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In eg a ing Digi al Technologies in o Solid Was e Managemen o
Achie ing Sus ainable De elopmen Goals (SDGs): A Case S udy o
KPK Wa e & Sani a ion Se ices Companies (WSSCs), Pakis an
D . Muhammad Ahsan Iqbal
Assis an P o esso , Rawalpindi Women Uni e si y
Email: [email protected]
Kundan Kuma (Co esponding Au ho )
Depa men o Na u al Resou ce and Socie y, College o Na u al Resou ces,
Uni e si y o Idaho,
995 MK Simpson Bl d, Idaho Falls, ID 83401, USA.
Email: [email p o ec ed]du
Malik Waqa Hassan Awan
Schola Riphah In e na ional Uni e si y,
Email: [email p o ec ed]
Abs ac
Solid Was e Managemen (SWM) is one o he mos p essing challenges o u ban
cen e s in de eloping coun ies, wi h signi ican implica ions o en i onmen al quali y,
economic g ow h, and public heal h. In Pakis an, pa icula ly in Khybe Pakh unkhwa
(KPK), municipal se ice p o ide s such as Wa e & Sani a ion Se ices Companies
(WSSCs) ace inc easing was e olumes, esou ce cons ain s, and ope a ional
ine iciencies. The apid e olu ion o digi al echnologies, including In e ne o Things
(IoT), Geog aphic In o ma ion Sys ems (GIS), A i icial In elligence (AI), blockchain,
and da a analy ics, p esen s ans o ma i e oppo uni ies o imp o e SWM sys ems.
This s udy in es iga es he media ing ole o Digi al Technology Adop ion in he
ela ionship be ween SWM and he achie emen o Sus ainable De elopmen Goals
(SDGs). A c oss-sec ional su ey was conduc ed wi h employees o all se en WSSCs
in KPK, using a s uc u ed ques ionnai e. Da a analysis employed desc ip i e s a is ics,
eliabili y and no mali y checks, Pea son co ela ion, simple linea eg ession, and
media ion analysis h ough he PROCESS mac o (Model 4). Resul s indica e a
signi ican posi i e ela ionship be ween SWM, digi al echnology adop ion, and SDG
achie emen . Mo eo e , digi al echnology adop ion pa ially media es he SWM SDG
link, highligh ing he necessi y o in eg a ing echnological solu ions in was e
managemen p ac ices. The pape p oposes policy e o ms, capaci y building, and join
go e nmen -non-go e nmen pa ne ships o speed up he digi aliza ion o he Pakis ani
was e sec o , which can lead o ad ancemen in Pakis an eaching he 2030 Agenda.
Keywo ds: Solid Was e Managemen , Digi al Technology Adop ion, Sus ainable
De elopmen Goals, Wa e & Sani a ion Se ices Companies.
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In oduc ion
SWM is a mul i- ace ed and esou ce-demanding se ice ha suppo s ci y heal h,
en i onmen , and economic p ospe i y (Momodu e al., 2011). By 2050, o e 6 billion
ci izens will li e in owns (Ri chie & Rose , 2018), and hey will p oduce
unp eceden ed amoun s o was e. Sha ma e al. (2021) posi ha by he middle o he
cen u y, he global gene a ion a e o municipal solid was es will exceed 3.40 billion
me ic ons a yea . The issue o was e managemen is eminen in all pa s o he wo ld,
bu he poo s uc u al, inancial, and echnological capabili ies cause unp eceden ed
ine iciencies, en i onmen al deg ada ion, and c ea e heal h haza ds in de eloping
na ions like Pakis an.
The Agenda 2030 o he Uni ed Na ions ocuses on was e minimiza ion, ecycling, and
en i onmen ally sound modes o disposal wi hin se e al o he SDGs, including Goal
6 (Clean Wa e & Sani a ion), Goal 11 (Sus ainable Ci ies & Communi ies), Goal 12
(Responsible Consump ion & P oduc ion), and Goal 13 (Clima e Ac ion). To achie e
hese, i is i al o ha e sys emic inno a ions in SWM ha would go beyond policy
changes and beha io al modi ica ions o conside echnological imp o emen s (Lee e
al., 2023).
New digi al echnologies, such as he use o In e ne o Things (IoT) sma bins and AI-
based ou e op imiza ion, GIS mapping, blockchain echnology o ack was e, and big
da a analy ics, a e changing he ace o u ban was e managemen (Fa imah e al., 2020;
Anagnos opoulos e al., 2017). The ools can inc ease ope a ional e iciency,
anspa ency, and eal- ime decision-making, ad ancing owa d SDGs. Ne e heless,
hei usage in he Pakis ani con ex is limi ed, including in he u ban cen e s, such as
he WSSCs o KP, which se e ~15 million people.
This esea ch ansla es he cu en li e a u e on he ela ionship be ween SWM and
sus ainabili y de elopmen and makes i speci ic o he ole o Digi al Technology
Adop ion. The p esen esea ch p oduces empi ical e idence o how echnology
in eg a ion will lead o he pe o mance op imiza ion o he SWM sys ems and na ional
SDG a ge s h ough an analysis o he ope a ional eali y o se en WSSCs in KP.
Objec i es o he S udy
1. To in es iga e he connec ion be ween he solid was e managemen , digi al
echnologies adop ion, and a ainmen o Sus ainable De elopmen Goals
(SDGs).
2. To analyze he di ec in luence o he solid was e managemen on he
achie emen o he SDGs in KPK WSSCs.
3. To de e mine he media ion e ec o he adop ion o digi al echnology on solid
was e managemen and SDG achie emen .
Li e a u e Re iew
Solid Was e Managemen (SWM)
Solid was e is he disca ded ma e ial ha is a esul o human ac i i y, such as
household e use, comme cial was e, cons uc ion was e, also known as cons uc ion
deb is, and indus ial by-p oduc s (Ch is ensen, 2011). Though no mally ega ded as
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solid, some was es can also be in he o m o sludge o semi-liquid. The Wo ld Bank
(Kaza e al., 2018) es ima es ha annual global solid was e gene a ion will ise om
2.01 billion me ic ons in 2016 o 3.40 billion me ic ons by 2050. The p ima y
objec i e o SWM is o collec , anspo , ea , and dispose o was e in ways ha p o ec
public heal h and he en i onmen while conse ing esou ces (Sha ma e al., 2021).
In de eloping coun ies, including Pakis an, SWM aces mul iple ba ie s: insu icien
in as uc u e, poo en o cemen o egula ions, low public awa eness, and limi ed
adop ion o mode n echnologies (Gue e o e al., 2013). Ine ec i e SWM con ibu es
o en i onmen al deg ada ion, disease ou b eaks, and loss o economic oppo uni ies,
while e ec i e sys ems can s imula e employmen , educe pollu ion, and suppo
ci cula economy models.
Sus ainable De elopmen Goals (SDGs) and SWM
The 2030 Agenda o Sus ainable De elopmen , adop ed by he Uni ed Na ions in 2015,
ou lines 17 SDGs ha encompass en i onmen al, social, and economic dimensions.
SWM is di ec ly ele an o se e al goals, especially:
SDG 6: Clean Wa e and Sani a ion — p e en ion o wa e pollu ion om was e
leacha e.
SDG 11: Sus ainable Ci ies and Communi ies — imp o ed u ban was e se ices.
SDG 12: Responsible Consump ion and P oduc ion — was e minimiza ion, ecycling,
and euse.
SDG 13: Clima e Ac ion — educ ion o g eenhouse gas emissions om was e.
P ope SWM con ibu es o hese a ge s by educing en i onmen al pollu ion,
conse ing esou ces, and p omo ing u ban esilience (Elsheekh e al., 2021).
Digi al Technologies in Solid Was e Managemen
Digi aliza ion is he use o digi al ools and echnologies in an a emp o modi y
business ope a ions and se ice deli e y. In he indus y, e iciency and sus ainabili y
a e d i en by Indus y 4.0 echnologies, i.e., IoT, AI, blockchain, GIS, d ones, and big
da a analy ics (Fa imah e al., 2020; Cheah e al., 2022).
Examples include:
IoT- illed Sma Bins: Sma Bins ha e senso s o de ec ullness and need o be
emp ied (Anagnos opoulos e al., 2017).
A i icial In elligence - Rou ing: The ou es o ou es a e op imized wi h he help o
AI o a el wi h he leas amoun o uel and en i onmen ally ha m ul emissions
(Hannan e al., 2020).
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GIS Mapping: The spa ial da a can acili a e in design o collec ion a eas and he sel -
managemen o places o illegal dumps (Mis a e al., 2018).
Blockchain Sys ems: Es ablish g ea e aceabili y o he was e s eams, p e en ing
was e ha appea s illegally (Ruohomaa & I ano a, 2019).
Da a Analy ics Pla o ms: Sho en he ime i akes o pe o m p edic i e main enance
o was e acili ies and o ecas was e gene a ion ends.
When combined wi h SWM, hese echnologies enhance he p ecision o ope a ional
decision-making, inc ease anspa ency, and allow in- ime se ice co ec ions, hus
con ibu ing o SDGs.
Linkages Be ween SWM, Digi al Technology Adop ion, and SDGs
The e is no sec e connec ion be ween SWM and SDGs, bu h ough he adop ion o
echnology, i becomes a ans o ma ional enable . Digi al echnologies make he
p ocess o da a ga he ing mo e p ecise, esou ces mo e e icien ly dis ibu ed, and
ci izens mo e in ol ed h ough he usage o mobile applica ions and epo ing sys ems
(Ga mann-Johnsen e al., 2020). In he Pakis ani scena io, whe e mos SWM ac i i ies
a e s ill conduc ed manually, adop ing digi al solu ions in o his sys em can help
inc ease hei o e all e iciency and speed up he pace o achie ing he SDGs.
Theo e ical F amewo k
The heo e ical amewo k applied in he s udy is he Technology-O ganiza ion-
En i onmen (TOE), which explains how he adop ion o echnological inno a ions in
o ganiza ions akes place. This s udy heo izes ha
Independen Va iable (IV): Solid Was e Managemen
Media o (M): Digi al Technology Adop ion
Dependen Va iable (DV): Achie emen o Sus ainable De elopmen Goals (SDGs)
Solid Was e Managemen → Digi al Technology Adop ion → Sus ainable
De elopmen Goals
This model assumes ha while SWM di ec ly impac s SDGs, he adop ion o digi al
echnologies s eng hens and pa ly media es his e ec .
Hypo heses
Based on he li e a u e, he ollowing hypo heses a e o mula ed:
H1: The ela ionship be ween solid was e managemen and he adop ion o digi al
echnology is signi ican ly posi i e conce ning he a ainmen o Sus ainable
De elopmen Goals.
H2: Solid was e managemen can con ibu e o a la ge ex en o he Sus ainable
De elopmen Goals.
H3: The e ec o solid was e managemen on achie ing Sus ainable De elopmen
Goals g ea ly depends on digi al echnology adop ion.
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Me hodology
Resea ch Design
This s udy employs a c oss-sec ional su ey esea ch design in analyzing he
connec ion be ween Solid Was e Managemen (SWM) and he ealiza ion o
Sus ainable De elopmen Goals (SDGs), wi h Digi al Technology Adop ion as he
media ing s ep. The me hod is app op ia e when he e a e hypo hesized ela ionships
ha one wan s o examine among a iables and enables he collec ion o p ima y da a
om a la ge sample wi hin a gi en pe iod o ime (Zikmund e al., 2010).
Popula ion and Sample o he S udy
The ocus popula ion o he p oposed s udy is he employees o he se en Wa e and
Sani a ion Se ices Companies (WSSCs) exis ing in he p o incial headqua e s o
Khybe Pakh unkhwa (KP), Pakis an. These WSSCs a e inco po a ed wi h he
Secu i ies and Exchange Commission o Pakis an (SECP), and hey look a e he
collec ion, anspo a ion, and disposal o Municipal was e in hei espec i e ci ies.
Acco ding o he o icial epo s, he e a e 6944 o al employees wo king a hese se en
WSSCs. Taking a sample size has been done based on he able p esen ed by K ejcie &
Mo gan (1970), which sugges s a sample size o 361 in he popula ion o his size. The
G Powe 3.1 so wa e was also used u he o indica e he minimum sample o 158 o
gi e p ope s a is ical powe a he 95 pe cen con idence le el and a medium e ec
size.
To ensu e obus esul s, 411 ques ionnai es we e dis ibu ed p opo iona ely ac oss
he se en WSSCs, and 406 comple e and alid esponses we e ecei ed, yielding a
esponse a e o 98.78%.
Resea ch Ins umen
The s uc u ed ques ionnai e used in his s udy comp ised wo sec ions:
Sec ion A: Demog aphic and o ganiza ional in o ma ion (e.g., age, gende , educa ion,
yea s o se ice, depa men ).
Sec ion B: Scales measu ing he h ee cons uc s — SWM, Digi al Technology
Adop ion, and SDG achie emen — using a se en-poin Like scale anging om 1
= S ongly Disag ee o 7 = S ongly Ag ee.
Measu emen Scales
Solid Was e Managemen (SWM):
Adop ed om Sa basso e al. (2019) and T ondillo e al. (2018), co e ing
awa eness & p ac ice, a i ude, and ope a ional s a us.
38 i ems in o al.
Digi al Technology Adop ion (DTA)
Adap ed om Fa imah e al. (2020), Anagnos opoulos e al. (2017), and Cheah
e al. (2022).
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I ems assess he a ailabili y, usage, and pe cei ed e ec i eness o IoT de ices,
GIS mapping, AI-based ou e op imiza ion, blockchain acking, and da a
analy ics in SWM ope a ions.
14 i ems in o al.
Sus ainable De elopmen Goals Achie emen (SDGs)
Adap ed om Asi i (2017) and Sola & Sunny (2019), ocusing on SDG- ela ed
ou comes such as imp o ed en i onmen al quali y, educed ca bon emissions,
enhanced ecycling, and communi y well-being.
13 I ems in o al.
Validi y and Reliabili y o Ins umen s
To ensu e con en alidi y, he ques ionnai e was e iewed by h ee academic expe s
in en i onmen al managemen and municipal go e nance, as well as wo senio WSSC
manage s wi h expe ience in echnology adop ion. A pilo es in ol ing 30 esponden s
om WSSCs was conduc ed, and C onbach’s Alpha alues exceeded he minimum
accep able h eshold o 0.70 o all cons uc s, indica ing good in e nal consis ency
(Nunnally, 1978).
Da a Collec ion P ocedu e
The da a collec ion p ocess was ca ied ou wi h o icial pe mission om he WSSCs’
managemen . Ques ionnai es we e dis ibu ed bo h physically and ia he
o ganiza ion’s digi al communica ion channels (e.g., email, Wha sApp g oups).
Responden s we e assu ed o con iden iali y and anonymi y o encou age hones
esponses. The da a collec ion spanned o e six weeks.
Da a Analysis Tools and Techniques
The collec ed da a we e analyzed using IBM SPSS S a is ics 26. The ollowing
s a is ical p ocedu es we e applied;
Desc ip i e Analysis: To summa ize demog aphic in o ma ion and a iable
cha ac e is ics.
No mali y Tes s: Skewness and ku osis alues we e assessed, wi h accep able
h esholds o ±3 (Kline, 2005).
Reliabili y Analysis: C onbach’s Alpha alues we e compu ed o ensu e in e nal
consis ency.
Co ela ion Analysis: Pea son’s P oduc -Momen Co ela ion Coe icien was used o
es H1.
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Simple Linea Reg ession: Used o es he di ec impac o SWM on SDG
achie emen (H2).
Media ion Analysis: Hayes’ PROCESS mac o (Model 4) was employed o es he
media ing ole o Digi al Technology Adop ion (H3), including o al, di ec , and
indi ec e ec s wi h boo s apped con idence in e als (5,000 samples).
Resul s and Discussion
Demog aphic P o ile o Responden s
A o al o 406 alid esponses we e ob ained om employees o he se en WSSCs in
KPK. The demog aphic p o ile shows ha 82.5% o esponden s we e male and 17.5%
we e emale. The majo i y o esponden s (46.3%) ell wi hin he age g oup o 31–40
yea s, ollowed by 28.1% in he 21–30 age g oup, 18.2% in he 41–50 age g oup, and
7.4% abo e 50 yea s. Rega ding educa ion, 54.7% held a bachelo ’s deg ee, 31.5% a
mas e ’s deg ee, and he emainde had in e media e o diploma quali ica ions.
Reliabili y Analysis
All C onbach’s Alpha alues exceed he h eshold o 0.70 (Nunnally, 1978),
con i ming ha he scales a e in e nally consis en and eliable
Desc ip i e S a is ics
The skewness and ku osis alues all wi hin he accep able ange o ±3 (Kline, 2005),
indica ing ha he da a a e no mally dis ibu ed.
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Co ela ion Analysis
Va iable
SWM
DTA
SDGs
SWM
1
0.701**
0.684**
DTA
0.701**
1
0.753**
SDGs
0.684**
0.753**
1
No e: p < 0.01 ( wo- ailed)
The co ela ion esul s show s ong posi i e ela ionships among all h ee a iables,
suppo ing H1 ha SWM, DTA, and SDG achie emen a e signi ican ly ela ed.
Reg ession Analysis
H2: SWM signi ican ly in luences SDG achie emen .
Model
β
Sig.
R²
SWM → SDGs
0.684
19.13
0.000
0.468
The eg ession analysis e eals ha SWM has a s ong and signi ican e ec on SDG
achie emen (β = 0.684, p < 0.001), explaining 46.8% o he a iance in SDG
achie emen . H2 is he e o e suppo ed.
Media ion Analysis (PROCESS Mac o – Model 4)
H3: Digi al Technology Adop ion media es he ela ionship be ween SWM and SDG
achie emen .
Pa h Coe icien s
Pa h a (SWM → DTA): β = 0.701, p < 0.001
Pa h b (DTA → SDGs): β = 0.513, p < 0.001
Pa h c (To al e ec ) (SWM → SDGs): β = 0.684, p < 0.001
Pa h c’ (Di ec e ec ) (SWM → SDGs con olling o DTA): β = 0.324, p <
0.001
Indi ec E ec
Boo s apped es ima e (5,000 samples) = 0.360, 95% CI [0.291, 0.434] does no include
ze o, indica ing a signi ican media ion e ec .
These esul s indica e pa ial media ion — meaning ha while SWM di ec ly impac s
SDG achie emen , a subs an ial po ion o his e ec is ansmi ed h ough he
adop ion o digi al echnologies. This alida es H3 and unde sco es he impo ance o
echnological in eg a ion in enhancing SWM ou comes.
Discussion
The indings a e consis en wi h global li e a u e emphasizing he ans o ma i e ole
o echnology in municipal was e managemen . The signi ican posi i e co ela ion
be ween SWM and DTA suppo s p io s udies (Fa imah e al., 2020; Cheah e al.,
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2022), highligh ing ha e ec i e SWM sys ems a e mo e likely o in eg a e
echnological inno a ions. The media ion esul s align wi h he Technology–
O ganiza ion–En i onmen (TOE) amewo k, sugges ing ha he adop ion o digi al
ools ac s as an o ganiza ional capabili y ha s eng hens he impac o SWM on SDG
p og ess.
In he case o KPK’s WSSCs, he adop ion o IoT-enabled bins, AI-based ou e
planning, and GIS acking can educe ope a ional ine iciencies, minimize uel
consump ion, and enhance anspa ency, he eby di ec ly con ibu ing o SDGs 6, 11,
12, and 13.
Conclusion
This pape has analyzed he associa ion be ween Solid Was e Managemen (SWM) and
he accomplishmen o Sus ainable De elopmen Goals (SDGs), h ough Digi al
Technology Adop ion (DTA) as an in e media e a iable, in he con ex o Wa e &
Sani a ion Se ices Companies (WSSCs) in Khybe Pakh unkhwa. Based on he c oss-
sec ional su ey o 406 esponden s, he indings alida e he a gumen ha SWM has
a posi i e e ec on SDG a ainmen , whils digi al echnologies ha e a conside able
impac on he ela ionship as a pa ial media o .
These esul s show he necessi y o echnological inno a ion o o e come he
ope a ional and en i onmen al issues o was e managemen in de eloping coun ies.
Al hough mo e ad anced and inno a i e SWM p ocesses will no eplace he p ocesses
employed in he pas , hei combina ion wi h echnologies like IoT-powe ed sma bins,
GIS mapping, AI-powe ed ou e op imiza ion, blockchain acking o was e, and da a
analy ics will b ing he p ocess a signi ican s ep o wa d, enhancing e iciency,
isibili y, and communi y in e ac ion.
Th ough implemen ing such inno a ions, WSSCs in KPK would be able o in e wine
hem wi h he na ional de elopmen al p io i ies o Pakis an and as - ack he
achie emen o SDGs 6, 11, 12, and 13. The biased media ion ha has been e ealed in
his s udy implies ha , hough SWM di ec ly in luences long- e m de elopmen , he
implemen a ion o digi al echnologies can inc ease his in luence and make he esul s
mo e no iceable and quan i iable.
Recommenda ions
Based on esea ch esul s, he ollowing ecommenda ions conce ning policymake s,
municipal au ho i ies, and WSSC managemen can be o e ed:
1. De elop a Digi al T ans o ma ion Roadmap o WSSCs
Es ablish a s a egic plan ou lining he adop ion and in eg a ion o IoT, GIS, AI,
and blockchain echnologies in was e collec ion, anspo a ion, and disposal.
2. In es in In as uc u e and Sma Technologies
Deploy IoT-enabled sma bins, GPS-based lee acking, and AI-based ou e
op imiza ion ools o enhance ope a ional e iciency and educe en i onmen al
impac .
