, the owners generally separated internal organs and sold them to customers,
whereas the owners in the second class market normally put internal organs into containers as foodresidues for livestock breeders and occasionally sold them to customers upon request.

63

(3) Mean difference in waste generation rate by market class
Regarding comparison among three market classes, the authors assessed the mean difference inwaste generation rates among the three market classes by 17 business categories using ANOVA. The
authors found significant mean differences for ‘Fish’ and ‘Vegetable’.Table 2-34 shows means and SDs
of waste generation rates for ‘Fish’ and ‘Vegetable’ by three market classes and ANOVA results. The
waste generation rate was the highest in the first class, and lower in the second class and the third class.
Table 2- 34. Waste generation rate by market class (g/stall/day)Category

Market1
Fish& fish2
product
3
ANOVA (F value)
1
Vegetable
2
3
ANOVA (F value)

N
7
14
11
9
14
17

General waste
452±412207 ±224
169±137
3.001
11785±7877
2928 ±2757
2863 ±1653
15.972***

Food residues
4352±3288
1428 ±1095
2725±2533
4.077*
340 ±564
199±358245 ±1009
0.098

Total waste
4804±3384
1635 ±1018
2894±2493
4.74*
12125 ±8134
3127±2996
3108 ±1504
15.351****p<0.05; **p<0.01; ***p<0.001

The differences among three markets are explainable by the fact obtained from questionnaire survey that,in the first class market, the area was largest and the operation time was very long, whereas the area andoperation time were smaller in the second and third class markets, which can be expected to influence
the amounts of sales and waste generation.
2.4.3.2 Waste composition of market(1) Physical composition
The physical composition of general waste by business category is shown in Table. The physicalcomposition differs among business categories. The results indicated that food waste accounts for thelargest part with subsequent plastic or grass. The proportions of food waste were high, with 93.2%,
81.7% and 77.2%, respectively, in ‘Vegetable’, ‘Fruit’ and ‘Beverage’. Previous reports have describedthat the major portion of solid waste in market is food waste and other organic matter. A survey
conducted in Danang[6] reported that the organic part accounted for 81.5%. A survey in Thailand also presented that theorganic from market was 85% [14].

64Table 2.4-7. Physical composition by business category (%)
ID
Rice, powder
Meat
Chicken &duck
Egg
Fish
Vegetable
Betel & areca
Fruit
Spice & Grocery
Food stalls
Beverages
Textiles &footwear
Fresh flowers

N
6
15
2
7
30
17
2
14
33
25
8
13
6
11
5
2

Daily commodity
ServiceSecond-hand shop

Plastic
6.8%
48.2%
51.8%
9.4%
32.6%
2.8%
2.2%
1.6%
26.4%
7.4%
8.1%
40%
2.1%
10.4%
30.1%
29.0%

Paper
0.7%
0.9%
9.9%
2.1%
0.2%
0.3%
0.1%
3.5%
14.7%
8.3%
0.1%
15.1%
1.7%
12.7%
2.4%
69.5%

Food
52.6%
44.8%
24.4%
67.8%
45.1%
93.2%
14.7%
81.7%
52.5%
25%
77.2%
20.3%
21.7%
62.3%
31.6%
-

Rubber
0.1%
11.3%
0.1%
1.6%
0.2%
21.2%
0.2%
0.1%
-

Grass
21.5%
2.9%
1.5%
20.5%
4.7%
1.3%
83%
12.7%
2.2%
51.9%
8.6%
74.5%
3.8%
2.4%
-

Textile
0.1%
2.9%
0.7%
2.3%
11.6%
-

Metal
1.0%
0.5%
2.1%
0.1%
0.8%
-

Glass
-

Ceramic
8.0%
-

Other
18.3%
3.3%
0.1%
17.4%
2.4%
0.3%
2%
4.3%
6.1%
0.5%
0.1%
21%
1.4%

The categories of ‘Meat’, ‘Fish’, ‘Textile’ and ‘Spice’ generated plastics with higher percentage,using mainly single-use plastic bags for packaging, whereas ‘Betel & areca’, ‘Fresh flower’ and ‘Food
stalls’ categories generated grass with higher percentages. These results directly reflected the fact thatsome parts of ‘Betel and areca’ and ‘Fresh flower’ such as stems, leaves, and un-needed parts are often
removed according to requests from customers. ‘Food stalls’ generated large amounts of grass because
Vietnamese people have a habit of wrapping some foods in leaves such as banana or lotus leaves. They
discard them after use. Glass was not found in all categories, and ceramic was only found in ‘Daily
commodity’ with 8%. Rubber was found in‘Chicken & duck’ because rubber bands areusually used to tie live chicken or duck legs.
The‘Textiles&

footwear’category

generated some rubber because rubber
pieces were often used to repair shoes.
(2)

Recycling

andcomposting

potential of waste from traditional markets
Table

2-35

presents

waste

composting and recycling potentials fromgeneralbybusiness

CategoryN

Rice/powder
Meat
Chicken &duck
Egg
Fish
Vegetable
Betel & areca
Fruit
Spice & Grocery
Food stalls
Beverages
Textiles &footwear
Fresh flower

6
15
2
7
30
17
2
14
33
25
8
13
6
11
5
3

category

Daily commodity

according to the definition shown in Table

ServiceSecond-hand shop

65

waste

Table 2- 35. Recycling and composting potential ofgeneral waste (%)
Nonrecyclable
18.1%
8.7%
26.1%
3.0%
18.0%
2.8%
1%
3.8%
8.7%
14.4%
6.2%
11.9%
2.7%
14.5%
34.6%
1.4%

Recyclable

Compostable

6.4%
48.9%
46.4%
10.5%
32.3%
2.9%
1.3%
4.1%
37.8%
9.5%
8.2%
63.6%
2.7%
21.4%
24.7%
98.6%

75.5%
42.4%
27.5%
86.6%
49.7%
94.3%
97.7%
92.1%
53.5%
76.1%
85.7%
24.4%
94.5%
64.1%
40.7%
0.0%

3. It is apparent that the composting potential was very high in some categories such as ’Betel & areca’,‘Vegetable’, ‘Fruit’ and ‘Fresh flowers’. The recycling potential was highest in the category of ‘Secondhand shop’ with 98.6%followed by ‘Textiles & apparel’ with 63.6%, ‘Meat’ with 48.9% and ‘Chicken &duck’ with 46.4%.
2.4.3.3 Estimation of total waste generation from traditional market in Hue(1)

Validation of general waste at three markets

To validate waste generation
rates, the authors estimated the 95%
confidence intervals (CIs) of total
amount of general waste for threetarget markets, Tay Loc in the firstclass, Xep in the second class andPhuoc Vinh in the third class, andcompared

the

CIs

with

Table 2- 36. Interval estimation of three markets and actual
amountMarketTay Loc market
Xep market
Phuoc Vinh market

95% CI of generalwaste (kg)1412–2163
286–502252–363

Actual amount
(kg)
1766
473.8
343.5

the

measured waste amounts by actual measurement on site. By multiplying the waste generation rate by thestall/vendor by total number of stall/vendor in target markets, the authors calculated the total amounts ofgeneral wastes for the target markets. The 95% confidence interval for each market was estimated using
Monte Carlo simulation (100,000 times) based on the mean and standard error of the waste generation
rates shown in Tables 2-31 and 2-32. Table 2-36 presents the 95% CIs of general waste (kg/day) and themeasured waste amounts in three markets. The results show that the measured waste amounts were in
the 95% CI range for the three markets.

66

(2)

Total waste generation from traditional markets

Table 2- 37. Waste amounts by business category from 23 markets (kg/day)General waste
Category
Rice&powder
Meat
Chicken &duck
Egg
Fish
Vegetable
Betel &Areca
Fruit
Coconut
Spice &Grocery
Food stalls
Beverages
Textiles & ApparelFresh flowers
Equipment, watch
ServiceSecond-hand shop
Rice&powder_vendor
Egg_vendor
Fish& fish product_vendor
Vegetable_vendor
Betel and areca_vendor
Fruit_vendor
Coconut_vendor
Spice & Grocery_vendor
Food stall_vendor
Textiles & Apparel_vendor
Amount (kg)Amount (tons)
PercentageN

Non recyclable

Recycling
potential

Composting
potential

Recyclable

Food
Residues

Total

220
630
75
18
1,045
951
50
427
5
1,315
265
90
968
64
545
181
61
36
16
79
436
20
224
6
141
31
16

4
15
2
0
48
132
1
63
459
45
29
16
23
6
21
12
0
2
0
0
48
1
15
551
10
0
1
1,502
1.5
8.8%

1
83
4
1
86
135
1
68
195
19
21
120
6
32
9
18
1
1
0
49
1
17
0
7
0
1
872
0.9
5.1%

15
72
2
7
133
4,422
56
1,520
276
154
218
46
194
95
14
0
7
6
0
1,611
83
374
0
53
0
22
9,377
9.4
55.2%

2
5
7
222
196
2
15
42
0
99
2
8
0
0
0
0
0
16
0
0
0
0
616
0.6
3.6%

3
13
200
2,737
238
79
286
730
29
0
0
129
38
0
0
0
124
0
0
4,605
4.6
27.1%

24
182
208
13
3,004
4,928
64
1,951
459
998
934
299
231
205
247
36
25
10
7
129
1,746
85
422
551
193
1
23
16,97217.0
100%

Hue city has 23 traditional markets. Because of a lack of information exists for the floor area by
each business categories in 23 markets, in this study, the author used only the number of stalls bybusiness category as an indicator for estimation the total amount of waste from traditional market in Hue.
By multiplying the waste generation rate by stall/vendor by the total number of stall/vendors in 23
markets, the authors calculated the total amount of general waste, recyclable, food residue, and total
waste by business category. The authors also estimated the potentials of recycling and composting in
general waste separately.
Table 2-37 presents details of waste generation from a traditional market in Hue: non-recyclable,
67

recycling potential, composting potential contained in general waste: recyclable, food residues, and total
waste by weight (kg/day) by business category. The total waste generation amount was 17.0 tons, ofwhich 3.6% of waste was separated at the source as recyclable, 69.2% was general waste, and 27.1%
was food residues. In general waste, the recycling potential accounted for 5.1%, composting potential
accounted for 55.2%, and the remaining waste accounted for only 8.8%. This result indicates that waste
generation in markets has high potential up to 82.3% of total for composting and livestock feeding. The
total disposal amount sent to the landfill site can be reduced from 69.2% to 8.8%.
Regarding the ‘Coconut’ category, the waste generation rate was the highest with 91.8
kg/stall/day and was mainly composed of coconut shell. Therefore, we did not bring it to the laboratory
to examine the composition. The authors assumed that the total generated waste from the ‘Coconut’
category was non-recyclable
Regarding composting potential, ‘Vegetable’ accounted for the largest amount with 4,422 kg/day,followed by ‘Vegetable_vendor’, (1,580 kg/day) and ‘Fruit’ (1,520 kg/day). These results suggest thatthese sources would contribute immensely to promoting waste reduction. Regarding about the recycling
potential, the largest contribution category was ‘Spice & grocery’ with 195 kg/day, followed by‘Vegetable’ (135 kg/day), ‘Textiles & apparel’ (120 kg/day), ’Fish’ (86 kg/day), and ‘Meat’ (83 kg/day).
The results revealed that these categories should be considered heavily for promotion for recycling formarket.
The total estimated composting potential was 9.4 tons, which contributes mostly to the total
general waste in the market. The large quantity of composting potential present in the MSW stream has agreat impact on the production of high-quality compost and offers great potential for resource recovery.
Such recovery is expected to play an important role in reducing total waste generation amount and in
mitigating the negative effects on environmental quality and natural resource conservation.

2.4.3.4 Interval estimation of total waste generation from traditional market in Hue
The 95% confidence interval (CI) of total waste amount from 23 markets was also estimatedusing Monte Carlo simulation (100,000 times) based on the mean and standard error of waste generation
rate shown in Tables 4 and 5. The results showed that the range of 95% CI was 14.9–18.9 tons/day.

68

Figure 2- 6. Sensitivity analysis of total waste generation.

The authors also examined the sensitivity as a percentage of the contribution from the waste
generation rate of each business category to the variance of the total waste amount. Figure 2-6 presents
the result of Uncertainty analysis. ‘Vegetables of first class market’ was identified as the category withthe largest contribution (34.8%) to the variance of the total waste amount, followed by‘Vegetable_vendor’ (13.5%)and‘Fish of first class,’ (10.9%). To improve the reliability of total
estimation, the sample size should be increased. Further investigation must be undertaken to clarify thefactors affecting waste generation rate in these categories.
2.4.4 Waste generation from supermarket
The total waste amount from super market was estimated as 2.5 tons/day

Conclusions
1) This study produced a detailed description of waste generation and composition by 17
business categories in a traditional market in Hue city, Vietnam. In all, 309 stalls/vendors in fivemarkets were surveyed for 10 consecutive days in the dry season.
2) The waste generation rate by stall/vendor was assessed by each business category by
three waste categories: general waste, recyclable, food residues. The waste generation rates of‘Vegetable’, ‘Fruits’, ‘Beverage’ and ‘FreshFlowers’ were higher in all wastes, whereas those of‘Rice’, ‘Meat’, ‘Service’ and ‘Second-hand shop’ were identified as having lower generation rates.
3) Significant mean differences of total waste amount were found in ‘Fish’ and ‘Vegetable’
categories. The waste generation rate was highest in the firstmarket class and lower in the second
market class and third market class.

694) The waste generation rate by floor area was also calculated in two markets in first andsecond classes. In most business categories, the stalls in the first class market discharged more waste
than those in the second class.
5) The general waste was classified into 10 physical categories and 77 sub-categories. Foodwaste accounted for the largest part, followed by plastic and grass. The results from sub-categories
demonstrated the potential for composting and recycling.
6) As the validation of waste generation rate, the authors estimated the 95% confidence
intervals (CIs) of total amount of general waste for three target markets and compared the CIs withthe measured waste amounts by actual measurements conducted on site. The measured waste
amounts were in the 95% CI range for the three markets.
7) The authors also estimated the total generated waste, the recycling and composting
potential for 23 traditional markets in Hue. The total waste generated from market was 17.0 tons/day,of which 4.6 tons (27.1%) was collected by pig farmers for feeding livestock and 0.6 tons (3.6%)
was sold to the recycling market. The composting potential accounted for 55.2% and the recycling
potential accounted for 5.1% of total waste generation from traditional market in Hue. The total
disposal amount sent to the landfill site would be reduced from 69.2% to 8.8% of the total.
8) By the Monte Carlo simulation, the confidence interval of total waste generation fromtraditional market in Hue was estimated. The 95% CI of total waste was 14.9–18.9 tons/day. By
Uncertainty analysis, ‘Vegetable of first class market’ was identified as the category with the largest
contribution to the variance of the total waste amount with subsequent ‘Vegetable_vendor’ and ‘Fishof first class’. To improve the reliability of total estimation, the sample size should be increased andfurther investigation is necessary to clarify the factors affecting waste generation rates in thesecategories.
References[1] World Bank, MoNRE and CIDA (2004). Vietnam Environment Monitor. 65 pp.
[2]Forbes, R.M., Peter, R.W., Marian, F., and Peter, H. (2001). Integrated solid waste
management: a Life Cycle Inventory, Second edition. Blackwell Science, Oxford, United Kingdom.
[3]Meidiana, C. and Thomas Gamse (2010). Development of Waste Management Practices inIndonesia. European Journal of Scientific Research ISSN 1450-216X 40(2), (2010), 199–210.
[4]Byer, P.H., Hoang, C.P., Nguyen, T.T.T., Chopra, S., Maclaren, V., andHaight, M. (2006).
Household, hotel and market waste audits for composting in Vietnam and Laos, Waste Management70Research 24, 465–472, 2006.
[5] JICA and Kokusai Kogyu Co.Ltd. (2003). The Study on Solid Waste Management in the
Municipality of Phnom Penh.
[6] Otoma, S., Hoang, H., Hong, H., Miyazaki, I., and Diaz, R. (2013). A survey on municipal
solid waste and residents’ awareness in Da Nang city, Vietnam. Journal of Material Cycles Waste
Management 15, 187–194.
[7]http://hueimperialcity.com/hue-introduction/[8]Hue Statistical Yearbook, 2012. Statistical Yearbook, Hue city’s Statistical Office,
Hue,Vietnam.
[9] HEPCO, 2011. Report on solid waste management of Hue city. Hue Urban Environment andPublic Works State Company (HEPCO) (in Vietnamese)[10] Government Decree No. 2/2003/NĐ-CP on market development and management[11]Vietnam’s Prime Minister (VPM). Decision 10-2007-QD-TTg of the Prime Minister: The
system of economic branches of Vietnam, dated 23 January 2007, Prime Minister of the Government ofVietnam. Obtained through the Internet:http://quangnamconsulting.wordpress.com/2010/09/29/decisionno-102007qd-ttg-issuing-the-system-of-economic-branches-of-vietnam/[12] Huijbregts, M.A.J., Gilijamse, W., Ragas, A.M.J., and Reijnders, L. (2003). Evaluating
uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a
Dutch one-family dwelling. Environmental Science & Technology 37, 2600–2608.
[13] Sonnemann, G.W., Schuhmacher, M., and Castells, F. (2003). Uncertainty assessment by aMonte Carlo simulation in a life cycle inventory of electricity produced by using a waste incinerator.
Journal of Cleaner Production 11, 279–292.
[14] Ali, G., Nitivattananon, V., Abbas, S., and Sabir, M. (2012). Green waste to biogas:
Renewable energy possibilities for Thailand’s green markets. Renewable and Sustainable Energy
Reviews 16, 5423–5429.

71

CHAPTER 3. SOLID WASTE GENERATION FROM INSTITUTIONAL SECTOR

3.1
Solid waste generation from educational facilities
3.1.1 Introduction
The amount of municipal solid waste (MSW) generated in Vietnam has been increasing in recent
years. The amount of MSW was approximately 19 million tons in 2008 [1] and increased to 23 million
tons in 2014 [2]. The amount of waste is expected to increase to 61.6 million tons by 2020 [3]. The rapid
increase in MSW has posed significant challenges for Vietnamese solid waste management authorities.
In addition, in 2015, the Vietnamese government issued a national strategy to manage waste anddiscarded material (Decree no. 38/2015/NĐ-CP), which indicated that daily-life solid waste must beclassified and stored according to the following three categories: biodegradable organic, reusable andrecycled, and “other” [4]. Determining the recyclable and organic amounts is indispensable forVietnam’s handling of waste in the years to come.
It should also be noted that some categories of waste are collected by informal sectors indeveloping countries; these include recyclables for sale and leftover food waste for farmers to feed
animals [5]. A survey in Hanoi showed that recyclable waste accounted for approximately 20% of MSW[6]. As for food residue, the estimated amount of recycled food residue was 4.1% of the domestic waste
collected in Danang, Vietnam [7]. However, most previous studies in Vietnam mainly focused on MSW
collected by formal sectors [8-11]. To design a comprehensive MSW plan, Vietnamese authorities needto determine the total material flow of MSW, including the waste recovered by informal sectors.
Currently, MSW management in Vietnam has been empirically planned without the use of
reliable data, but it will include such data in the near future. Shortcomings in MSW data include
inconsistencies in definitions and collecting data; lack of unification in reporting data among themunicipalities; and differences in the amount of waste generated, which makes predicting trends
difficult [1]. As some previous studies have mentioned, it is important to understand the amount of waste
generated, the waste composition, and the waste stream as the first step in developing an effective MSW
strategy that includes 3R promotion (reduce, reuse, recycle) [12, 13].
MSW is generated and discharged from various sources: households; commercial sources like
hotels, restaurants, and markets; and institutional sources like educational facilities, medical facilities,
and offices. To promote the 3Rs, it is indispensable for all members of society to separate valuable items
such as recyclables and food residue. In particular, the public sector is responsible for raising awareness72

and promoting waste separation. Educational facilities could assume a role in educating individuals, andtake the lead in 3R promotion.
Previous studies have investigated the generation of waste from educational facilities, but somestudies have focused only on universities [14, 15, 16], and other studies have surveyed waste fromprimary and secondary schools [17, 18]. They did not cover all school categories from nursery school
through university. One study in Vietnam surveyed waste from educational facilities, but the study did
not provide details on waste composition, the potential for recycling and composting, or waste collectedby informal sectors [19].
In order to provide the scientific information that can contribute to the promotion of the 3Rs at
educational facilities, this study aimed to provide a detailed description of the solid waste generation andcomposition from educational facilities in Hue, Vietnam. The authors surveyed six school categories,
ranging from “Day-care center” to “College & University,” including “Private tutoring,” according to“The system of economic branches of Vietnam, 2007” [20], and analyzed differences by category. The
authors conducted a survey on the waste collected by formal sectors, and also measured the amount of
separated recyclables and food residue collected by informal sectors. The waste collected by formal
sectors was classified into 10 physical categories and 77 sub-categories to identify the potential forrecycling and composting the waste. This study also conducted an interval estimation of the total wastein Hue, and aimed to clarify the reliability of collected data and improve future tasks through uncertainty
analysis.
3.1.2 Methodology
3.1.2.1 Sample selection
Hue, the capital city of Thua Thien Hue province, located in the center of Vietnam, was selected
as the study area. Hue is comprised of 27 wards with a total area of 71.7 km2 and a population of342,556 as of 2011 [21]. Hue is well known for its historical monuments, which were deemed World
Cultural Heritage sites on December 11, 1993, by the United Nations Educational, Scientific, andCultural Organization (UNESCO) [22]. Regarding solid waste management, the amount of collected
waste in Hue is reported to be approximately 210 tons/day. The collection rate in the entire city was
about 89%, and 90–95% in urban areas, in 2011 [23].
There are a total of 402 educational facilities in Hue. The authors covered six school categories
according to “The system of economic branches of Vietnam, 2007”: “Day-care center,” “Kindergarten,”“Primary school,” “Secondary school,” “College & University,”and “Private tutoring.” For the sample
73

selection, the authors used the lists of schools by school category. For “Kindergarten,” “Primary school,”“Secondary school,”and “College & University,”the lists provided the number of students at each school.
Thus, the authors sorted the lists by the number of students and chose target schools by systematic
sampling. Regarding “Day-care center” and “Private tutoring,” the lists did not include data on thenumber of students at each school. Therefore, the authors chose target schools from the original lists
without sorting by systematic sampling. A total of 35 targets were selected. The total number of facilities
in Hue, the number of targets, and the description by school category are shown in Table 1.
3.1.2.2 Outline of survey
This survey focused on MSW and did not include construction and demolition waste, medical
waste, or hazardous waste. The procedure for the waste generation survey followed the methodology
presented byMatsui et al. (2014) [24]. The authors conducted three surveys for all target facilities: awaste generation survey by actual measurement, a waste composition survey, and a questionnaire survey.
A waste measurement survey was conducted to acquire data on the generation amount for sevenconsecutive days. Before the survey period, the authors spent three days to prepare and practice withsurveyors and target facilities. The target facilities separated the waste into three categories according totheir usual customs; waste collected by formal sectors (hereinafter referred to as “general waste (GW)”),
recyclables sold to informal sectors (hereinafter referred to as “separated recyclables (SR)”), and foodresidue sold/given to pig farmers (hereinafter referred to as “separated food residue (SFR)”). The
surveyors measured and recorded the amount of waste per category (GW, SR, SFR) daily. The
separation rates for recyclables and food residue were as follows:o Day-care center: 0% for recyclables; 0% for food residueo Kindergarten: 33% for recyclables; 100% for food residueo Primary school: 57% for recyclables; 86% for food residueo Secondary school: 78% for recyclables; 67% for food residueo College & University: 100% for recyclables; 100% for food residueo Private tutoring: 0% for recyclables; 0% for food residue

74

Table 3- 1. Total number of and samples from educational facilities in Hue by school category
Total
numberin HueNumbe
r oftargets

Day-care center

126

6

Kindergarten

49

9Category

Primary school

37

Description [25]Provides nurturing and caring for children from 3
months old to 3 years old.
Accepts children from 3 to 6 years of age. This service
is offered by both public and private sectors.

7

Consists of five grades (Grades 1 to 5), starting with 6year-old children. Children will complete primaryschools at the age of 11.

Secondary school

35

9Includes two levels: lower and upper secondary
education. Lower secondary education consists of four
grades (Grades 6 to 9). Upper secondary education
comprises three grades (Grades 10 to 12).

College &University

112

Includes colleges, universities, and academic research
institutes.

2

Provides learning services to help students preparing
for important examinations or to simply enhance theirknowledge in specific subjects (e.g., English,
mathematics, etc.)

Private tutoring

55

The authors conducted a waste composition survey for GW during the survey period. To acquire
the information relating to recycling and composting potential contained in GW, the authors classified
GW into 10 physical categories and 77 sub-categories. This classification system was based on material(plastic, paper, kitchen waste, rubber & leather, grass, textiles, metal, glass, ceramic, and miscellaneous),
type (container/packaging, product, and other), and potential to recycle and compost. The recycling
potential was based on the practical trading status of the recycling market in Hue. Recyclable items
contained plastic, paper, glass, metal, and textiles that can be bought and sold at a recycling market. The
composting potential was based on the acceptable items as determined by some composting plants in
Vietnam. Descriptions of waste classification categories are included in Table 2-3. The authors asked thepersons in charge of facility management or waste handling to record the attributes and current status oftarget facilities by using the structured questionnaire and through interviews. The questionnaire wasdesigned to obtain detailed information on relevant factors (business-scale indicators) influencing waste
generation, recycling activities, and attitudes toward solid waste management at each facility.

3.1.2.3 Analytical procedure
75

The authors calculated basic statistics relating to waste generation rates (WGRs) by waste
generation amount divided by five indicators: facility, classroom, baby/kid/student (hereinafter referred
to as “Student”), staff members (including lecturers, managers, researchers, and other people who
provide service at educational facilities), and area. The mean differences of WGRs of total waste among
the six school categories were assessed by analysis of variance (ANOVA). The authors also calculated
the coefficients of variations (CVs: standard deviations divided by means) of WGRs, and compared theCVs among five indicators. The CV was used as the indicator of reliability in this study. The authors
chose WGRs with smaller CVs among five indicators for further analyses.
The waste composition of GW by percentage was calculated according to the 10 physicalcategories by school category. In addition, based on 77 sub-categories, the authors assessed the recycling
and composting potential in GW. The total waste generated from educational facilities in Hue wasdetermined through extrapolating by the total number of facility/student in Hue and the waste generation
rate by facility/student. (“Day-care center” and “Private tutoring” were estimated by facility.
“Kindergarten,” “Primary school,” “Secondary school,” and “College & University”wereestimated by
student). The authors also calculated the 95% confidence interval (CI) of the total waste generated by aMonte Carlo simulation (100,000 times) based on the mean and standard error of the WGR by
student/facility per educational category. Monte Carlo simulations are used widely to assess error
propagation for model parameters [26]. The uncertainty of the result is affected by the uncertainty of the
input data [27]. The authors intended to estimate the sensitivity as a percentage of the contribution fromeach parameter to the variance of the final result [28].
3.1.3 Results and discussion
3.1.3.1 Waste generation rates of educational facilities
Table 3-2 presents the means and standard deviations (Mean ± SD) of WGRs of GW, SR, SFR,
and total waste per school category according to the following indicators: daily amount at the facility(kg/facility/day), daily amount by student (g/student/day), daily amount by classroom (g/classroom/day),daily amount by staff member (g/person/day), and daily amount by area (g/m2/day). The authors also
indicated the result of ANOVA on the mean difference among school categories, as seen in Table 3. The
result indicated that there were significant differences in WGRs by all indicators (p<0.001) among
school categories.
Regarding the total WGR by facility, “College and University” generated the largest amount(117.2 kg/facility/day), followed by “Primary school” (51.8 kg/facility/day), “Kindergarten” (39.4
76

kg/facility/day), “Secondary school” (32.7 kg/facility/day), and “Day-care center” (2.1 kg/facility/day).
“Private tutoring” generated the smallest amount (0.8 kg/facility/day). Regarding the total WGR by
student, “Kindergarten” was the category with the highest WGR (141 g/student/day), followed by “Daycare center” (81 g/student/day) and “Primary school” (76 g/student/day).
Table 3- 2. Waste generation rates of educational facilities
GeneralCategoryNDay-care center
Kindergarten
Primary school
Secondary school
College&
UniversityUniversity
Privatetutoring

Separated

Mean

SD

30
43
35
41
9
9

2.1
15.5
26.1
22.9
93.9
0.8

2.3
13.0
22.9
12.7
55.5
0.4

Day-care center
Kindergarten
Primary school
Secondary school
College & University
Private tutoring

30
43
35
41
9
9

81
54
40
23
19
34

Day-care center
Kindergarten
Primary school
Secondary school
College & University
Private tutoring

30
43
35
41
9
9

1,205
1,947
1,533
1,064
1,059
514

Day-care center
Kindergarten
Primary school
Secondary school
College&
UniversityUniversity
Privatetutoring

30
43
35
28
9
9

670
512
681
324
229
610

By facility (kg/facility/day)
0
0
0
0
0.1
0.5
23.8
19.3
0.8
1.2
24.9
22.7
2.8
3.3
7.0
6.6
4.9
2.7
18.5
5.5
0
0
0
0
By student (g/person/day)
40
0
0
0
0
17
0.5
2
87
30
18
1.0
2
35
27
14
2.0
2
5
5
6
1.0
1
5
3
33
0
0
0
0
By classroom (g/classroom/day)
646
0
0
0
0
852
14
62
2,974
1325
619
58
93
1,346
992
447
129
136 309
284
557
61
39
226
86
356
0
0
0
0
By staff member (g/person/day)
469
0
0
0
0
179
4
19
804
290
359
22
35
587
443
152
35
37
48
44
104
15
10
53
24
426
0
0
0
0

11.3
6.8
10.1
4.0
4.8
22.3

By area (g/m2/day)
0
0
0
0.1
0.3
12
0.3
0.5
8.2
0.3
0.4
1.7
0.1
0.1
0.6
0
0
0

Day-care center
Kindergarten
Primary school
Secondary school
College&
UniversityUniversity
Private tutoring***

77

p<0.001

30
38
35
41
9
9

8.7
3.2
4.0
3.4
4.1
19.5

Mean

SD

Separated
Mean

SD

0
6.0
6.3
3.1
0.4
0

Total waste
Mean

SD

CV

ANOVA
(F value)

2.1
39.4
51.8
32.7
117.2
0.8

2.3
31.1
42.8
17.0
48.6
0.4

1.1
0.8
0.8
0.5
0.4
0.5

27.39***

81
141
76
29
25
34

40
33
37
12
2
33

0.5
0.2
0.5
0.4
0.1
1.0

67.67***

1,205
4,934
2,937
1,502
1,346
514

646
1885
1346
625
444
356

0.5
0.4
0.5
0.4
0.3
0.7

53.41***

670
1,321
1,290
407
296
610

469
334
671
167
72
426

0.7
0.3
0.5
0.4
0.2
0.7

25.96***

11.3
18.8
18.5
6.0
5.6
22.3

8.7
8.5
7.5
6.2
4.4
19.5

0.8
0.5
0.4
1.0
0.8
0.9

16.03***

The higher WGRs from “Kindergarten” and “Primary school” facilities were mainly attributable
to the large amount of SFR. The higher WGR at “Day-care center” facilities was considered to be due tothe waste generated in infant care, such as disposal diapers.
The total WGRs were smaller at “Secondary school” (29 g/student/day), “College & University”(25 g/student/day), and “Private tutoring” (34 g/student/day). The total WGRs in these categories wereclose to the reported value (21 g/student/day) in a 2010 study in Cambodia [18]. Table 3 also shows theCVs of WGRs for total waste. The WGRs by student had the smallest CVs in most of the school
categories. Therefore, the authors chose the WGR by student as the representative indicator for waste
generation, which was used for further analyses

3.1.3.2 Mean difference in waste generation rates by food service provided
At “Primary schools,”
some schools provided foodservice at their own canteens,and others did not. The authors
analyzed the mean difference ofthe WGR by food service
provided by ANOVA, as shownin Table 3-3. The WGR for total
waste

differed

Table 3- 3. Waste generation rate of primary school by foodservice
Category
General waste
Separatedrecyclable Food
Separated
residue
Total waste

Primaryschool withfood service
42±18
2±2
41±24
85±33

Primaryschoolwithout
25±11
0
0
25±11

ANOVA
(F value)
3.9
00
15.7****** p<0.001;

significantly

between “Primary school with food service” (85 g/student/day) and “Primary school without foodservice” (25 g/student/day). The difference was considered to be due to the large contribution of SFR at“Primary school with food service” (41 g/student/day). “Primary school without food service”did notseparate recyclables and food residue.

3.1.3.3 Waste composition at educational facilities(1) Physical composition
Table 3-4 presents the composition of GW by 10 physical categories per school category. It was
revealed that plastic, paper, and food waste were dominant in most school categories. The results in thisstudy were in line with the following reported values on waste from schools in Ho Chi Minh City [19]:78food residue ranged from 23.5% to 75.8%, followed by plastic, which ranged from 8.5% to 34.4%, andpaper, which ranged from 1.5% to 27.5%. Food waste accounted for the highest portion at “Kindergarten”
facilities (54.8%), whereas it was lowest at “Secondary school” facilities (5.2%). This difference could
be explained by the fact that the entire “Kindergarten” category provided food service, and someinedible cooking residue is discharged as GW. At the “Secondary school” facilities, the students rarely
ate inside the buildings. Paper accounted for a larger portion at “Day-care center” facilities (47.5%),
followed by “Private tutoring” (35.4%). Paper at “Day-care center” facilities was mainly baby diapers.
This resulted in the lower recycling potential at “Day-care center” facilities. Grass and wood accounted
for a large portion at “Primary school with food service” (15.2%), “Secondary school” (14.5%), and“Primary school without food service” (12.4%).

Table 3- 4. Physical composition of GW at educational facilities (%)
Physical category

Day-carecenterKindergarten

Primaryschoolwith foodservicePrimaryschool without
food service

SecondaryschoolCollege &University

Privatetutoring

Plastic
Paper
Food waste
Rubber & leather
Grass & wood
Textiles
Metal
Glass
Ceramic
Miscellaneous

16.3%
47.5%
22.9%
0.1%
3.0%
1.7%
0.6%
0.0%
5.1%
2.8%

14.4%
18.9%
54.8%
0.2%
3.1%
1.3%
0.3%
0.5%
0.7%
5.7%

25.5%
15.4%
31.4%
0.2%
15.2%
1.4%
0.3%
0.0%
0.5%
10.1%

38.6%
12.3%
20.7%
0.0%
12.4%
4.2%
0.6%
0.0%
0.2%
10.8%

35.1%
28.1%
5.2%
0.1%
14.5%
1.4%
0.6%
0.0%
1.9%
13.1%

18.4%
22.6%
45.6%
2.0%
2.4%
1.2%
0.6%
2.3%
0.0%
4.9%

21.7%
35.4%
19.7%
0.0%
5.1%
0.0%
0.0%
0.0%
0.0%
18.2%

(2) Recycling and composting potential of general waste at educational facilities
Although the educational facilities in Hue separated some recyclables and food residue collectedby informal sectors, some recyclable and compostable portions were discharged as GW. The authors
categorized each component of GW according to the potential shown in Table 2-3, and aggregated thedata by the following components: “Recycling potential in GW,” “Composting potential in GW,” and“Other residue in GW.”

79

Table 3- 5. Recycling and composting potential from general waste by school category (%)
Day-carecenterKindergarten

Primaryschoolwith foodservicePrimaryschoolwithout
food service

SecondaryschoolCollege &University

Privatetutoring

23.7%

57.2%

46.7%

35.5%

42.0%

39.2%

35.1%

35.7%
28.2%
33.7%
43.0%
33.0%
40.6%
14.6%
19.5%
22.0%
25.2%
Contribution of components in recycling potential
Plastic C&P
32.8%
35.7%
43.6%
53.8%
42.3%
Plastic product
1.9%
4.3%
6.1%
3.9%
6.1%
Plastic other
0.0%
0.2%
0.1%
0.4%
0.0%
Paper C&P
47.9%
38.4%
15.4%
15.1%
24.8%
Paper product
6.5%
6.9%
21.1%
13.1%
25.1%
Paper other
0.4%
2.8%
2.4%
2.2%
3.1%
Other material
10.5%
11.7%
11.2%
11.4%
8.6%
Contribution of components in composting potential
Kitchen waste
83.3%
92.6%
64.0%
54.0%
38.7%
Garden waste
8.7%
3.3%
30.6%
30.8%
51.0%
Other item
8.0%
4.1%
5.4%
15.2%
10.3%
C&P: containers and packaging

27.8%
33.0%

53.9%
11.0%

39.7%
2.1%
8.7%
17.1%
19.2%
5.7%
7.6%

24.5%
0.0%
0.0%
35.2%
15.0%
2.4%
23.0%

98.8%
0.2%
0.9%

53.6%
0.7%
45.8%

Physical category

Composting
potential
Recycling potential
Other residue

Table 3-5 presents the potential to compost and recycle and provides a detailed breakdown from
GW by school category. “Composting potential in GW” accounted for a large portion, from 23.7% at“Day-care center” to 57.2% at “Kindergarten.” Table 6 also shows that “Recycling potential in GW”
accounted for a large portion among educational facilities, which ranged from 28.2% at “Kindergarten”
to 53.9% at “Private tutoring.” These results were similar to a 2009 study at the University of Northern
British Columbia, which indicated that the recyclable materials made up more than 37% of waste inmost of the activity areas on campus [29]. “Plastic C&P,” “Paper C&P,” and “Paper product” showed
higher contributions in recycling potential. “Kitchen waste” accounted for the highest portion in all
school categories, and “Garden waste” showed a higher contribution at “Primary school without foodservice,” “Primary school with food service,” and “Secondary school” in composting potential. To
promote recycling and composting at educational facilities, these five items should be considered as
major target items for initial separation.

3.1.3.4 Estimation of total waste generation from the educational sector
To draw the total waste flow diagram of solid waste generated from educational facilities in Hue,80

the authors estimated the total amount of three components (GW, SR, SFR) from the educational
facilities in Hue by each category. Additionally, the authors estimated the total recycling potential,
composting potential, and residual food amount in GW by the contribution of each component presented
in Table 6 multiplied by the total amount of estimated GW amount
Table 3-6 presents the estimation of total waste and the breakdown of the waste generated fromeducational facilities. The total waste generated from educational facilities in Hue was 5.76 tons/day, of
which 3.29 tons (57.2%) was GW, 0.15 tons (2.6%) was SR, and 2.33 tons (40.5%) was SFR. The highpercentage of SFR revealed that pig farmers played an important role in MSW systems, recovering
40.5% of the total waste from educational facilities. Although the educational facilities achieved acertain level of recycling and separating of food residue, the GW still contained 19.3% that could berecycled and 22.4% that could be composted. Based on the comparison of the SR amount and therecycling potential by school category, it was suggested that the separated portion of recyclables wasquite low, especially at “Kindergarten” and “Primary school” facilities; the amount of SR was only 0.01
tons (4.5%) in 0.22 tons of total recyclables and 0.03 tons (12.0%) in 0.25 tons of total recyclables,
respectively. The composting potential was more than one-third the GW amount. Some past studies havementioned that organic waste is typically the heaviest component of a waste stream and has the highest
potential to emit greenhouse gases once buried in a landfill [30]. The result of this study suggested thatthe total disposal amount sent to a landfill could be reduced from 3.29 tons (57.2%) to 0.89 tons (15.5%)
by recovering the recyclables and compostable parts within GW.
Regarding the contribution from each school category, “Kindergarten” was identified as thelargest generation source with 1.99 tons/day, followed by “Primary school with food service” (1.5
tons/day), “Secondary school” (1.04 tons/day), “College & University” (0.83 tons/day), and “Day-care
center” (0.26 tons/day). The “Private tutoring” category generated the smallest amount, with 0.04
tons/day. It is clear that the GW from “Secondary school,” “Primary school with food service,”“Kindergarten,” and “College & University” categories contained larger recycling and composting
potential. To promote recycling and composting at educational facilities, these four school categories
should be considered as major targets to make separation an initial priority.

81

Table 3- 6. Estimation of total waste and the breakdown from educational facilities
General waste
Separatedrecyclables

SeparatedfoodresidueTotalamountCategory

Unit

Total
number in
Hue

Day-care center
Kindergarten

Facility
Student

126
14,114

0.11
0.11

0.09
0.21

0.06
0.44

0
0.01

0
1.22

0.26
1.99

Primary school withfood service

Student

22,057

0.13

0.22

0.34

0.03

0.78

1.50

4,037

0.02

0.04

0.04

0

0

0.10

35,39032,579
55

0.31
0.20
0.01
0.89
15.5%

0.34
0.17
0.02
1.11
19.3%

0.16
0.24
0.02
1.29
22.4%

0.06
0.05
0
0.15
2.6%

0.17
0.16
0
2.33
40.5%

1.04
0.83
0.04
5.76
100%

Primary school without
Student
food service
Secondary schoolStudent
College & University
Student
Private tutoring
Facility
Total (tons/day)%

ResidualamountRecycling
potential

Composting
potential

3.1.3.5 Interval estimation and Uncertainty analysis
The 95% CI of the total waste amount from 402 educational facilities was also estimated byMonte Carlo simulation (100,000 times) assuming normal distributions based on the means and standard

0.0%

10.0%

20.0%

30.0%

40.0%

60.0%

55.0%

SFR at Primary school with food service

16.8%

GW at Primary school with food service
GW at Secondary School

50.0%

8.2%

SFR at Kindergarten

5.9%

GW at College & University

4.8%

GW at Daycare center

3.2%

Figure 3- 1. Sensitivity analysis educational facilities
errors of the WGRs shown in Table 3-2. The results showed that the range for a 95% CI was 4.85–7.71
tons/day.The authors also examined the sensitivity as a percentage of the contribution from the WGR ofeach school category to the variance of the total waste amount. Figure 3-1 presents the results ofUncertainty analysison the total waste generated at schools. “SFA at primary school with food service”
was identified as the category with the largest impact on the CI of the total waste amount (55%),82

followed by “GW at primary school with food service” (16.8%), “GW at the secondary school” (8.2%),and “SFR at kindergarten” (5.9%). To improve the reliability of the total estimation, the authors should
start by improving the data reliability at “Primary school with food service” by a further survey toincrease the sample size and clarify the influencing factors on WGRs of SFR and GW.

Conclusions
1) This study aimed to provide a detailed description of the solid waste generation and composition
from educational facilities in Hue, Vietnam. The authors surveyed 35 educational facilities for six
school categories over seven consecutive days.
2) The WGRs by facility, student, classroom, staff member, and floor area were assessed by school
category by three waste categories: general waste (GW), separated recyclables (SR), andseparated food residue (SFR).
3) The mean differences of WGRs of the total waste among the school categories were assessed by
ANOVA. There were significant differences in WGRs by all indicators among the six school
categories.
4) GW was classified and measured by 10 physical categories and 77 sub-categories. Plastic, paper,and food waste were the dominant forms of waste in most school categories. “Composting
potential in GW” accounted for 23.7%–57.2% and “Recycling potential in GW” accounted for
28.2%–53.9%.
5) The total waste generated from educational facilities in Hue was estimated to be 5.76 tons/day, of
which 3.29 tons (57.2%) was GW, 0.15 tons (2.6%) was SR, and 2.33 tons (40.5%) was SFR. GW
still contained 19.3% that could potentially be recycled and 22.4% that could potentially becomposted in the total waste amount. The total disposal amount sent to the landfill could be
reduced from 3.29 tons (57.2%) to 0.89 tons (15.5%).
6) To promote recycling and composting at educational facilities, the key findings can besummarized as follows:

Target waste items: According to the detailed breakdown of recycling andcomposting potential, plastic containers & packaging, paper containers & packaging, paper
products, kitchen waste, and garden waste showed the highest contributions.


Target school categories: According to the estimated recycling and composting

potential, “Secondary school,” “Primary school with food service,” “Kindergarten,” and83

“College & University” contained the largest potential to recycle and compost within GW.
The abovementioned items and school categories should be considered as the major policy
targets for separation with the highest priority.
7) The 95% CI of the total waste amount was estimated to be 4.85–7.71 tons/day. According to theUncertainty analysis, the WGRs of the “Primary school with food service” category wereidentified as having the largest impact on the CI of the total waste amount.
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86

3.2

Solid waste generation fromhealth-care facilities

3.2.1 Introduction
Vietnam is one of the most densely inhabited country in the world, and its population growth rateis expected to be 1.054%. Approximately 30% of the country’s residents currently live in the cities; its
urbanization annual rate of change was 3%. According to statistics of the Ministry of Natural Resources
and Environment, by 2008 Vietnam produced over 27.87 million tonnes of waste each year from various
sources; of which, hazardous health-care waste accounted for only a small percentage (0.64%). The total
amount (tonnes/year) of hazardous health-carewaste generated is increasing day by day and is estimatedto be 300 thousand tonnes per year by 2050 (JICA, 2010). Therefore, health-care waste management in
Vietnam has been given serious attention at different levels of government, as well as at the community
level.At the national level, health-care waste management is underthe scope of the Ministry of Health,the MONRE, and the Ministryof Construction. At the local level, it is undertaken by theUrban
Environmental Companies, which are directly under theProvincial People’s Committees or the
Department of Transportand Public Works or the Department of Construction or theDepartment of
Natural Resources and Environment (DONRE).The central administrative bodies and respective
departmentsof the provincial, district, and local levels have their own rolesand responsibilities forhealth-care waste management in theirjurisdictions. Other local agencies and organizations forhealthcarewaste management also share responsibilities of management,such as collection at source,transportation and treatmentactivities.As health-care waste is recognized as a hazardous source
forhuman health and environmental quality, the Government of Vietnamhas incorporated this issue into
several development strategiesand environmental protection plans with a view to theboosting
appropriate management and handling of this source.Several existing legal documents (strategies,
development plans,and guidelines) have been issued by the government and relevantcentral ministries
for developing and improving the health-carewaste management system. The development goals andexpectedresults by target year 2015 (Decision 2149/2009/QD-TTg, and DecisionNo.1873/2009/QDBYT) and 2020 (Decision 2149/2009/QDTTg,and Decision 30/2008/QD-TTg) have been introduced.
In 2009, there were 13,450 health-care facilities with more than232,925 beds (GSO, 2010), including1002 general and specialtyhospitals, 682 regional clinics, 43 sanatoriums, 10,979 health-careservice
units in villages and precincts, 710 health-care service unitsin offices and enterprises, and 34 otherfacilities.
In order to provide the scientific information that can to provide a detailed description of the87solid waste generation health-care facilities in Hue, Vietnam. The authors surveyed 32 target samples at
8 different categories according to “The system of economic branches of Vietnam, 2007”, and analyzed
differences by category. The authors conducted a survey on the waste collected by formal sectors, andalso measured the amount of separated recyclables and food residue collected by informal sectors. This
study was not cover the composition survey to avoid some medical waste that remaining in generalwaste. This study also conducted an interval estimation of the total waste in Hue, and aimed to clarify
the reliability of collected data and improve future tasks through uncertainty analysis.

3.2.2 Results and discussion
3.2.2.1 Waste generation rates of health-care facilities
The waste generation rate was discussed on the following 3 business scale indicators; the numberof facility, the number of bed, and the number of staff. Table 3-7 presented the averages and standard
deviations (Ave ± SD) of waste generation rates by 3 different units; kg/facility/day, g/bed/day, andg/staff/day.
Among various categories of healthcare services, the waste generation rate (g/facility/day) of “National
hospital” category was the highest (2767 kg/facility/day), followed by “other hospital” category (46.5kg/facility/day). “Dental_family” was the smalles category for the generation rate by facility with therate of 0.4 kg/facility/day. Among waste types, general waste accounted for the highest part, followed byfood residues, and recyclable waste. (Hazardous healthcare waste was not covered in this study. )Regarding the generation rate by bed (kg/bed/day), “National hospital” category was still having highest
waste generation rate with 1.24 kg/bed/day.

88

Table 3- 7. Waste generation rate from health-care facilities in Hue
GeneralRecyclable
waste
WGR from health-care facilities (kg/facility/day)
Dental_family4
0.4±0.50±0Dental_large
3
1±1.7
0±0
Clinic_family2
1.5±1.50±0Clinic_large
101±2.2
0.2±0.5
Health station3
0.5±0.20±0Hospital TW
12410±031.6±0
Other hospital7
41.9±26.8
0.8±1.7
Orthopedic and rehabilitation2
4.6±1.80±0centers
WGR from health-care facilities (kg/bed/day)
Dental_family20.78±0.48
0±0
Dental_large20.26±0.33
0±0Clinic_family1
0.26±00±0Clinic_large
60.34±0.43
0.01±0.02
Health station
3
0.21±0.110±0
Hospital TW
11.08±00.01±0
Other hospital7
0.68±0.53
0.01±0.04
Orthopedic and rehabilitation1
0.21±00±0centers
WGR from health-care facilities (kg/staff/day)
Dental_family
40.76±0.92
0±0
Dental_large
30.09±0.12
0±0Clinic_family2
1.33±1.76
0.01±0.01
Clinic_large
100.21±0.35
0.01±0.01
Health station3
0.11±0.02
0±0
Hospital TW
11.05±00.01±0
Other hospital7
0.47±0.16
0.01±0.03
Orthopedic and rehabilitation2
0.13±0.03
0±0centers
CategoryN

Foodresidue

Total

0±0
0±0
0±0
0.5±1.6
0±0325.6±03.8±7.1

0.4±0.51±1.7
1.5±1.5
1.7±4.1
0.5±0.2
2767.2±046.5±34.1

0±0

4.6±1.8

0±0
0±0
0±0
0.03±0.080±0
0.15±00.04±0.07

0.78±0.48
0.26±0.33
0.26±00.39±0.43
0.21±0.11
1.24±00.73±0.520±0

0.21±00±0
0±00±0
0.01±0.05
0±0
0.14±00.04±0.07

0.76±0.92
0.09±0.12
1.34±1.77
0.23±0.36
0.11±0.02
1.21±00.52±0.170±0

0.13±0.03

As a reference, the waste generation rate by bed (kg/bed/day) for various levels of healthcare
institutions in Vietnam surveyed by JICA(2010) was shown in Table 3-8.In comparison to this JICA
survey result, the result of this survey wasrather lower, butthat was similar to the resultof Patil and
Shedkar (2006); the waste generation rate of public hospital was reportedas 0.11 – 0.65 kg/bed/day.

89

Table 3- 8. Amount of general healthcare waste from various levels of hospital establishments
Categories

Total general healthcare waste generation (excluding hazardous
healthcare waste)(kg/bed/day)
Province
Central hospital
District hospital
Average
hospital
0.97
0.88
0.73
0.86
1.08
1.27
1
1.12
0.64
0.47
0.45
0.52
0.5
0.41
0.45
0.45
1.01
0.87
0.73
0.87
0.82
0.95
0.73
0.83

Hospital
Intensive care
Medical ward
Pediatrics
SurgicalMaternity
Ophthalmology/
Otolaryngology
Pala-clinicalSource: JICA (2010)

0.66

0.68

0.34

0.56

0.11

0.1

0.08

0.10

3.2.2.2 Total estimation of waste generation from health-care facility
Table 3-9 presents the estimation of total waste and the breakdown of the waste generated fromhealth-care facilities. The total waste generated from health-care facilities in Hue was 4.38 tons/day, of
which 3.94 tons (90%) was GW, 0.06 tons (1%) was SR, and 0.38 tons (9%) was SFR.
Table 3-9. Total estimated amount of waste from healthcare facilities (Unit: tons/day)Categories
Dental_family
Dental_large
Clinic_family
Clinic_large
Health station
Hospital TW
Other hospital
Orthopedic and rehabilitation centers
Total amount
Percentage (%)

Total
in Hue

Unit

66
354
364
1155
101
2239
1012
95

Staff
Staff
Staff
Staff
Bed
Bed
Bed
Bed

Generalwaste

Separatedrecyclable

0.05
0.03
0.48
0.24
0.02
2.41
0.68
0.02
3.94
90%

0
0
0
0.01
0
0.03
0.01
0
0.06
1%

Separated
Food
residue
0
0
0
0.02
0
0.33
0.04
0
0.38
9%

Total
waste
amount
0.05
0.03
0.49
0.26
0.02
2.77
0.74
0.02
4.38
100%

3.2.2.3 Interval estimation and uncertainty analysis
The 95% CI of the total waste amount from health-care facilities was also estimated by Monte
Carlo simulation (100,000 times) assuming normal distributions based on the means and standard errorsof the WGRs. The results showed that the range for a 95% CI was 3.25 – 5.52 tons/day.The authors also
examined the sensitivity as a percentage of the contribution from the WGR of health-care facility to thevariance of the total waste amount.
90

0.0%

20.0%

40.0%

Clinic_family

22.3%
11.5%

Other hospitals

Dental_family

80.0%

60.1%

National hospital

Clinic_large

60.0%

4.6%
0.2%

Figure 3- 2. Sensitivity analysis educational facilities
Figure 3-2 presents the results of Uncertainty analysison the total waste generated at health-carefacilities. “Clinic_family” was identified as the category with the largest impact on the CI of the total
waste amount (60.1%), followed by “National hospital” (22.3%), “Other hospital” (11.5%), and“Clinic_large” (4.6%). To improve the reliability of the total estimation, the authors should start by
improving the data reliability at “Clinic_family” by a further survey to increase the sample size andclarify the influencing factors on WGRs.

91

3.3. Solid waste generation from Offices

The waste generation rate was estimated by the number of staff who was working at the Offices.
Table 3-10 presented the waste generation rate and total estimated amount from the office by 11
categories.
Among 11 categories of office sector, the waste generation rate (g/staff/day) of “Other services”
category generated the largest with the amount of 656 g/staff/day, followed by “Administrative Services
and Assistant Services” with 293 g/staff/day. However, the contribution to total waste amount of“Financial, Banking and Insurance” was largest with 454 kg/day. Food residues weren’t discharged forcollection by informal sectors such as pig farmers.
The total waste generated from Office in Hue was 1410 kg/day, of which 1035 kg/day (73%) was GW, 0
201 (14%) was SR.
Table 3-10 Waste generation rate and total estimated waste amount from Offices

Category

Waste generation rate (g/staff/day)
Total
Separated Total waste
staff Generalwaste
recyclable amount

Total waste amount (kg/day)Genral Separated Total
waste
Recyclable amount

CP_Province/City

140

236

37

272

33

5

38

CP_Ward

822

51

16

67

42

13

55

Departments (City level)

1690

68

9

180

115

16

304

Transport and Warehouse
Information andCommunication
Financial, Banking andInsurance
Professional practice, Scienceand Technology
Administrative Services andAssistant Services
Artuctic, Recreational activities

2726

25

1

26

68

3

70

624

19

45

64

12

28

40

3785

90

30

120

340

114

454

1259

53

10

63

67

13

80

738

293

0

293

216

0

216

170

148

51

200

25

9

34

Real-Estate Business

158

65

1

66

10

0

10

Other services

164

656

0

656

108

0

108

Total

1035

201

%

73%

14%

1410
100%

92

CHAPTER 4 SOLID WASTE GENERATION FROM OTHER SECTOR
4.1

Waste generation from household part at home-based business4.1.1 Waste generation rate
Based on the survey at home-based business that presented in the section 2.3, the waste
generation from household part also be considered. The table 4-1 showed the waste generation rate ofhousehold part by household size. The mean difference among 8 categories was found at General waste
and total amount.
Table 4- 1. Waste generation rate from household part at home-based businessHousehold size

N

General waste

Recyclable

Food residue

Total amount

1 member5450±1973±8

86±193540±283

2 members

6

668±4721±1

154±307823±647

3 members

25

351±22934±17543±104428±277

4 members

52

281±14112±27

93±185386±244

5 members

48

284±2192±7131±223417±298

6 members

39

311±1835±8139±269455±343

7 members

11

248±1126±1255±118309±145

8 members30

217±1285±877±134298±132

5.69***

0.82

0.92

3.52**

ANOVA* p<0.05, ** p<0.01, ***p<0.001
4.1.2 Waste composition and potential
The figure 4-1 showed the waste composition of household part at home-based business. Foodwaste continues to be the largest component with 48%, followed by plastic (14%) and paper (13%). The
figure 4-2 showed the composting and recycling potential from general waste, the composting potential
accounted for 19.4% and the recycling potential accounted for 28.3%. The residue amount was only
22.3%

93

Metal
2%

Glass
5%

TextileGrass & 4%

Other
7%

Nonrecyclable
22.3%

Plastic
14%

Wood
4%

Paper
13%

Rubber &leather
3%

Compostingpotential,
49.4%

Recycling
potential
28.3%

Food waste
48%

Figure 4- 1 . Waste composition ofhousehold_HB

Figure 4- 2. Composting & Recycling Potential ofgeneral waste from household_HB

4.1.3 Total estimation of waste generation amount from household part at HBB
The table 4-2 showed the total estimation of waste generation amount by 5 components. The total generated
amount from household part at household with business was 27.02 tons/day.

Table 4- 2. Total estimation of waste generation amount from household part at HBBHouseholdmemberTotal
population

1 member8061
2 members
9190
3 members
10389
4 members
16124
5 members
10737
6 members
6962
7 members
2355
8 members
2215
Total waste amount(tons)%

ResidualamountGeneral Waste
Recycling
Composting
potential
potential

Separated
Recyclable

Separated
Food
residueTotalamount

0.81
0.81
0.58
0.48
0.48
0.52
0.45
0.39

1.03
1.02
0.73
0.61
0.60
0.65
0.57
0.50

1.79
1.78
1.28
1.07
1.05
1.14
0.99
0.86

0.03
0.01
0.28
0.09
0.02
0.04
0.05
0.04

0.70
1.24
0.34
0.75
1.05
1.12
0.44
0.62

4.36
4.86
3.21
3.01
3.21
3.48
2.49
2.41

4.50

5.72

9.98

0.55

6.27

27.02

16.7%

21.2%

36.9%

2.0%

23.2%

100.0%

4.1.4 Interval estimation and Uncertainty analysis
The 95% CI of the total waste amount from household part in HHB in Hue was also estimated byMonte Carlo simulation (100,000 times) assuming normal distributions based on the means and standard
errors of the WGRs shown in Table 4-1 . The results showed that the range for a 95% CI was 23.2 –94

30.2tons/day.
The authors also examined the sensitivity as a percentage of the contribution from the WGR ofhousehold part at HBB category to the variance of the total waste amount. Figure presents the results of

0.0%

20.0%

40.0%

2 member_household

60.0%

51.9%

1 member_household

32.8%

4 member_household

4.5%

5 member_household

3.6%

3 member_household

3.5%

6 member_household

2.2%

Figure 4- 3. Sensitivity analysis of total waste generation from home-based business sector in
Hue
Uncertainty analysison the total waste generated at household part in HBB. “2 member_household” wasidentified as the category with the largest impact on the CI of the total waste amount (51.9%), followedby “1 member_household” (32.8%), “4 member_househol” (4.5%), and “5 member_household” (3.6%).
To improve the reliability of the total estimation, the authors should start by improving the datareliability at “2 member_household” and “1 member_household” a further survey to increase the sample
size and clarify the influencing factors on WGRs of SFR and GW.

4.2

Waste generation from household without business

The total waste generation from household without business was estimated based on the resultsof a study in Hue in 2010 (Yen et al, 2010) by the household size. The total waste amount generationfrom this part was 78.2 tons/day.
4.3

Waste generation from dormitory and street

The total waste amount in dormitory waste estimated by the total number of students who wasstaying at dormitory in Hue during the survey period, the total amount was 0.3 tons/day. Besides, basedon the report of HEPCO, the street waste accounted for 15% of total collected amount from MSW in
Hue, the total waste amount from street was estimated as 31.1 tons/day

95

CHAPTER 5 TOTAL ESTIMATION OF SOLID WASTE GENERATION AND ITSPOTENTIAL FOR RECOVERY
5.1

Total waste generation from commercial and institutional sectors

The total waste generation (tons/day) from commercial and institutionalsectors (C&I sectors) in Huecity was calculated based on the waste generation rate (kg/business scale indicator/day) and the total
number of each business scale indicator in Hue city, as introduced chapter 2 and 3.
Table 5-1 presented the estimated results of total waste generation amount and the breakdown from C&I
sectors by six waste types such as: General waste (GW), Residue in GW, Recycling potential in GW,
Separated recyclable, Composting potential in GW and Separatedfood residues. The waste generation byamount (tons/day) and percentage (%) by sub-categories of C&I sectors were calculated.
The total waste generation amount from C&I sectors were 106.2 tons/day, of which 63.4 tons (60%) was
GW, 9.8 tons (9%) was SR, and 33 tons (31%) was SFR. The high percentage of SFR revealed that pig
farmers played an important role in MSW systems at C&I sectors. Informal sector had recovering 40%of the total waste from C&I sectors. Although the C&I sectors achieved a certain level of recycling andseparating of food residue, the GW still contained 11% that could be recycled and 27% that could becomposted. Based on the comparison of the separated recyclable amount (9%) and the recycling
potential (11%), it was suggested that the separated portion of recyclables was quite low, especially at“Restaurant”; the amount of recycling potential was three times larger than that of separated recyclable.
The composting potential was more than one-third the GW amount. The result of this study suggested
that the total disposal amount sent to a landfill could be reduced from 63.4 tons to 20.7 tons (19%) by
recovering the recyclables and compostable parts within GW at C&I sectors.
Regarding the contribution from each category at C&I sectors, the results revealed that institutional
sectors accounted only 12% of total amount while commercial sector was dominant with 88% of total
amount. “Beverage &Restaurant” was identified as the largest generation source with 39.8 tons/day(38%), followed by“Home-based business” (24%) and “Traditional market” (16%).

96

Table 5- 1. Total waste generation from commercial and institutional sectors

Category
Commercial sector
Hotel
Beverage&Restaurant
Traditional market
Super market
Home-based businessInstitutional sector
Educational facilities
Hospital
OfficeTotal C&I sector

GW(tons)
54.8
4.0
18.6
11.8
2.1
18.3
8.7
3.3
3.9
1.4
63.4

5.2

Residue in
GW
tons
15.7
2.2
7.9
1.5
0.0
4.1
5.0
0.9
3.9
0.2
20.7

%
15%
2%
7%
1%
0%
4%
5%
1%
4%
0%
19%

Recycling
potential
in GW
ton
%
s
9.2 9%
0.9 1%
3.4 3%
0.9 1%
0.0 0%
4.0 4%
2.0 2%
1.1 1%
0%
0.9 1%
11.
11
3%

Separated
Recyclable
ton
s
8.1
0.5
1.0
0.6
1.2
4.8
1.7
0.1
0.1
1.5
9.8

%
8%
1%
1%
1%
1%
4%
2%
0%
0%
1%
9%

Composting
potential in
GW
tons
%
27.7 26%
0.9
1%
7.3
7%
9.4
9%
0.0
0%
10.2 10%
1.5
1%
1.3
1%
0%
0.2
0%
29.2 27%

Separated
Food
Residues
tons
%
30.2
28%
2.6
2%
20.3
19%
4.6
4%
0.7
1%
2.1
2%
2.7
3%
2.3
2%
0.4
0%
0%
33.0
31%

Total
Waste
tons
93.1
7.1
39.8
17.0
4.0
25.2
13.1
5.7
4.4
3.0
106.2

%
88%
7%
38%
16%
4%
24%
12%
5%
4%
3%
100%

Total waste generation and discharge flow in Hue

By integrating the waste generation amount from the household part at home-based businesscategory, household with business, dormitory and street waste with the C&I sector. The total waste
generation amount and breakdown of total waste generation in Hue was described in the Figure 5-1.
The total waste generated in Hue was 244.2 tons/day, of which 39.2 tons (16.1%) were collected by pig
farmers for feeding livestock and 10.4 tons (4.2%) were sold to the recycling market. The daily amount
was collected by environmental company (HEPCO) in Hue was 194.7 tons. The composting potential
accounted for 41% of total waste generation in Hue. The recycling potential accounted for 13.7%. The
total disposal amount sent to the landfill site would be reduced from 79.7% to 25% of the total waste
amount.

97

Separated
Recyclable
10.4 tons(4.2 %)

Total WG
244.2 tons
(100%)

General waste
194.7 tons(%)

Food residues
39.2 tons(16.1%)

Recyclable
potential
33.4 tons (13.7%)

Recycling market
43.8 tons(17.9%)

Landfill site
61 tons(25%)

Non-recyclable
61 tons (25%)

Composting
potential
100.1 tons (41%)

Composting/Feeding139.3
tons(57.1%)

Figure 5- 1. Waste flow of total MSW in Hue city

5.3

Interval estimation and uncertainty analysis

Table 5- 2. Interval estimation of total
WGA in Hue in 2012

The 95% confidence interval of total waste

Categories

95% CI of waste
in 2012 (tons)

discharge amount by a Monte Carlo Simulation based on

Hotel

6.51-8.23

the means and standard errors of waste generation rates.

Restaurant

36.01-43.64

Table presented the 95% confidence interval (95% CI) of

Home-based business15.07-27.78

waste generation amount (tons/day) in Hue city excluding

Traditional market

14.9-18.9

street waste. The results showed that the range of 95% CI

Educational facilities

4.85-7.71

was 189 – 214 tons/day.

Hospital

3.25-5.52

To estimate the impact of each waste generation rate

Office0.95-1.85
23.2-30.2

analysis for sub-categories of commercial, institutional,

Household withbusiness_HH
Household withoutbusinessand other sectors was conducted. Figure 5-2 and Figure 5-

Total waste amountaffectingthe CI of total waste amount, a uncertainty

3 presented the results of sensitive analysis of total MSW
in Hue city and from C&I sectors, respectively.

98

69.94-87.06
189-214*This table did not include the street waste

Figure 5- 2. Uncertainty analysis from MSW in Hue
The uncertainty analysis results of total waste generation from MSW in Hue showed that “HH withoutbusiness_1 member”was identified as the category with the largest contribution (26%)%) to the variance ofthe total waste amount,, followed by ‘Home-based business_retail’ (13.6%)%) and ““Small restaurant”(10.9%). To improve the reliability of total estimation, the sample size should be increased
increased.Further
investigation must be undertaken to clarify the factors affecting waste generation rate in these categories.
5.4

Recyclinging and composting potential
Figure 5-4 and 5-5 presented the waste composting
composand recycling potential of total waste

generation in Hue city; the potential for recycling or composting were defined based on the practicaltrading status of recycling market in Hue city. Regarding the recycling potential, “Container andpackaging” (C&P) was dominant in comparing to other categories. Of which, plasti
plastic_C&P comprises
44%, paper_C&P accounted for 21%, follows by paper_product (12%), metal (8%).
As for composting potential, the component of this part contains mostly food waste with 94%,
followed by garden waste and “Grass
Grass and wood_C&P”
wood_C&P

99

Figure 5- 3. Contribution of component inrecycling potential
5.1

Figure 5- 4. Contribution of component incomposting potential

Financial potential
Based on the data accumulated from recycling market that conducted by Hue University ofscience, the author estimated the total financial aspect from the recycling potential that remaining in GW.
Table 5-3
3 clearly depicts how waste amount, after separating recycling and food residue for informal
sectors can generate considerable profits of approximately 104.9 million vnd/day (about 5,000 us
dollars/day). The revenue from eachach category also showed in thatth table.

100

Table 5- 3. Financial potential in general waste
Code
101
102
103
104
105
106
107
108
109
110
201
202
203
204
205
206
207
208
209
210
211
212
701
702
703
704
705
706
709
802
803

101

Categories
PET bottle
Other plastic bottle
Tray
Tube
Other shape of containers
Shopping plastic bags
Other plastic packaging
Other containers and packaging
Plastic product
Other plastics
CartonContainers
Cardboard
Packaging
Other containers and packaging
Newspapers/magazines /Advertising
Books
Notebooks
Photocopy paper/OA paper
Disposal paper products
Other paper product
Other Paper
Containers
Other containers and packaging
Products and others
Steel_Containers
Steel_Other containers and packaging
Steel_Products and others
Other metals
Disposal bottle
Other containers
Total amountPrice(vnd/kg)

Total
amount
(kg)

7733
7672
5166
5166
5166
5040
4433
5166
5166
4433
2613
2613
2613
2620
2620
2793
3560
3560
3560
3560
2620
2620
23567
23567
23567
6410
6410
6410
3650
1537
1537

700
500
700
300
1300
4800
1600
600
700
600
900
2400
1000
500
400
500
25
500
300
1100
300
700
200
100
75
500
500
500
200
300
100

Total
revenue(mill.vnd)

%

5.5
5.2%
4.2
4.0%
3.7
3.5%
1.8
1.7%
6.8
6.4%
24.1
23.0%
6.9
6.6%
3.1
3.0%
3.7
3.5%
2.6
2.5%
2.2
2.1%
6.2
5.9%
2.5
2.4%
1.3
1.2%
1.1
1.1%
1.5
1.4%
0.1
0.1%
1.7
1.6%
1.2
1.2%
4.1
3.9%
0.7
0.7%
1.8
1.7%
5.4
5.1%
2.4
2.3%
0.3
0.3%
2.9
2.8%
3.1
2.9%
2.9
2.8%
0.6
0.5%
0.5
0.5%
0.2
0.2%
104.9
100%
1$ ~ 20, 940 vnd in June 04,2012

CHAPTER 6 CONCLUSION AND RECOMMENDATION
6.1
Summary of key points
This dissertation consists of 6 sections, the contents of each section are shown as follows:
Section 1 introduces the research background, overview of solid waste management in Vietnam, theresearch area, and the scope as well as objectives of the study. The outline of whole study was alsopresented in this section.Section 2 and section 3 presents the detailed description of waste generationand characteristics from commercial and institutional sector in Hue city, respectively. Section 2 includes
4 sub-sections on hotels, restaurant and beverage sector, homebased business, traditional market andsupermarket. Section 3 includes 3 sub-categories such as educational facilities, health-care facilities andoffices. In each section, the literature review and methodology on sample selection, questionnaire,
survey design and analytical procedure for study was described in detail.Regarding the results anddiscussion part, the waste generation rate and composition were discussed on differences categories by
various business scale indicators. For example, in hotel sector, 6 categories such as: Guest-house, 1-star
hotel, 2-star hotel, 3-star hotel, 4-star hotel and 5-star hotel were considered and the waste generation
rate by number of bed, number of room, number of staff and floor area was also discussed. Besides, themean difference among categories was also checked. Then the model on waste generation rate by
number of room, number of staying guest, number of event guest was developed. Moreover, thepotential for composting (food waste and garden waste), recycling (plastic and paper), reducing waste(food residues) and financial potential were identified. The interrelationships between the waste
generation and business scale indicators were explored. Then the total waste generation and its potential
on recycling and composting from each sector was shown and discussed. The 95% confidence interval(CI) and uncertainty analysis of total waste amount from each sector was also estimated.
In section 4, the waste generation and characteristic from other sector were presented. The waste
generation from dormitory, household with business and household without business by house size also
described. Besides, the author also estimated the total waste generation amount and confidential intervalof total waste amount for these sectors.In the section 5, the total image of municipal solid waste
generation in Hue city was shown. The contribution of each sector to total waste generation amount in
Hue was also discussed. Additionally, the total recycling and composting potential, and residual foodamount in general waste in Hue was also highlighted. The 95% CI of the total waste amount frommunicipal solid waste in Hue was also estimated by Monte Carlo simulation (100,000 times) assuming
normal distributions based on the means and standard errors of the WGRs. The sensitivity as a102

percentage of the contribution from the WGR of each category to the variance of the total waste amount
was also examined. This section also showed the financial aspect that can recover from general waste
amount in Hue.Finally, section 6 summarizes the main conclusions of the dissertation and shows thereasonable suggestions for improving and managing municipal solid waste in Vietnamese cities.
Additionally, recommendations for future research and the possible development are represented.
The main achievements were shown as follows:(1) The authors also estimated the total generated waste, the recycling and composting potential for 269
hotels in Hue. The total waste generation amount was 7.1 tons/day. In general waste, the recycling
potential accounted for 13.1%, composting potential accounted for 30.5 %, and the remaining waste
accounted for only 12.2%. The total disposal amount sent to the landfill site can be reduced from
55.8% to 12.2%.The results indicated that the total waste amount from hotel was mostly affected by
the higher class hotel. In which, the waste generation by check-in guest had high influence (44.5%)
on the total waste, followed by waste generation from room part in higher class (24.5%). The lower
class had less impact to the total WGA of hotel.

(2) The total waste generation amount from food & beverage service sectors in Hue city, which was
39.85 ton/day. For general waste, the recycling potential, and compostable potential accounted for3.44 ton/day (8.6%) and 7.86 ton/day (19.7%), respectively. The total amount of disposal wastedelivered to landfill site can be reduced from 18.60 ton/day (46.7%) to 7.29 ton/day (18.3 %).The
95% confidential interval estimation of total waste generated from food and beverage service sectors
in Hue City was 36.01 - 43.64 ton/day. The 95% CI of recycling potential and composting potential
amount were 3.08 - 3.81 ton/day and 6.82 to 8.91 ton/day, respectively(3) The total waste generated from Home-basebusinessin Hue was 25,151kg/day, of which 18,257 kg(73%) was GW, 4,767 kg (2.6%) was SR, and 2128 kg (8%) was SFR. GW still contained 21% that
could potentially be recycled and 22% that could potentially be composted in the total waste
amount. The total disposal amount sent to the landfill could be reduced from 18,257 kg7512 kg.
The 95% CI of the total waste amount was estimated to be 15.07-27.78 tons/day. According to theUncertainty analysis, the WGRs of the “Retail” category were identified as having the largest
impact on the CI of the total waste amount.

103

(4) The total generated waste, the recycling and composting potential for 23 traditional markets in Hue.
The total waste generated from market was 17.0 tons/day, of which 4.6 tons (27.1%) was collectedby pig farmers for feeding livestock and 0.6 tons (3.6%) was sold to the recycling market. The
composting potential accounted for 55.2% and the recycling potential accounted for 5.1% of total
waste generation from traditional market in Hue. The total disposal amount sent to the landfill sitewould be reduced from 69.2% to 8.8% of the total. By Uncertainty analysis, ‘Vegetable of first classmarket’ was identified as the category with the largest contribution to the variance of the total waste
amount with subsequent ‘Vegetable_vendor’ and ‘Fish of first class’.

(5) The total waste generated from educational facilities in Hue was estimated to be 5.76 tons/day, of
which 3.29 tons (57.2%) was GW, 0.15 tons (2.6%) was SR, and 2.33 tons (40.5%) was SFR. GW
still contained 19.3% that could potentially be recycled and 22.4% that could potentially becomposted in the total waste amount. The total disposal amount sent to the landfill could be reduced
from 3.29 tons (57.2%) to 0.89 tons (15.5%).To promote recycling and composting at educational
facilities, the key findings can be summarized as follows:

Target waste items: According to the detailed breakdown of recycling and composting potential,
plastic containers & packaging, paper containers & packaging, paper products, kitchen waste,
and garden waste showed the highest contributions.



Target school categories: According to the estimated recycling and composting potential,“Secondary school,” “Primary school with food service,” “Kindergarten,” and “College &University” contained the largest potential to recycle and compost within GW.

The abovementioned items and school categories should be considered as the major policy targets
for separation with the highest priority.
(6) The total waste generated from healthcare facilities was 4.4 tons/day, of which the general waste
accounted for 90% and the amount recovery by informal sector was 10%. The total waste amountfrom the Offices, dormitory and street was 3 tons/day, 0.3 tons/day and 31.1 tons/day, respectively.

(7) The total waste generation amount from C&I sectors were 106.2 tons/day, accounted for 43.5% of
total MSW in Hue. The total general waste amount from C&I sectors accounted for 32.5% of total
general waste in Hue.
104

(8) The total waste generated in Hue was 244.2 tons/day, of which 39.2 tons (16.1%) were collected by
pig farmers for feeding livestock and 10.4 tons (4.2%) were sold to the recycling market. The daily
amount was collected by environmental company (HEPCO) in Hue was 194.7 tons. The
composting potential accounted for 41% of total waste generation in Hue. The recycling potential
accounted for 13.7%. The total disposal amount sent to the landfill site would be reduced from
79.7% to 25% of the total waste amount. The Uncertainty analysisresults of total waste generationfrom MSW in Hue showed that “HH without business_1 member”was identified as the category
with the largest contribution (26%) to the variance of the total waste amount, followed by ‘Homebased business_retail’ (13.6%) and “Small restaurant” (10.9%).

(9) The recycling potential in general waste can generate considerable profits of approximately 104.9
million vnd/day (about 5,000 us dollars/day). According to Decree 122/2015/ND-CP regulating on
the new region-based minimum wages, the above-mentioned revenue amount can pay for the 600
workers with the minimum wage is 3,500,000 vnd/month.

6.2. Recommendations
1) To promote recycling and composting at MSW in Hue city, the key findings can be summarized
as follows:

Target waste items: According to the detailed breakdown of recycling and composting
potential, plastic containers & packaging, paper containers & packaging, paper products,
kitchen waste, and garden waste showed the highest contributions.



Target generation sources: According to the estimated recycling and composting potential,“Household without business” “Home-based business” “Restaurant” and “Traditional market”
contained the largest potential to recycle and compost within general waste

The abovementioned items and generation source categories should be considered as the major
policy targets for separation with the highest priority.

2) Based on the Uncertainty analysis, to improve the reliability of the total estimation, the author
should start by improving the data reliability at “HH without business_1 member”, ‘Home-based105

business_retail’ (13.6%), “Small restaurant”. To improve the reliability of total estimation, thesample size should be increased.Further investigation must be undertaken to clarify the factors
affecting waste generation rate in these categories.

3) From the results of potential on recycling and composting and financial aspect, there is a huge
potential for considerable improvement.

4) Waste pickers mayneed to be trained to be more organized and to have wider networksin order tokeep the cost of operations to a minimum, and purchaserecyclables from businesses, academic
institutions, as wellas individual households.

5) In addition to what government agencies and businesses cando, there is a lot which can be doneby the teachers/academics inmodifying the behavior of our youth. Many schools andinstitutionsare adopting green initiatives, but it should be made compulsoryall the schools, as itleads to addiction/behavioral change. Theycan also be a role model in segregating waste to bringin thebehavioral change in the society. Sustainability and environmentalmanagement courses
may be incorporated into curriculumthroughout different levels of school systems including
highereducation.

106

APPENDICES
Appendix 1. Questionnaire survey on educational facilities
Appendix 2. Questionnaire survey on market

107

108

Code of questionnaire

Appendix 1Questionnaire for Educational facilities
Responder’s name:Name of Facility:
Address:
A.

Interviewer’s nameTel:

Category:

GENERAL INFORMATION(Please answer all following information)

No.
1
2
3

4

Question
Number of total classes in the facility
Number of total classrooms
Number of total teachers, staff(1) Fixed staff(2) Temporary staff
Teaching time

Answer………………. Classes………………. Classrooms………………. People………………. People………………. People
From …………. To …………..
From …………. To …………..

[Please ask manager about the yearly schedule including holiday and vacation]

5

Area

Building area: ………………...
Floor area: …………………….
Garden area: …………………..
Total area: …………………….

6

Other business or facilities□ Canteen Business time: ………. to ……..
Food
service:□Breakfast□ Lunch□ Dinners

What kind of food: ………………...
………………………………………..
………………………………………
Number of tables:
Number of chairs:
Number of staff:
Full time:- Part time:Area:m2
Where they prepare/cook food:Net-sales: ………………………
□ Photocopy shop Business time: ………. to ……..
Area:m2
Number of machines:
Number of workers:□ Dormitory (including dormitories for student and staff) Student- Number of rooms:- Number of residents:
Staff- Number of rooms:- Number of residents:□ Others, ………………….

7

109

Waste collection fee_____________VND/monthB. What are the problems, difficulties, limitations which your facilities have met for waste management?

1

Activity/process

Discription for problems, difficulties, limitation

Waste storage(1) …………………………………………………
(2) …………………………………………………

2

Waste discharge(1) …………………………………………………
(2) …………………………………………………

3

Recycling- Waste pickers- Junk-buyers- Junk-shops- Pig farmers(1) …………………………………………………
(2) …………………………………………………

4

Waste collection service(1)
by HEPCO

5

Collection fee by HEPCO…………………………………………………
(2) …………………………………………………
(1) …………………………………………………
(2) …………………………………………………

6

Others(1) …………………………………………………
(2) …………………………………………………

C.

WASTE STORAGE, DISCHARGE AND RECYCLING[Forstaff in charge of waste]

D. Does your facility manage to separate the following waste categories for storage?
“Manage to separate” means you clearly separate the category from general waste, store in designatedplace/equipments/containers, sell/give to specified collectors such as junk buyers/pig farmers, and manage therevenue as your facility’s income if you get some.

□ Food waste□ Recyclable□ Garden waste□ Other()

E. How and where do you keep the abovementioned categories?
Waste category
General waste

Food/kitchen waste

Recyclable waste

Garden waste

110

Material & shape
(1)
(2)
(3)
(1)
(2)
(3)
(1)
(2)
(3)
(1)

Type of container
Volume (L)No. containers

Storage place

(2)
(3)
(1)
(2)
(3)

Other

F. How many times and when do you usually discharge the abovementioned categories?
Waste category
General waste

How many
times
/ day

Food/kitchen waste/ day

Recyclable waste/

Garden waste

/

Other

/

1st time

When
2nd time

Who collect3rd time
HEPCO

G. How do you discharge the abovementioned categories?

General waste

How do you discharge?
When do you transport waste to
What kind of container do you transfer waste
storage place
to collection vehicles or use for collectors□ Before collection time,□ Storage container
Time: …….:………
□ Plastic bag□ During collection time
□ Other, ………………………………………………..

Food/kitchen waste□ Before collection time

Waste category□ During collection time

□ Storage container□ Plastic bag□ Other, ……………………………………………..
□ Storage container□ Plastic bag□ Other, ………………………………………………..
□ Storage container□ Plastic bag□ Other, ………………………………………………..

□ Before collection time
Time: …….:………
□ During collection time

□ Storage container□ Plastic bag□ Other, ………………………………………………..

Time: …….:………
□ During collection time
Recyclable waste□ Before collection time
Time: …….:………
□ During collection time

Garden waste□ Before collection time
Time: …….:………

Other

H. Does your facility burn waste ?

□ Yes

□ No

If answer is YES, what kind of waste does you burn?
a.

All wastes as general waste (mixed waste component)b. Selection of some special components, Please describe which waste categories does you burn? And how muchdo you burn?
Burned category
1
2

111

How much of each category does you burn?
Kg/time%c.

How many times do you burn waste per day or per week ?
(1) …… per day(2) …… per week (3) …… per month

2. Medical hazardous waste: (1) …… per day(2) …… per week (3) …… per month

1. General waste:

I.

What do you discharge with your recyclables?
 Please identify (by or ) the recyclable
 Please identify the discharge methods (Discharge/keep for selling or reuse again/others) of each waste
recyclable

Compon
ent

Items

1
2.
3.
4.
5.
6.

Paper

Newspaper
Cardboard/Boxes
Books, notebook
Bags/packaging
Wastepaper
Others, …………………

1.
2.
3.

Glass

Bottles/ containers
Products
Others, …………………

Plastic

BottlesContainers/foam box
Bags/packages
Products
Others, …………………

Metal

Cans
Containers
Electric applianceHome utensil
Others, ………………….

No.

1.
2.
3.
4.
5.
1.
2.
3.
4.
5.

J.

Discharge methods of waste recyclables ()Discharge
Keep for
Keep for selling
Othersinto generalreusing(description)
Keep forPrice
waste ()againselling () (VND/kg)[For recyclables kept for selling in Q6] Do you get benefit from recyclables? How much per time ormonth?

□ YES
If the answer is YES, ask the following question:
Benefit Facility 1: ……………………….

Facility 2: ……………………….
Facility 3: ……………………….
Others ……………………………………………………
112

□ NO
……………VND/ …………….
……………VND/ …………….
……………VND/ …………….

[Please ask manager/staff for getting information about sale record/receipt of recyclables]

K. How many categories do you have for food waste?
□ Only one category (mixing solid food and soup together)□ More than one sub-category,
Description: ………………………………………………………………………………………..
L.

Do you get some revenue from food waste? If so, how much do you get per time or month?

□ Yes
□ No

revenue
Reason……………VND/ …………….
………………………………………………………………
…...

M. What are the problems, difficulties, limitations which your facilities have met for waste
management?
Activity/process

Discription for problems, difficulties, limitation

1

Waste storage…………………………………………………
…………………………………………………

2

Waste discharge…………………………………………………
…………………………………………………

3

Recycling- Waste pickers- Junk-buyers- Junk-shops- Pig farmers…………………………………………………
…………………………………………………

4

Waste collection service
by HEPCO…………………………………………………

5

Collection fee by HEPCO…………………………………………………
…………………………………………………

6

Others(3) …………………………………………………
(4) …………………………………………………
(5) …………………………………………………

113

Code of questionnaire

Appendix 2Questionnaire for Kiosk in Market
Responder’s name:Name of Kiosk:
Address:N.

Interviewer’s nameTel:

Category:

GENERAL INFORMATION(Please answer all following information)

No.
1

Question
Number of total staff /workers

Answer………………. People(in the case, there are more than 02 staff, please check carefully theworking allocation among them: parallel working or one-by-one
working in a day).

(3) Fixed staff(4) Temporary staff
Opening hours- In the case, kiosk sell at only one location

2

-

3

………………. People………………. People
From …………. to …………..
From …………. to …………..

In the case, kiosk sell at more than one locations
Location 1: ………………………………
Location 2: ………………………………

Total area
- In the case, kiosk sell at only one location

4

In the case, kiosk sell at more than one locations
Location 1: ………………………………
Location 2: ………………………………
Where do the kiosk’s workers take breakfast/lunch?

5
6
7
8

Peak/Bottom days in a week
Peak/Bottom days in a month
Peak/Bottom months in a year
Waste collection fee

Time: …………. to …………
Time: …………. to …………
Kiosk area:
Storage area:

m2
m2

Kiosk area:
Kiosk area:

m2
m2

-

□ Buy food from other shops and eat attheir kiosks□ Bring food from their own house andeat at their kiosks□ Eat the products/food which they sell
□ Come back and take at their own house□ Others, ……………………………..

Peak:
Peak:
Peak:

Bottom:Bottom:Bottom:_____________VND/month

9. What items/sub-categories do the target kiosk sell? Please indicate by cycle the relevant ID
Category
1
2

114

ID

Sub-category

Mobile retail or in-market-retail of food1Gạo (Rice)

Mobile retail or in-market-retail of meat and meat products2Thịt (Meat)3

Gà Vịt (Chicken & Duck)34Trứng (Eggs)

Mobile retail or in-market-retail of fish and fish products

5

Cá (fish)

Mobile retail or in-market-retail of vegetables

6

Rau (Vegetable)

7Trầu cau (Betel and areca)

8

Trái cây (Fruits)9Dừa (Coconut)

10

Gia vị (Spice)11Tạp hóa (Grocery)12

Bánh kẹo

13

Công nghệ phẩm (CNP)

14

Bún (Fresh rice noodle)

4
Mobile retail or in-market-retail of fruits
5
Mobile retail or in-market-retail of other foodstuffs

6

7

Mobile retail or in-market-retail of food stalls

15

Hàng ăn (Food stalls)

8

Mobile retail or in-market-retail of beverages

16

Quán nước (Beverages)

9

Mobile retail or in-market-retail of cigarettes, rustic tobacco

17Thuốc lá (cigarettes, rustic tobacco)

Mobile retail or in-market-retail of textiles, apparel, footwear18

Vải (Textiles)

19Quần áo (apparel)20Giầy dép (Footwear)21

Sửa giầy dép (Footwear repairing)22Quần áo cũ (Second hand cloths)

10

11

Mobile retail or in-market-retail of fresh flowers, ornamental
plantsRemainders of other commodities in market-retail1223

Hoa (Flower)

24

Dung cụ gia đình (Household's equipment)

25

Nhang đèn (Incense & candle)

26

Nón là (Palm-leaf conical hat)

27

Sành sứ (Porcelain)

28

Vàng (Gold)29Thuốc (Medicine)30Điện thoại (Mobile phone)31Đồng hồ (Watch)32

Cắt tóc (Hair cutting)

10. For small restaurant (kiosk sell food/beverage) inside the market, Asking following questions:- Business time: ………. to ……..
- Food service:□ Breakfast□ Lunch□ Dinners- What kind of food: ………………, …………………, …………………, …………………,- Number of tables:………… units; Number of chairs: ………… units- Number of staff: Full time: ………… people; Part time: ………… people- Area: ………… m2- Where they prepare/cook food:…………………………………………………………………………………

115

O.

WASTE STORAGE, DISCHARGE AND RECYCLING

1. Does your kiosk manage to separate the following waste categories for storage?
“Manage to separate” means you clearly separate the category from general waste, store in designatedplace/equipments/containers, sell/give to specified collectors such as junk buyers/pig farmers, and manage
the revenue as your kiosk’s income if you get some.

□ Food waste□ Other□ Recyclable()

2. How and where do you keep the abovementioned categories?
Type of container
Volume (L)Material & shape

Waste category
General waste

Food/kitchen waste

Recyclable waste

Other

No. containers

Storage place

(1)
(2)
(3)
(1)
(2)
(3)
(1)
(2)
(3)
(1)
(2)
(3)

3. How many times and when do you usually discharge the abovementioned categories?
Waste category
General waste

How many
times
/ day

Food/kitchen waste/ day

Recyclable waste

/

Other

/

1st time

When
2nd time

Who collect3rd time

4. How do you discharge the abovementioned categories?
Waste category
General waste

116

How do you discharge?
When do you transport waste to
What kind of container do you transfer waste
storage place
to public dustbin?
□ Before close time your kiosk, □ Storage container
Time: …….:………
□ Plastic bag□ During close time your kiosk □Other,

………………………………………………..
Food/kitchen
waste□ Before close time your kiosk,
Time: …….:………
□ During close time your kiosk

Recyclable waste□ Before close time your kiosk,
Time: …….:………
□ During close time your kiosk

Other□ Before close time your kiosk,
Time: …….:………
□ During close time your kiosk

5.

No.

Items

Paper

Newspaper
Cardboard/Boxes
Books, notebook
Bags/packaging
Wastepaper
Others, …………………

Glass

Bottles/ containers
Products
Others, …………………

Plastic

BottlesContainers/foam box
Bags/packages
Products
Others, …………………

Metal

Cans
Containers
Electric applianceHome utensil
Others, ………………….

1.
2.
3.
1.
2.
3.
4.
5.
1.
2.
3.
4.
5.

………………………………………………..
□ Storage container□ Plastic bag□………………………………………………..

Other,

What do you discharge with your recyclables?
 Please identify (by or ) the recyclable
 Please identify the discharge methods (Discharge/keep for selling or reusing again/others) of eachwaste recyclable

Compon
ent

1
2.
3.
4.
5.
6.

□ Storage container□ Plastic bag□ Other, ……………………………………………..
□ Storage container□ Plastic bag□
Other,

6.

117

Discharge methods of waste recyclables ()Discharge
Keep for
Keep for selling
Othersinto generalreusing(description)
Keep forPrice
waste ()againselling () (VND/kg)[For recyclables kept for selling in Q5] Do you get benefit from recyclables? How much per time ormonth?

□ YES□ NO

If the answer is YES, ask the following question:……………VND/ …………….
Benefit

Others[Please ask manager/staff for getting information about sale record/receipt of recyclables]

7.

How many categories do you have for food waste?
□ Only one category (mixing solid food and soup together)□ More than one sub-category,
Description: ………………………………………………………………………………………..
8.

Do you get some revenue from food waste? If so, how much do you get per time or month?

□ Yes
□ No
9.

revenue
Reason……………VND/ …………….
………………………………………………………………
…...

What are the problems, difficulties, limitations which your facilities have met for waste
management?
Activity/process

Discription for problems, difficulties, limitation

1

Waste storage…………………………………………………

2

Waste discharge…………………………………………………

3

Recycling- Waste pickers- Junk-buyers- Junk-shops- Pig farmers…………………………………………………
…………………………………………………

4

Waste collection service
by Market managers…………………………………………………

5

Collection fee by Market
managers…………………………………………………

6

Others118…………………………………………………