Impact of the Mobility Restrictions in the Palestinian Territory on the Population and the Environment

Abstract

This paper analyzes the mobility restrictions in the Palestinian territory on the population and the environment. The literature review shows a scientific concern for this issue, with an emphasis on describing mobility barriers and the severe conditions experienced by the population due to these barriers as well as the impact of mobility restrictions on employment opportunities. On the other hand, the literature review also shows a deficit in quantitative analysis of the effects of mobility restrictions on the environment, particularly on energy consumption and greenhouse gas emissions. This paper aims to fill this gap through a quantitative analysis by including data collection about mobility restrictions, using network analysis to determine the impact of these restrictions on inter-urban mobility, and analysis of the resulting energy consumption and CO₂ emissions. The results show that mobility restrictions induce a general increase in energy consumption and CO₂ emissions. The average value of this increase is about 358% for diesel vehicles and 275% for gasoline vehicles.

1. Introduction

This paper aims to evaluate the impact of inter-urban mobility restrictions on the Palestinian territory’s population and environment. It considers two kinds of mobility restrictions: (i) the construction of the separation wall, which resulted in road closure, and (ii) checkpoints, which cause serious disturbances in inter-urban mobility. These restrictions started around thirty years ago with the installation of permanent or temporary checkpoints [1,2,3,4,5,6,7] and the construction of a separation wall [1,2,3,4,5,8]. They caused severe disturbances in the daily life of the population, with such adverse effects as anxiety, increased physical risk, time losses, and decreased employment opportunities. They also induced a significant increase in transport route distance and time, resulting in higher energy consumption and greenhouse gas emissions. Evaluating the impact of the mobility restrictions on the population and the environment constitutes the first step of a tentative attempt to find solutions and reduce the adverse effects of these restrictions. This begins with developing awareness on the part of the population, the authorities, and the international community about the harmful impact of the restrictions. It also includes developing digital tools that provide shared real-time information that could help attenuate the impact on the population and environment.

Several scholars and international institutions have described the mobility restrictions in the Palestinian territories and analyzed their impact on the population. The World Bank has published a notebook [9] exploring the historical events and facts of the evolving mobility restrictions in the Palestinian territories, using a narrative approach. The notebook states that the physical mobility barriers have fragmented the West Bank territory into disconnected cantons. Moreover, they stifle economic activity by raising the cost of doing business and increasing uncertainty. The International Monetary Fund (IMF) [10] and the United Nations Conference on Trade and Development and (UNCTAD) [11] confirmed the impact of these barriers on economic activity. Both studies used a macroeconomic indicator-based approach and time-series data to analyze the impact of the barriers on macroeconomic performance, the labor market, and fiscal balance.

Braverman [2] argued that barriers and walls in the Palestinian territory complicate a straightforward physical task by preventing population movement. He reported that the intensified use of technology at checkpoints to control commuters dehumanized the local Palestinian community. Boussauw and Vanin [12] indicated that the mobility restrictions in the Palestinian territory created a closed social system, with adverse consequences for economic and cultural exchange. Cali and Miaari [7] analyzed the impact of mobility restrictions on employment. They showed that checkpoints reduced the employment opportunity, the number of working days, and working wages. For example, installing a checkpoint ten minutes from a Palestinian locality reduced employment opportunity and the working days by 0.14 and 0.22 percentage points, respectively. Rijke and Minca [4] made a deep analysis of daily life at a checkpoint. They highlighted the long queues and the arbitrary implementation of rules in a way that was humiliating and at times, violent towards the population. They argued that checkpoints create new political geographies, with a regime of mobility uncertainty and arbitrariness due to travel time delays.

Other scholars analyzed the impact of the mobility restrictions on different areas in the world. For example, Barka [13] reported that border crossings and checkpoints in West Africa caused waiting times for trucks ranging from 18 min to 29 min per 100 km. Hence, the cost of trading sectors in Africa is doubled compared with Asian regional trade. In addition, Reyna et al. [14] analyzed the environmental impact of mobility restrictions at the border crossing between Mexico and the United States. They showed that the highest-congestion scenario could increase by as much as 200% in vehicle volume, 460% in PM2.5 and NOx emissions, and 540% in GHGs emissions.

According to the literature review, previous researchers have focused on analyzing and quantifying the economic and social cost of the mobility restrictions in the Palestinian territories, while the environmental impact is less considered. This paper aims to fill this gap through a quantitative analysis of the impact of mobility restrictions on the environment. It contributes to this issue through research which combines (i) the collection of data on the mobility restrictions and their integration into a GIS system,; (ii) the use of network analysis to investigate the impact of the separation wall and checkpoints on route length and transport time [15]; and (iii) evaluating the impact of the mobility restrictions on energy consumption and CO₂ emissions. The latter constitutes a critical environmental issue for the Palestinian territory, as the transportation sector accounts for around 72% of its CO₂ emissions [16]. This paper paves the way for a methodology that can help scan the spatial and temporal adversity of the mobility restrictions and develop digital tools that will provide real-time information to attenuate the impact of these restrictions on both the population and the environment.

The paper is organized as follows: the first section outlines the methodology and material used in this research; the second section presents and discusses the application of the research methodology to the Palestinian territory; finally, the last section summarizes the primary outcomes of this research and outlines its limitation and perspectives.

2. Materials and Methods

2.1. Overview

Our evaluation of the impact of mobility restrictions on the population and the environment was based on a scientific methodology that can be reproduced and applied to other kinds of mobility restrictions, such as those related to natural or man-made disasters. The methodology includes the following three phases.

The first phase concerns data collection about the inter-urban mobility infrastructure and restrictions. Data was collected from different sources, mainly from governmental authorities and non-governmental organizations (NGOs). In the future, this could be extended to social media and mobile crowdsourcing.

The second phase uses network analysis to determine the best route [14] under two conditions: the absence of mobility restrictions and the presence of those restrictions.

The last phase analyses the impact of the mobility restrictions on (i) the population, with a focus on increases in route length and travel time, and (ii) the environment, with emphasis on the additional energy consumption and greenhouse gas emissions.

The following sections will present this methodology in detail through its application to the Palestinian territory.

2.2. Data Collection

This research was based on data related to physical infrastructure, community mobility, and mobility restrictions. The collected data were integrated into a Geographic Information System (GIS) that provided a spatial illustration to support analysis of the mobility restrictions. Figure 1 summarizes the types and sources of data collected. Road network data were obtained from the last updated version of the 2018 geodatabase of the Palestinian ministry of transport (MOT). The data came in a shapefile with categories including road name, number, width, and type. Mobility restriction data, including the separation wall and checkpoints, were obtained from the Geospatial web mapping application of the ministry of local government (Geomolg) [17]. Geomolg is an open-source application that provides the ability to downloading the target layer in the form of a shapefile for any location.

Due to the lack of data on community commuting, individual vehicular emissions, and waiting time due to mobility restrictions, the study used data from a field survey conducted by the Applied Research Institute-Jerusalem (ARIJ) [18]. The survey concerned checkpoints distributed in different locations in the West Bank. It employed 70 vehicles (cars, taxis, buses, and trucks) with tracking devices (GPS) for a period of 6 months (January–July 2018). More than 18 million records were registered.

Table 1. The Average delay time at permanent checkpoints in the West Bank [18].

Checkpoint	Governorate	Average Delay Time (Minutes)
Jalameh	Jenin	29
Kafriat	Tulakerm	42
Qalqilya DCO	Qalqilya	80
Jaljoulia	Qalqilya	89
Ni’lin workers	Ramallah	23
Maccabim	Ramallah	55
Qalandiya	Jerusalem	81
Glo 300	Bethlehem	34
Al Jab’a	Bethlehem	50
Tarqumiya	Hebron	26
Meitar	Hebron	35

summarizes the average delay time recorded in this survey at the main checkpoints in the Palestinian territory. The delay ranges from 23 to 89 min, with an average value of 49.5 min. This research was conducted with the average values, which constitute a limitation for this research. In the future, mobility data could be enhanced using crowdsourcing, including social media and mobile applications [19,20].

Most data were obtained in shapefile format in the shape of polygon or line, which includes attributes labeled in different fields (

Table 2. Attributes of shapefiles.

Governorate Border	Palestinian Communities	Separation Wall	WB Road Network
ObjectID	ObjectID	ObjectID	ObjectID
Shape Length	Shape Length	Shape Length	Shape Length
Shape Area	Shape Area	Status	Status
-	Community Name	-	Road Type

). All the shapefiles have two common fields: (i) ObjectID, a unique, non-null integer field used to identify rows in tables in a shapefile, and (ii) Shape Length, which is used to store the calculated geometry of lines or polylines lengths. For example, in the attribute of polygon data (Qalqilya governorate border and Palestinian communities), a table called “Shape Area” stores the calculated geometry of the polygon area.

The shapefile for the Palestinian communities also has the “Community Name” attribute. The separation wall shapefile includes the attribute of the “Status” of the wall (constructed, under construction, and planned). The WB Road network shapefile contains an attribute “Status” of the road segment, either paved or unpaved, and “Road Type”, to define the classification of the road network (local, regional, main, internal, and settlement).

The collected data then needed to be prepared. This phase consisted of three steps: (i) the creation of a geodatabase; (ii) the construction of the network topology; and (iii) building the network dataset.

• Creation of Geo-database

A geo-database is the native data structure used in ArcGIS and is the primary data format used to edit and manage the data. A Geodatabase can be a personal, file, or enterprise [15]. A personal Geo-database was created using ArcGIS 10.1, which operates a database that can store, query, and manage spatial and non-spatial data and contains data on the separation wall, road network, and communities.

• Building Network Topology

For accurate analysis, it is necessary to build a topology of the road network to discover errors in the data and correct them. This was performed by applying topology rules in order to ensure that there were no dangles in the road network, and that the roads did not intersect or overlap.

• Building Network Dataset

Creating the road network dataset required a geodatabase containing a line feature class stored in a feature dataset. The network dataset consisted of the set of edges representing the links over which agents travel and the set of junctions which connect those edges and facilitate navigation from one edge to another.

2.3. Network Analysis: Best Route Analysis

The best route can be the quickest, shortest, or most scenic route [15]. For example, if the impedance is time, then the best route is the fastest route. Hence, the best route can be defined as the route that has the lowest impedance. In this research, the impedance is the distance, so the best route is the shortest route.

Different methods have been proposed to analyze the impact of natural or man-made events on urban mobility. For example, Huang [21] used a hazard graph model to study the spatial pattern of flood impact on urban mobility performance using space syntax theory. Ghandour et al. [22] provided a visual analysis of safety hazards on the roads network in Lebanon using spatial autocorrelation and spatial clustering theories such as the Global G method, Getis–Ord, and Hot Spot Analysis. Ahmed [15] used network analysis to identify the best route from an incident to any healthcare service providers in the Greater Cairo metropolitan area. Other models have used time-series data to predict future hazard trends on road networks. For example, [23] employed the Autoregressive Integrated Moving Average (ARIMA) model to explain and predict the trend of traffic crashes in Palestine. Finally, Dsca et al. [24] used Dijkstra’s algorithm and the Global Positioning System (GPS) to determine the accumulated cost between any two nodes of a road network. Since this method is simple and well adapted to existing data, it was used in this research. This method has been used recently by [25] to find the shortest path for tsunami evacuation. [26] highlighted the capacity of Dijkstra’s develop an algorithm to find the shortest route from a given vertex to any other vertex. Based on previous research findings [24,25,26], Dijkstra’s algorithm was used in this research because of its simplicity and ability to find the shortest route between any two locations in the Palestinian territory.

2.4. Impact of the Mobility Restrictions on Travel Time and Delay

The impact of the separation wall on the mobility between two vertexes was estimated using the difference between the route in the presence of the wall (D_W) and the route without the wall (D₀). The increases in the route length (ID_W) and in the transport time (TD_W) due to the wall were calculated as follows:

ID_W = D_W − D₀

(1)

TDW = IDW/AvS

(2)

where AvS denotes the average traffic speed.

Checkpoints have different forms: permanent checkpoints, partial checkpoints, road gates, earth mounds, road barriers, and controlled tunnels. The time delay due to checkpoints (TD_CP) was obtained from [18]. Table 1 summarizes the average values of this delay at the main Checkpoints in the Palestinian territory.

2.5. Impact of the Mobility Restrictions on the Population and the Environment

The additional travel time due to the mobility restrictions (TD_tota) is equal to the sum of the time delay due to the wall (TD_W) and that due to the checkpoints (TD_CP):

TDtotal = TDW + TDCP

(3)

The impact on the environment concerns both energy consumption and greenhouse gas emissions. The determination of the additional energy consumption due to the separation wall (E_W) was carried out as follows:

$${\mathrm{E}}_{\mathrm{W}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{ID}}_{\mathrm{Wi}}·{\mathrm{ECm}}_{\mathrm{i} }$$

(4)

where n designates the number of vehicles concerned by the separation wall, ID_wi is the additional distance of vehicle i due to the separation wall (Equation (2)), and ECm_i denotes the energy consumption per km of vehicle i.

The calculation of the energy consumption due to the checkpoints (E_CP) was carried out using (Equation (5)):

$${\mathrm{E}}_{\mathrm{CP}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{TD}}_{\mathrm{CPi}}·{\mathrm{ECs}}_{\mathrm{i} }$$

(5)

where TD_CP is the time delay at the checkpoint and ECs is the rate of energy consumption of idle or slowly moving vehicles.

The CO₂ emissions due to the separation wall (CO_2W) and to the checkpoints (CO_2CP) were determined from the related energy consumptions as follows:

$${\mathrm{CO}}_{\mathrm{2W}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{ID}}_{\mathrm{Wi}}·{\mathrm{EC}}_{\mathrm{mi} }· \mathrm{CO}2{\mathrm{F}}_{\mathrm{i}}$$

(6)

$${\mathrm{CO}}_{\mathrm{2CP}}={{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{D}}_{\mathrm{CPi}}·{\mathrm{EC}}_{\mathrm{si} }· \mathrm{CO}2\mathrm{Fi}$$

(7)

where CO₂Fi is the CO₂ emission factor of vehicle i, which depends on the type and energy use of the vehicle.

3. Results and Discussion

3.1. Presentation of the Case Study

The methodology presented in Section 2 was applied to the Qalqilya governorate in Palestine, which covers a total land area of 166 km² with about 121,671 inhabitants [27,28]. According to the Palestinian Central Bureau of Statistics, this governorate has around 5752 gasoline vehicles and 7320 diesel vehicles [29]. The city of Qalqilya is the economic and administrative hub of this governorate. It is encircled by the separation wall, which constitutes a physical barrier between the city and other localities of the governorate, as illustrated in Figure 2.

According to a recent report of the Palestinian Central Bureau of Statistics and the Ministry of Transport [29], the Qalqilya governorate has around 2927 gasoline vehicles and 3717 diesel vehicles, consisting of 76.6% private cars, 15.4% trucks and commercial cars, and 5% taxis.

The Qalqilya governate suffers from about 40 checkpoints, including three earth mounds, five closed road gates, fourteen agricultural gates, one roadblock, three partial checkpoints, five tunnel checkpoints, and five watchtowers [17,18]. Figure 2 shows the localization of these checkpoints. This study focused on the Jaljoulia permanent checkpoint, which is located at the entrance of Qalqilya city. It concerns the mobility of people working in areas beyond this checkpoint and the urban center services for people living in the surrounding localities.

3.2. Impact of Mobility Restrictions on Route Length and Travel Time

3.2.1. Impact of the Separation Wall

The methodology presented in Section 2 was used to determine the best route between Qalqilya and other localities with and without the separation wall. Figure 3 shows the shortest paths determined without (Black line) and with (Green line) the separation wall. This indicates that the wall causes an increase in the length of the travel route.

Table 3. Impact of the wall on the best route length between Qalqilya city and other localities.

Destination	Travel Distance without the Wall D0 (km)	Travel Distance with the Wall DW (km)	Increase in the Travel Distance Due to the Wall IDW (km)
Kafr Laqif	7.5	12	4.5
Beit Amin	6.5	16.5	10
A’zzun A’tma	8.6	18.2	9.6
Jinsafut	12	17.5	5.5
Hajja	8.6	17.3	8.7
Kafr Qaddum	15.8	19.6	3.8
Jayyus	9.2	12.5	3.3
Sanniriya	6	16.1	10.1
Kafr Thulth	6.5	12.4	5.9
Al-Mudawwar	4.6	16.1	11.5
Ad Daba	4	22.6	18.6
Ras at Tira	3.6	23.1	19.5

summarizes the impact of the separation wall on the length of the best route between the city of Qalqilya and 12 localities. It shows that the wall increases the route length for all localities by a distance which varies between 3.3 km (Jayyus) and 19.5 km (Ras at Tira). The average increase in the length of the traveling route is 9.2 km, compared to the average total length without the wall, which is 7.7 km.

Table 4. Impact of the wall on the travel time between Qalqilya city and other localities.

Destination	Travel Time without the Wall T0 (min)	Travel Time with the Wall TW (min)	Time Delays Due to the Wall TDW (min)
Kafr Laqif	9.0	13.4	5.4
Beit Amin	7.8	19.8	12.0
A’zzun A’tma	10.3	21.8	11.5
Jinsafut	14.4	21.0	6.6
Hajja	10.3	20.8	10.5
Kafr Qaddum	19.0	13.5	4.5
Jayyus	11.0	15.0	4.0
Sanniriya	7.2	19.3	12.1
Kafr Thulth	7.8	14.9	7.1
Al-Mudawwar	5.5	19.3	13.8
Ad Daba	4.8	27.1	22.3
Ras at Tira	4.3	27.7	23.4

summarizes the impact of the separation wall on the travel time between Qalqilya city and other localities. The calculation was conducted with an average traffic speed of 50 km/h [30]. It can be observed that the wall increases the travel time for all the localities. The additional travel time varies from 4.0 min (Jayyus) to 23.4 min (Ras at Tira), with an average value of 11.1 min compared to the average transport time without the wall, which is 9.3 min.

3.2.2. Impact of the Checkpoints

According to [18], the time delay at the main checkpoint in the Qalqilya (TD_CP) governate is 89 min.

Table 5. Impact of the checkpoints on the travel time between Qalqilya city and other localities.

Destination	Traveling Time without the Checkpoint T0 (min)	Traveling Time with the Checkpoint TCP (min)	Ratio TCP/T0
Kafr Laqif	9.0	98.0	10.9
Beit Amin	7.8	96.8	12.4
A’zzun A’tma	10.3	99.3	9.6
Jinsafut	14.4	103.4	7.1
Hajja	10.3	99.3	9.6
Kafr Qaddum	19.0	108.0	5.7
Jayyus	11.0	100.0	9.1
Sanniriya	7.2	96.2	13.4
Kafr Thulth	7.8	96.8	12.4
Al-Mudawwar	5.5	94.5	17.2
Ad Daba	4.8	93.8	19.6
Ras at Tira	4.3	93.3	21.7

provides the ratio between travel time with the checkpoint (T_CP) and without the checkpoint (T₀) for all the localities. This ratio varies from 5.7 (Kafr Kaddum) to 21.7 (Ras at Tira), with an average value of 12.3.

Table 6. Impact of the mobility restrictions on the travel time between Qalqilya city and localities.

Locality	Travel Time without Restrictions (T0) (Minutes)	Travel Time with the Restrictions (TMR) (Minutes)	TMR/T0
Kafr Laqif	9	103.4	11.48
Beit Amin	7.8	108.8	13.94
A’zzun A’tma	10.32	110.84	10.74
Jinsafut	14.4	110	7.63
Hajja	10.32	109.76	10.63
Kafr Qaddum	18.96	112.52	5.93
Jayyus	11.04	104	9.42
Sanniriya	7.2	108.32	15.04
Kafr Thulth	7.8	103.88	13.32
Al-Mudawwar	5.52	108.32	19.62
Ad Daba	4.8	116.12	24.19
Ras at Tira	4.32	116.72	27.018

provides the impact of the total mobility restrictions on travel time between Qalqilya city and other localities. It shows that the checkpoints account for a significant part of travel delays. It also shows that mobility restrictions lead to a substantial increase in travel time. The ratio between the travel time with mobility restrictions (T_MR) and the travel time without mobility restrictions is between 5.93 (Kafr Qaddum) and 27.01 (Ras at Tira), with an average value of 14.08. The dramatic increase in travel time due to the mobility restrictions disturbs inhabitants’ mobility and access to work and health, educational, and social services. It also causes economic losses related to time loss and rise in fuel consumption.

3.3. Impact of the Mobility Restrictions on the Environment

This section presents the effects of the mobility restrictions on the environment, focusing on energy consumption and CO₂ emissions.

3.3.1. Impact on Energy Consumption

The influence of the wall on energy consumption (Ew) is determined by Equation (4), using the increase in the travel distance due to the wall (ID_W) (Table 3) and the average energy consumptions of gasoline and diesel vehicle in Palestine [31] (

Table 7. Average vehicle energy consumption in Palestine [31] and average energy consumption of idle or slowly moving vehicles [32,33].

Vehicle	Average Vehicle Energy Consumption in Palestine (L/km)	Average Energy Consumption of Idle or Slowly Moving Vehicles (Liter/min)
Gasoline	0.129	0.033
Diesel	0.107	0.049

).

The energy consumption due to a checkpoint (E_CP) was determined assuming that the vehicle consumes energy during 30% of the delay time at the checkpoint (TD_CP = 89 min), and the average energy consumption of idle or slowly moving vehicles (Table 7) [32,33]. According to (Equation (5)), E_CP = 0.8811 L.

Table 8. Impact of mobility restrictions on energy consumption (gasoline vehicles).

Locality	E0 (L)	EW (L)	Etotal (L) E0 + Ew + ECP	Ratio Etotal/E0
Kafr Laqif	0.9675	0.5805	2.4291	2.51
Beit Amin	0.8385	1.29	3.0096	3.58
A’zzun A’tma	1.1094	1.2384	3.2289	2.91
Jinsafut	1.548	0.7095	3.1386	2.02
Hajja	1.1094	1.1223	3.1128	2.80
Kafr Qaddum	2.0382	0.4902	3.4095	1.67
Jayyus	1.1868	0.4257	2.4936	2.10
Sanniriya	0.774	1.3029	2.958	3.82
Kafr Thulth	0.8385	0.7611	2.4807	2.82
Al-Mudawwar	0.5934	1.4835	2.958	4.98
Ad Daba	0.516	2.3994	3.7965	7.35
Ras at Tira	0.4644	2.5155	3.861	8.31

provides, for all of the different localities, the energy consumption of a gasoline vehicle with the wall and the checkpoint (E_total), including the energy consumption without the wall (E₀), and due to both the wall (E_W) and the checkpoint (E_CP). It shows that the increase in energy consumption due to the wall varies between 0.42 L (Jayyus) and 2.51 L (Ras at Tira), with an average value of 1.19 L, be compared with the average value of without the wall (E₀ = 0.99 L). The ratio between the energy consumption with the wall and the checkpoint (T_total) to the energy consumption without mobility restrictions (E₀) varies between 1.67 (Kafr Qaddum) and 8.31 (Ras at Tira), with an average value of 3.75. These results show that the mobility restrictions have a significant influence on energy consumption, which results in a substantial increase in both CO₂ emissions and energy expenses for inhabitants.

Table 9. Impact of mobility restrictions on energy consumptions (diesel vehicles).

Locality	E0 (L)	EW (L)	Etotal (L) E0 + Ew + ECP	Ratio Etotal/E0
Kafr Laqif	0.8025	0.4815	2.5923	3.23
Beit Amin	0.6955	1.07	3.0738	4.41
A’zzun A’tma	0.9202	1.0272	3.2557	3.53
Jinsafut	1.284	0.5885	3.1808	2.47
Hajja	0.9202	0.9309	3.1594	3.43
Kafr Qaddum	1.6906	0.4066	3.4055	2.01
Jayyus	0.9844	0.3531	2.6458	2.68
Sanniriya	0.642	1.0807	3.031	4.72
Kafr Thulth	0.6955	0.6313	2.6351	3.78
Al-Mudawwar	0.4922	1.2305	3.031	6.15
Ad Daba	0.428	1.9902	3.7265	8.7
Ras at Tira	0.3852	2.0865	3.78	9.81

summarizes the impact of the mobility restrictions on the energy consumptions of a diesel vehicle. The energy consumption due to the checkpoint (E_CP) was evaluated following the method presented in the previous section, giving E_CP = 1.3083 L. These results show that the environmental impact of mobility restrictions is higher for diesel vehicles than for gasoline vehicles. The ratio between the energy consumption with the wall and the checkpoint (T_toal) to the energy consumption without mobility restrictions (E₀) varies between 2.01 (1.67 for the gasoline vehicle) and 9.81 (8.31 for the gasoline vehicles), with an average value of 4.58 (3.75 for the gasoline vehicles).

3.3.2. Impact of Mobility Restrictions on CO₂ Emissions

This section will focus on CO₂ emissions, as they constitute vehicles’ primary greenhouse gas emission. CO₂ emissions due to the wall (CO_2W) were determined using Equation (6), with the energy consumptions presented in Table 8 and Table 9 and the CO₂ emissions factors used by [31] (

Table 10. CO₂ emissions factor (CO₂Fi) [31].

Gasoline (gm/L)	Diesel (gm/L)
2598	2925

). The CO₂ emissions due to Jaljoulia checkpoint were determined using the energy consumption related to this checkpoint as defined in the previous section and the CO₂ emissions factors in Table 10. Calculation using Equation (7) gave CO_2CP = 2.289 and 3.877 kg CO₂ for gasoline and diesel vehicles, respectively.

Table 11. Impact of mobility restrictions on CO₂ emissions (gasoline vehicles).

Locality	CO20 (g)	CO2W	CO2 total (g) CO20 + CO2W + CO2CP	Increase (%)
Kafr Laqif	2514	1508	6311	151
Beit Amin	2178	3351	7819	259
A’zzun A’tma	2882	3217	8389	191
Jinsafut	4022	1843	8154	103
Hajja	2882	2916	8087	181
Kafr Qaddum	5295	1274	8858	67
Jayyus	3083	1106	6478	110
Sanniriya	2011	3385	7685	282
Kafr Thulth	2178	1977	6445	196
Al-Mudawwar	1542	3854	7685	398
Ad Daba	1341	6234	9863	636
Ras at Tira	1207	6535	10031	731

summarizes the impact of the mobility restrictions on the CO₂ emissions of a gasoline vehicle. It shows that the mobility restrictions cause a significant increase in CO₂ emissions, which varies between 67% (Kafr Qaddum) and 731% (Ras at Tira), with an average value of 275%.

Table 12. Impact of mobility restrictions on CO₂ emissions (diesel vehicles).

Locality	CO20 (g)	CO2W	CO2 total (g) CO20 + CO2W + CO2CP	Increase (%)
Kafr Laqif	2347	1408	7582	223
Beit Amin	2034	3130	8991	342
A’zzun A’tma	2692	3005	9523	254
Jinsafut	3756	1721	9304	148
Hajja	2692	2723	9241	243
Kafr Qaddum	4945	1189	9961	101
Jayyus	2879	1033	7739	169
Sanniriya	1878	3161	8866	372
Kafr Thulth	2034	1847	7708	279
Al-Mudawwar	1440	3599	8866	516
Ad Daba	1252	5821	10900	771
Ras at Tira	1127	6103	11057	881

summarizes the results obtained for diesel vehicles. It shows that the impact of the mobility restrictions on the CO₂ emissions of a diesel vehicle is greater than that of a gasoline vehicle. The increase in CO₂ emissions of a diesel vehicle due to the mobility restrictions varies between 101% (67% for the gasoline vehicle) and 881% (731% for the gasoline vehicle), with an average value of 358% (275% for the gasoline vehicle).

4. Conclusions

This paper has presented a quantitative analysis of the impact of the mobility restrictions in the Palestinian territory on both the population and the environment. The research was conducted for two types of mobility barriers: the separation wall and checkpoints. These physical barriers have fragmented the Palestinian territory into disconnected cantons. They result in a significant increase in the time and distance required for inter-urban transport, and consequently in energy consumption and CO₂ emissions.

Research conducted with data on the Qalqilya governorate showed that the ratio between the travel time with mobility restrictions and without mobility restrictions was between 5.93 and 27.01, with an average value of 14.08. This increased travel time causes time loss, anxiety, and additional expenses for the population.

Mobility restrictions induce a significant increase in energy consumptions and CO₂ emissions. The rise in energy consumption due to mobility restrictions is between 67% and 731% for gasoline vehicles, with an average value of 275%. CO₂ emissions follow the same increasing trend. The impact of the mobility restrictions on diesel vehicles is even higher, with an increase in CO₂ emissions is of between 101% and 881%, and an average value of 358%.

The authors are aware of the research limitations related to the use of average values for vehicles as concerns mobility restrictions. This limitation is due to a lack of detailed data about individual vehicles. In the future, similar research could be enhanced by following the methodology proposed in [34], and by the use of mobile crowdsourcing to collect detailed data about the vehicles affected by the mobility restrictions, including their type, energy consumption, and route.