Table 3 Environmental sustainability... | Emerald Publishing

Table 3

Environmental sustainability indicators linked to digitalization and vehicle routing optimization

KPIs	Measurement focus	Supporting digital technologies	Vehicle routing optimization role	Environmental impact	References
GHG emissions	CO₂-equivalent emissions generated per route, trip, or vehicle	IoT sensors, GPS, AI, big data analytics	Minimizes travel distance, idle time, and unnecessary trips	Reduction in carbon footprint and air pollution	Sicilia-Montalvo et al. (2013), Jabir et al. (2017), Dutta et al. (2021), Li et al. (2023), Vishkaei and De Giovanni (2024)
Fuel consumption	Fuel used per vehicle, route, or ton-kilometer	IoT, telematics, cloud platforms	Optimizes route selection, load consolidation, and speed profiles	Lower fossil fuel use and emissions	Kirci (2016), Gayialis et al. (2018), Su and Fan (2020), Salehi-Amiri et al. (2022), Ren et al. (2023)
Energy consumption (EVs)	Electrical energy consumed per route or delivery	AI, ML, IoT, charging data platforms	Considers battery constraints, charging locations, and energy-efficient routing	Improved energy efficiency and reduced indirect emissions	Basso et al. (2021), Jelen et al. (2022), Wang et al. (2024)
Route efficiency	Ratio of productive travel distance/time to total travel	GPS, real-time traffic data, AI	Identifies shortest and least congested routes	Reduced congestion, emissions and travel time	Kaabachi et al. (2017), Gomes et al. (2021), Cerrone and Sciomachen (2022), Vishkaei and De Giovanni (2024)
Vehicle idle time	Duration of vehicle inactivity during operations	IoT sensors, telematics, real-time monitoring	Reduces waiting time through optimized scheduling	Lower fuel waste and emissions	Hrabec et al. (2019), Salehi-Amiri et al. (2022), Bouleft and Elhilali Alaoui (2023)
Fleet utilization	Degree to which vehicle capacity and availability are effectively used	Big data analytics, cloud platforms	Balances vehicle assignment and capacity constraints	Fewer vehicles required, reduced environmental impact	Afifi et al. (2014), Villarreal et al. (2016), Ren et al. (2023), Vishkaei and De Giovanni (2024)
Empty or redundant trips	Trips with low or no productive load	IoT, routing analytics	Eliminates unnecessary or poorly planned routes	Reduced fuel consumption and emissions	Juan et al. (2016), Luo et al. (2021), Justo et al. (2023), Li et al. (2023)
Waste collection efficiency	Collection effectiveness per route or area	IoT-enabled smart bins, sensors	Dynamic routing based on fill-level data	Fewer trips and improved environmental performance	Sarvari et al. (2020), Akbarpour et al. (2021), Salehi-Amiri et al. (2022), Idrissi et al. (2024)

KPIs	Measurement focus	Supporting digital technologies	Vehicle routing optimization role	Environmental impact	References
GHG emissions	CO₂-equivalent emissions generated per route, trip, or vehicle	IoT sensors, GPS, AI, big data analytics	Minimizes travel distance, idle time, and unnecessary trips	Reduction in carbon footprint and air pollution	Sicilia-Montalvo et al. (2013), Jabir et al. (2017), Dutta et al. (2021), Li et al. (2023), Vishkaei and De Giovanni (2024)
Fuel consumption	Fuel used per vehicle, route, or ton-kilometer	IoT, telematics, cloud platforms	Optimizes route selection, load consolidation, and speed profiles	Lower fossil fuel use and emissions	Kirci (2016), Gayialis et al. (2018), Su and Fan (2020), Salehi-Amiri et al. (2022), Ren et al. (2023)
Energy consumption (EVs)	Electrical energy consumed per route or delivery	AI, ML, IoT, charging data platforms	Considers battery constraints, charging locations, and energy-efficient routing	Improved energy efficiency and reduced indirect emissions	Basso et al. (2021), Jelen et al. (2022), Wang et al. (2024)
Route efficiency	Ratio of productive travel distance/time to total travel	GPS, real-time traffic data, AI	Identifies shortest and least congested routes	Reduced congestion, emissions and travel time	Kaabachi et al. (2017), Gomes et al. (2021), Cerrone and Sciomachen (2022), Vishkaei and De Giovanni (2024)
Vehicle idle time	Duration of vehicle inactivity during operations	IoT sensors, telematics, real-time monitoring	Reduces waiting time through optimized scheduling	Lower fuel waste and emissions	Hrabec et al. (2019), Salehi-Amiri et al. (2022), Bouleft and Elhilali Alaoui (2023)
Fleet utilization	Degree to which vehicle capacity and availability are effectively used	Big data analytics, cloud platforms	Balances vehicle assignment and capacity constraints	Fewer vehicles required, reduced environmental impact	Afifi et al. (2014), Villarreal et al. (2016), Ren et al. (2023), Vishkaei and De Giovanni (2024)
Empty or redundant trips	Trips with low or no productive load	IoT, routing analytics	Eliminates unnecessary or poorly planned routes	Reduced fuel consumption and emissions	Juan et al. (2016), Luo et al. (2021), Justo et al. (2023), Li et al. (2023)
Waste collection efficiency	Collection effectiveness per route or area	IoT-enabled smart bins, sensors	Dynamic routing based on fill-level data	Fewer trips and improved environmental performance	Sarvari et al. (2020), Akbarpour et al. (2021), Salehi-Amiri et al. (2022), Idrissi et al. (2024)

Source(s): Authors’ own work based on literature review