Use of a Hybrid Approach to Estimate Greenhouse Gas Emissions from the Transport Sector in Palestine

Abstract

The transport sector is the second leading emitter of greenhouse gas emissions (GHGEs) from fuel combustion activities on a global scale. Reducing emissions related to this sector requires detailed data about the emissions by vehicle type and traveled distance. Generally, these data are missing in developing countries, which makes difficult the establishment of effective policies for the reduction of these emissions. This paper presents a hybrid method to estimate these emissions using the IPCC 2006 guidelines. The method combines bottom-up and top-down approaches to estimate vehicular emissions using data about the vehicle type, vehicle kilometers traveled, and fuel consumption. This method is applied for the first time for the Palestinian territory. Data have been collected from the administration, official reports, and papers. The results show a significant increase in the total vehicles in Palestine, particularly diesel vehicles. Emissions from the on-road transport system were approximately 2,207,834 tons of CO₂eq in 2019. Diesel vehicles were responsible for about 75% of these emissions. Private cars were the most significant contributor to these emissions, with a share exceeding 50% of the total emissions, followed by commercial vehicles and light trucks (20%), public taxis (9%), and buses (7%). These results show clearly that the GHGEs reduction policies in Palestine should focus on diesel and private vehicles by developing the public transport systems and replacing diesel and gasoline vehicles with more environmentally friendly vehicles, such as hybrid and electric cars.

1. Introduction

The transport sector is one of the main emitters of greenhouse gas emissions (GHGEs) from the energy sector [1,2,3]. World Resources Institute (WRI) data showed that the transport sector is the second largest contributor to GHGEs after electricity production [4]. This is also true on a country scale, as the transport sector is considered a significant emitter of GHGEs in many countries, including but not limited to the United States, United Kingdoms, France, Belgium, Italy, Lebanon, Jordan, Palestine, Egypt, United Arab Emirates, Saudi Arabia, and Kuwait [2,5,6,7,8,9,10,11,12,13,14]. The reduction of the emissions related to this sector requires policies, which should be guided by detailed data about the emissions of this sector, mainly by vehicle type and traveled distance.

Vehicular emissions can be estimated using either a bottom-up or a top-down approaches, depending on the data availability and geographical scope [15]. Top-down approaches are based on fuel consumption data. In contrast, bottom-up approaches require higher levels of detailed data such as the number of vehicles, total kilometers traveled, characterization of vehicles, etc. [16,17]. The combination of the two approaches creates the so-called hybrid methodology or approach [18].

Scholars used the top-down approach to estimate GHGEs on large geographical scales, such as national and country scales, which could be downscaled to finer scales based on proxy data [19,20,21,22,23,24]. Charabi et al. (2020) assessed the emissions from the transport sector in Oman by transportation categories based on the Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines, using the default emission factors and fuel consumption data [24]. Engo (2019) applied a top-down approach to perform a decoupling analysis of GHGEs from the transport sector in Cameroon [22]. On a local level, Sówka and Bezyk (2018) used the territorial principle to account for GHGEs from the major sectors in Wroclaw, Poland. Concerning the transport sector, carbon accounting was based on the average fuel consumption values for each vehicle category and the average vehicle kilometers traveled (VKT) values derived from data available on a country scale [23].

The bottom-up approach was applied on lower scales (e.g., city scale). Mateo Pla et al. (2021) applied a bottom-up approach to quantify spatial urban traffic emissions in Valencia City based on information gathered from traffic management and control systems [25]. Using these traffic monitoring systems has allowed the researchers to estimate the emissions in the city at a high temporal and spatial resolution. However, the applicability of this novel approach is subject to the availability of traffic monitoring sensors. Some bottom-up approaches consider the operational environment and transportation modes for higher accuracy in terms of GHGEs accounting [17]. For instance, a study carried out in Korea proposed a methodology that analyzes the emissions for each transport mode, taking into account the transportation environments, which requires more detailed activity data [17]. Other bottom-up models used vehicle ownership and VKT data in carbon accounting, such as Zeng et al. (2016), who used this approach to estimate vehicular emissions from four major Chinese cities [26].

Hybrid models were used to estimate emissions on different spatial scales [20,27,28]. For example, Baayoun et al. (2019) estimated GHGEs from light-duty vehicles in Lebanon according to the Tier 2 methodology in the European Monitoring and Evaluation Programme/European Environmental Agency (EMEP/EEA) air pollutant emission inventory guidebook [15]. In this study, the emission factors reported by the manufacturer for each vehicle model were used to estimate the emissions besides the VKT (as activity data) [28]. The distance traveled was estimated from the fuel consumed, where the fuel efficiency of a vehicle was considered constant and equal to the value reported by the manufacturer.

Despite the variety in vehicular emissions estimation methods, some countries, particularly developing countries, face difficulties estimating these emissions at a higher classification level (by vehicle and fuel type) due to a lack of required data. This paper presents a hybrid approach to estimating these GHGEs in these countries. The capacity of this method is illustrated through its application for the first time in the Palestinian territory.

The contribution of this research stems from estimating the GHGEs related to fuel combustion in mobile sources at a higher level of detail using limited data. It provides policymakers with detailed information to help establish policies to reduce vehicular emissions. Data availability in Palestine constitutes a significant barrier to the use of the previous models to estimate detailed vehicular emissions. The published literature on GHGEs from the transport sector in Palestine is very limited. Few articles used the top-down approaches to estimate the GHGEs related to the transport sector on a national scale. Some scholars [29,30] estimated the emissions from the entire vehicle fleet in Palestine by the type of fuel (diesel and gasoline), while [31,32,33] assessed the environmental impact of introducing hybrid and electric vehicles to the vehicle fleet in Palestine. Qureitem et al. (2020) investigated the GHGEs from on-road vehicles based on the IPCC 2006 guidelines and using the default emission factors, the emissions were estimated as the national totals from diesel and gasoline vehicles [29]. Aburas, H., and Shahrour, I. (2021) analyzed the impact of mobility restrictions in Palestine on the environment and public health in terms of energy consumption and GHGEs [30]. A study by Hassouna, F. M. A., and Al-Sahili, K. (2020) assessed the environmental impact of introducing hybrid vehicles to the vehicle fleet in the West Bank [31]. The predicted emissions in this study for 2020 were double the actual emissions estimated by the Palestinian Central Bureau of Statistics (PCBS) from the transport sector [34], which could be attributed to the accuracy of the model used to predict the number of vehicles and kilometers traveled [35] and to the use of emission factors [36] different from the default in the IPCC guidelines. A second study by Hassouna, F. M. A., and Al-Sahili, K. (2020) investigated another alternative to reduce the environmental impact of the transport sector; this time, they assessed the implication of replacing 10% of traditional vehicles with electric vehicles by 2030 [32]. In estimating the GHGEs from electric vehicles in 2030, they assumed the emission factors of electricity generation to be constant for the next 10 years. This is inaccurate, as the electricity generation mix varies from one year to another [37]. Another study tried to introduce a sustainable public transportation system by replacing the traditional taxi fleet (entirely composed of diesel vehicles) with hybrid ones under two scenarios: 10% and 50% hybrid taxi penetration [33]. A similar methodology and emission factors to the previously presented studies were used.

Since road transport constitutes a significant contributor to the GHGEs in Palestine, it is necessary to analyze these emissions by type of vehicle (private cars, taxis, buses, trucks, etc.) and fuel to establish more targeted policies to reduce the national GHGEs. This research contributes to this goal by estimating the GHGEs by type of vehicle and fuel. The proposed hybrid approach based on the 2006 IPCC guidelines is used for this estimation using data collected for the transport sector in Palestine over the period 2011–2021.

This paper presents successively (i) a description of the research methodology and data collection and analysis, (ii) a presentation and discussion of the GHGEs related to road transport in Palestine, and finally, (iii) a presentation of the main findings and research limitations.

2. Materials and Methods

2.1. Overview

This study aims at estimating GHGEs from the transport sector in Palestine, which suffers from a lack of detailed data about this sector. The GHGEs are estimated using a hybrid method combining bottom-up and top-down approaches.

The transport system in Palestine is based on the use of road vehicles. It is one of the main sources of GHGEs in the country [13]. The Tier 1 method is used to estimate the vehicular emissions. It combines two methodologies presented in the IPCC guidelines: (i) the fuel consumption method representing a top-down approach, and (ii) vehicles’ kilometers traveled, which is a bottom-up approach.

To estimate the GHGEs by the type of vehicle and fuel, which has not been estimated before for Palestine, it was necessary to collect the data according to the IPCC guidelines. The following section presents the data collection.

Table 1. Classification of the vehicle fleet in Palestine in 2019 by type of vehicle and fuel.

Type of Vehicle	Total Number	Fuel
		Diesel	Gasoline	Electric	Hybrid Diesel	Hybrid Gasoline	Unclassified
Bus	2912	2912
Tractor	703	703
Public Taxi	12,219	12,157	29			32
Private Car	249,637	115,083	131,758	66		624	1947
Commercial Vehicle	15,921	15,380	541
Commercial Truck < 4.5 ton	10,807	10,685	16
Other Truck > 4.5 ton	4127	4122
Motorcycle	1212		1212
Trailer and Semi-Trailer	3757	601
Other Vehicle	820	820
Grand Total	302,115	162,463	133,556	66		656	1947

summarizes the characteristics of the vehicle fleet in Palestine in 2019 as provided by the administration.

2.2. Data Collection

The data required to estimate the GHGEs from on-road vehicles include (i) activity data (fuel sale, VKT, etc.) and (ii) emission factors of GHGEs from the combustion of fuel. Other proxy data were used to fill the gaps and estimate the VKT and fuel sales by the type of vehicle and fuel. Table 1 and

Table 2. Data collected by its source.

Data	Source	Reference
Number of vehicles by the type of vehicle and fleet	Palestinian Ministry of Transport in the West Bank and Gaza Strip (MoT)	[38]
Vehicles’ kilometers travelled by type of vehicle	Published literature	[39]
Vehicles’ kilometers travelled by type of fuel	Energy consumption in the transport sector and fuel economy in Palestine from PCBS	[40]
Fuel sales by type of fuel	Palestinian Energy and Natural Resources Authority (PENRA) and Palestinian Central Bureau of Statistics (PCBS)	[41,42]
Typical fuel consumption figures, per km, by category of vehicle	European Environment Agency (EEA)	[15]
Fuel economy by type of vehicle in Palestine	Energy consumption in the transport sector and fuel economy in Palestine from PCBS	[40]
Fuel quality in gas stations in Palestine	Published literature	[39,43]

summarize the data used in this research and the sources, respectively.

The vehicle statistics (number of registered/licensed vehicles, type of vehicle by fuel) were obtained from the official statistical reports published by the Ministry of Transport (MoT) annually. The report provides information on the transport fleet in Palestine regarding the vehicle fleet composition, number of registered and licensed vehicles, driving licenses, and car rental and sale companies [38,44]. The data quality is high because the data are collected from vehicles’ registration and license offices, official units, and the Palestinian Central Bureau of Statistics (PCBS).

Data on the VKT were available separately by type of fuel (diesel, gasoline, etc.) and type of vehicle (private car, taxi, truck, bus, etc.). The data on the VKT by type of fuel were obtained from the PCBS through a survey that used to be carried out annually until the year 2015. The report of this survey is available under the name ‘Energy consumption in the transport sector and fuel economy in Palestine’ [40]. The report also provides data on fuel economy by type of vehicle in Palestine. On the other hand, the VKT by type of vehicle was obtained from [39]. The data are considered accurate since representative sample sizes were used.

Fuel sales data were collected from the Palestinian Energy and Natural Resources Authority (PENRA) and PCBS [41,42]. These data are highly accurate because they are collected on a country scale from imports and local and national suppliers. The fuel quality in gas stations was obtained from [39,43], where representative fuel samples from different gas stations in Palestine were tested for quality and characteristics. Concerning the GHGEs emission factors, the default emission factors for mobile sources in the IPCC guidelines were used due to the unavailability of country-specific emission factors [18].

2.3. Trend Analysis

The Mann–Kendall non-parametric test (M–K test) was applied using R-4.3.1 software to check the consistently increasing or decreasing trends in the total number of vehicles and the number of diesel and gasoline vehicles in the West Bank from 2011–2021. The restriction to the West Bank was due to the lack of data on the vehicle fleet in the Gaza Strip. However, the data from the West Bank could be considered representative of the national scale because the vehicle fleet in Gaza Strip accounts for only 15% of the national fleet [44].

2.4. Estimation of GHGEs

Since this study aims to estimate the emissions from the transportation sector by the type of vehicle and fuel, it is necessary to know the amount of fuel consumed by each type of vehicle. A hybrid approach combining the two methodologies mentioned in the 2006 IPCC guidelines for emissions estimation from the transport sector was used in this study. This approach is based on fuel consumption and the distance traveled by vehicles. For the first method, the fuel sold is a good approach to estimating road traffic’s emissions on a national or country scale, whereas the vehicles’ kilometers traveled could be applied at smaller scales but requires more data regarding the number and characteristics of the vehicles, distance traveled, etc. Both approaches were combined together to estimate the GHGEs from on-road vehicles in Palestine by the type of vehicle and fuel. Figure 1 explains the methodology for estimating GHGEs in this study.

The data collected included the vehicle fleet composition by the type of vehicle and fuel (private car, taxi, bus, motorcycle, truck, trailer and semi-trailer, and other vehicle) and the total fuel consumption by type of fuel (diesel, gasoline) [45]. The vehicles’ distance traveled in Palestine was collected from the survey on energy consumption in transport by the PCBS [40]. The survey included 6947 vehicles in Palestine. In this survey, vehicles were classified based on the type of vehicle and fuel. Three categories of vehicles were identified:

• Passenger vehicles, including private cars, taxis, motorcycles, and buses.
• Goods vehicles, including trucks and commercial cars.
• Other vehicles, including ambulances, mobile cranes, self-propelled rollers, bulldozers with metallic wheels, etc.

The results of a survey carried out by the Palestine Polytechnic University in 2017 were used [39] to estimate the average distance traveled by different types of vehicles. The estimation of the amount of fuel consumed by each type of vehicle was based on data collected in 2017. After obtaining the total driven distance by type of vehicle in Palestine in 2019 (km/year), the result was compared to those in the 2014 survey and the published literature [29,31,32,33,39,40] for verification. This result and other information obtained from the 2014 survey and data on the vehicle fleet in 2019 by type of vehicle and fuel were used to estimate the total driven distance by type of vehicle and fuel. The result was verified using the total diesel and gasoline sold in 2019, as reported by PENRA [45]. The GHGEs were estimated according to Tier 1 as follows:

$${\mathrm{E}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}}_{\mathrm{G}\mathrm{H}\mathrm{G},\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l},\mathrm{v}\mathrm{e}\mathrm{h}\mathrm{i}\mathrm{c}\mathrm{l}\mathrm{e}}(\mathrm{k}\mathrm{g})={\mathrm{F}\mathrm{u}\mathrm{e}\mathrm{l} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{p}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l},\mathrm{v}\mathrm{e}\mathrm{h}\mathrm{i}\mathrm{c}\mathrm{l}\mathrm{e}}(\mathrm{T}\mathrm{J})\times {\mathrm{E}\mathrm{F}}_{\mathrm{G}\mathrm{H}\mathrm{G},\mathrm{f}\mathrm{u}\mathrm{e}\mathrm{l}}(\mathrm{k}\mathrm{g}/\mathrm{T}\mathrm{J})$$

(1)

where:

• Emissions _{GHG, Fuel, Vehicle}: emissions of a GHG by type of fuel (diesel, gasoline, etc.) and vehicle (private cars, taxis, etc.).
• Fuel consumption _{fuel, vehicle}: the amount of a specific fuel consumed by a specific type of vehicle.
• EF _{GHG, fuel}: emission factor of a GHG due to combustion of a specific fuel type in a specific vehicle category.

The density of diesel and gasoline used in this research to convert the fuel from physical units into energy units to estimate the GHGEs was adapted from a published article that examines the gasoline and diesel characteristics at nine gas stations in Palestine. The used densities of diesel and gasoline were 0.748 kg/L and 0.833 kg/L, respectively [43].

3. Results and Discussion

3.1. Trend of Vehicle Fleet and GHGEs from the Transport Sector 2011–2021

The results show a significant increasing trend in the vehicle fleet in Palestine during the study period of about 18,260 vehicles/year, as illustrated in Figure 2. This increase is significant at the 0.001 level. The growing number of vehicles is a result of demographic growth and the weak public transportation system in the country [31].

Figure 3 shows a decreasing trend in the number of gasoline vehicles in Palestine by about 1.05%/year. This result is significant at the 0.001 level. The number of diesel vehicles grew over the same period by 0.95%/year. The increase in diesel vehicles could be attributed to the fact that diesel vehicles provide better mileage than gasoline ones and the lower diesel prices in Palestine than gasoline prices.

The GHGEs from the transport sector for the period 2011–2021 were estimated using the IPCC 2016 guidelines, as explained in Section 2. Figure 4 illustrates the variation in the GHGEs during the study period. The highest GHGEs were recorded in 2021. As the emissions are not showing a clear trend at first glance, the Mann–Kendall test was also used to check significant trends in vehicular emissions. Figure 5 shows an increasing trend of about 135,737 tons of CO₂eq/year. However, this trend is statistically not significant.

3.2. GHGEs from the Transport Sector in Palestine, 2019

The total emissions from the transport sector in 2019 in Palestine were 2,207,834 tons of CO₂eq, where CO₂ accounts for 98% of these emissions (Figure 6). Diesel vehicles contributed to around 75% of the total GHGEs, as shown in Figure 7. This result is reasonable because about 55% of the fleet in 2019 was diesel vehicles. Additionally, the amount of diesel fuel consumed by on-road transport in 2019 was twice that of gasoline fuel, which is not only attributed to the larger number of diesel vehicles. Indeed, public taxis, buses, and most commercial vehicles and trucks are diesel-based vehicles [46]. The fuel consumption of these vehicles is usually higher than gasoline vehicles [40].

The vehicular emissions were estimated and classified by the type of vehicle for 2019, as presented in Figure 8. Private cars are the biggest contributor to the GHGEs from the transport sector, with a share exceeding half of the total on-road emissions (1,207,169 tons of CO₂eq/year, equivalent to 55% of the transport sector emissions). This result is expected, as the private cars category has the highest vehicle population. The second top emitter is the commercial vehicles and light trucks, with a share of about 20% of the total vehicular emissions (437,138 tons of CO₂eq/year). Public taxis come in third place, with around 9% (203,703 tons of CO₂eq/year) of the total emissions, followed by buses, with 7% (161,917 tons of CO₂eq/year) of the total transport emissions.

Figure 9 shows the GHGEs by the type of vehicle and fuel. The largest share of GHGEs is attributed to private cars for both the diesel and gasoline-based vehicles (593,447 and 613,721 tons of CO₂eq/year, respectively). The gasoline emissions are higher due to the larger number of private gasoline cars. Modest amounts of gasoline-related emissions are released from commercial vehicles, trucks, gasoline, and other vehicles (this category includes tractors, motorcycles, ambulances, etc.).

The estimated GHGEs from the transport system were compared to the National Greenhouse Gas Inventory for 2019, and the result is presented in Figure 10. Unfortunately, the comparison was only possible for the total GHGEs because the National Greenhouse Gas Inventory does not provide any information on emissions from different types of vehicles. The relative differences between the results in this study and the National Greenhouse Gas Inventory in terms of the total GHGEs (CO₂eq) and CO₂ emissions were approximately 3.00% and 2.61%, respectively, while the differences in N₂O and CH₄ emissions were higher: around 36% for N₂O and 16% for CH₄. This difference could be attributed to (i) the use of fuel characteristics in Palestine instead of standard values and (ii) the use of default emission factors for N₂O and CH₄, as two values are available for gasoline vehicles based on control technology (motor gasoline uncontrolled and oxidation catalyst), which directly affects the N₂O and CH₄ but not the CO₂ emissions. According to the IPCC guidelines, in the case of motor gasoline-uncontrolled vehicles, the default emissions factors of N₂O and CH₄ are 3.2 kg/TJ and 33 kg/TJ, respectively, while for motor gasoline vehicles with oxidation catalysts, the emission factors are 8.0 kg/TJ for N₂O and 25 kg/TJ for CH₄.

The emissions from passenger cars are equal to 4.84 tons CO₂eq, which is slightly higher than the value for a typical passenger car, which is 4.60 tons CO₂eq [47]. This result could be attributed to the poor fuel quality and the aged vehicle fleet in Palestine.

4. Conclusions

The transport sector is considered a major contributor to the GHGEs from fuel combustion activities in the energy sector. This paper presented a hybrid method to estimate the GHGEs from the on-road transport system with application in Palestine, which constitutes a study area with limited data availability. This method combines bottom-up and top-down approaches to estimate GHGEs using detailed vehicle data, including vehicle type, fuel consumption, and traveled distance. It provides information that helps policymakers to establish more targeted environmental policies that help the country to reduce its GHGEs and achieve the goals set concerning the country’s nationally determined contribution.

The vehicle fleet was assessed for the period 2011–2021. The data analysis indicates a significant increase in vehicles in the country of about 18,260 vehicle/year, with a clear preference for diesel vehicles, since the share of diesel vehicles is growing by 0.95%/year on the account of gasoline vehicles. In terms of the GHGEs over the same period, the emissions showed an increasing trend; however, this trend was statistically insignificant.

The GHGEs from vehicles were approximately 2,207,834 tons of CO₂eq in 2019. About 75% of these emissions were due to diesel vehicles. Private cars constituted the biggest contributor to the GHGEs in the transport sector, with a share exceeding 50% of the total vehicle emissions, followed by commercial vehicles and light trucks (20%), public taxis (9%), and buses (7%).

These results show clearly that the GHGEs reduction policy should focus on diesel and private vehicles by developing public transport systems and replacing diesel and gasoline vehicles with environmentally friendly vehicles, such as electric and hybrid cars.

The main limitation of this study is the absence of country-specific emission factors and some data for the Gaza Strip.