Links between the Energy Intensity of Public Urban Transport, Regional Economic Growth and Urbanisation: The Case of Poland
Abstract
:1. Introduction
1.1. Presentations of Research Problems
1.2. Organisation of the Paper
- Does the regional economic development of voivodeships affect the energy intensity of public urban transport?
- Does urbanisation shape energy consumption patterns in public urban transport (and are there agglomeration effects)?
- Does the level of urbanisation of voivodeships depend on their regional level?
- The dominance of public transport in the leading cities in the urban complex causes the polarisation of subregions into more and less developed and more and less energy-intensive areas.
- The increase in the accessibility of public transport in leading areas causes agglomeration effects between neighbouring areas and a strong dependence of economic growth on energy consumption by public transport.
2. Brief Literature Review
2.1. Characteristics of Urbanisation
2.2. Economic Growth and CO2 Emissions
2.3. Environmental Kuznets Curve (EKC)
2.4. Factors of Energy Intensity of Transport
3. Materials and Methods
3.1. Data Explanation
3.2. The Case of Poland
3.3. Methodology
- Does the regional economic development of voivodeships affect the energy intensity of public urban transport?
- Does urbanisation shape energy consumption patterns in public urban transport (and are there agglomeration effects)?
- Does the level of urbanisation of voivodeships depend on their regional level?
- The dominance of public transport in the leading cities in the urban complex causes the polarisation of subregions into more and less developed and more and less energy-intensive areas.
- The increase in the accessibility of public transport in leading areas causes agglomeration effects between neighbouring areas and a strong dependence of economic growth on energy consumption by public transport.
- Within-dimension; and
- Between dimension.
4. Results and Discussion
4.1. Findings and Explanations
4.2. Polemic Discourse on Relationships between the Energy Intensity of Public Urban Transport, Regional Economic Growth and Urbanisation
- Urban transport is heavily reliant on fossil fuels;
- The car’s dominant position in urban travel;
- The increasing extension of metropolitan areas, resulting in ongoing reliance on the passenger automobile and a rise in the distance between traffic sources and destinations;
- The impact of the transport sector on climate change; and
- Externalities (among others: pollution in cities, intensive road traffic and its noise and traffic safety).
- Integrating public transport in agglomerations, which will enable passengers to flexibly use various means of transport;
- Shortening travel time by public transport;
- Inducing a change during the journey, when it is done in a non-intrusive manner and ensures the shortening of door-to-door travel time;
- Maintaining an attractive tariff offer in such a way that the cost of travelling by public urban transport is much lower than by car;
- High spatial accessibility of public urban transport and transfer points;
- Easy access to information on timetables and transfer points; and
- Increasing the level of passenger comfort in public urban transport.
5. Conclusions
- Raise the vehicle emission standards, both for individual cars and public transport vehicles as well as for new vehicles, and phase out aged and high-emission vehicles.
- The creation of transport policy should have its origins in the policy of sustainable development (including energy). There should be a relationship in the basis for creating an economic policy with its environmental and energy policy objectives.
- Development of pricing policy and strategy in public transport, which gives the opportunity of commuters to choose the more effective way of transport, especially when commuting from suburban areas.
- The necessity to create an efficient way of promoting environmentally friendly policies and optimisation of energy use for mobility in suburban areas.
- There is a need for investment in urban infrastructure in order to keep energy efficient transport system.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Type of Transport | Factors | Spatial Scope | Time Scope | Source |
---|---|---|---|---|
Passenger transport with various vehicles: car, walk, bus, cycle, rail/DART/Luas, taxi/hackney, lorry/motorcycle and other |
| Ireland | 2009–2019 | [76] |
Passenger transport with various vehicles: metro, own vehicle (zero, one, two) (four-wheeled vehicle (i.e., hatchback, car, MPV, SUV, minivan, van and pickup), not including the motorcycle) |
| Philippines: Manila | from April to May 2017 | [85] |
Passenger transport, various vehicles: car, plane, bus and trains |
| European Union (EU28) | 2015–2050 | [86] |
Road passenger transport: various vehicles |
| Thailand | 2007–2017 | [55] |
Road passenger transport: car, bus, motorcycle, bicycle and walking |
| China: Changzhou | 2008 | [73] |
Road passenger transport: cars |
| European Union | 2021–2022 | [87] |
Public transport |
| Belgium | 1980–2020 | [72] |
Public transport |
| various: about 20 | 1990–2020 | [80] |
Public transport |
| 284 prefecture-level cities in mainland China | 2013–2015 | [88] |
Urban transport |
| China, Beijing | no data | [49] |
Variable | Unit | Abbreviation | Type | Databases |
---|---|---|---|---|
Energy intensity of public urban transport | MJ/capita | EI | Response variable | Own calculations based on [89,90] |
Gross domestic product per capita | PLN/capita in current prices | GDPC | Explanatory variable | [89] |
Gross domestic product per capita squared | GDPC2 in PLN2/capita2 | GDP2C | Explanatory variable | [89] |
Urbanisation rate | % | UR | Explanatory variable | [89] |
The average energy efficiency of buses | MJ/km | EE1 | The component used to calculate the EI of public urban transport | [90] |
The average energy efficiency of trams | MJ/km | EE2 | The component used to calculate the EI of public urban transport | [90] |
The average energy efficiency of trolleybuses | MJ/km | EE3 | The component used to calculate the EI of public urban transport | [90] |
Average annual mileage of buses | km | KM1 | The component used to calculate the EI of public urban transport | [89] |
Average annual mileage of trams | km | KM2 | The component used to calculate the EI of public urban transport | [89] |
Average annual mileage of trolleybuses | km | KM3 | The component used to calculate the EI of public urban transport | [89] |
Number of rolling stock of buses | pcs. | NR1 | The component used to calculate the EI of public urban transport | [89] |
Number of rolling stock of trams | pcs. | NR2 | The component used to calculate the EI of public urban transport | [89] |
Number of rolling stock of trolleybuses | pcs. | NR3 | The component used to calculate the EI of public urban transport | [89] |
Energy consumption of buses | GJ | EC1 | The component used to calculate the EI of public urban transport | Own calculations based on [89,90] |
Energy consumption of trams | GJ | EC2 | The component used to calculate the EI of public urban transport | Own calculations based on [89,90] |
Energy consumption of trolleybuses | GJ | EC3 | The component used to calculate the EI of public urban transport | Own calculations based on [89,90] |
Population | pers. | POP | The component used to calculate the EI of public urban transport | [89] |
Provinces | EI | GDPC | GDP2C | UR | ||||
---|---|---|---|---|---|---|---|---|
Average (MJ/Capita) | Coefficient of Variation (%) | Average (PLN/Capita) | Coefficient of Variation (%) | Average (PLN2/Capita2) | Coefficient of Variation (%) | Average (%) | Coefficient of Variation (%) | |
Greater Poland | 321.36 | 6.34 | 51,950.09 | 17.40 | 2,773,063,454.64 | 34.87 | 54.85 | 1.28 |
Kuyavian-Pomeranian | 347.97 | 5.33 | 39,295.18 | 15.74 | 1,578,905,488.45 | 31.93 | 59.63 | 1.09 |
Lesser Poland | 400.50 | 5.59 | 43,412.55 | 17.51 | 1,937,196,586.55 | 35.10 | 48.55 | 0.76 |
Lodzkie | 483.25 | 6.52 | 45,279.36 | 17.31 | 2,106,053,025.73 | 35.27 | 63.05 | 0.91 |
Lower Silesia | 358.71 | 5.90 | 53,619.45 | 14.95 | 2,933,487,997.09 | 30.24 | 69.07 | 0.89 |
Lublin | 225.66 | 6.47 | 33,453.45 | 15.57 | 1,143,784,186.18 | 31.45 | 46.36 | 0.28 |
Lubuskie | 201.07 | 5.56 | 40,086.73 | 15.04 | 1,639,989,717.45 | 30.30 | 64.15 | 1.32 |
Masovian | 659.64 | 10.02 | 77,070.55 | 16.57 | 6,088,127,428.91 | 33.43 | 64.30 | 0.21 |
Opolskie | 170.66 | 9.06 | 38,745.55 | 15.15 | 1,532,555,781.36 | 30.75 | 52.49 | 1.04 |
Podkarpackie | 175.62 | 6.78 | 33,960.82 | 16.04 | 1,180,325,094.09 | 32.24 | 41.26 | 0.25 |
Podlaskie | 301.70 | 8.61 | 35,009.27 | 16.32 | 1,255,310,727.64 | 33.37 | 60.55 | 0.45 |
Pomeranian | 417.17 | 4.85 | 46,487.18 | 16.15 | 2,212,273,418.09 | 32.48 | 64.52 | 1.43 |
Silesian | 566.22 | 7.42 | 50,155.55 | 14.69 | 2,564,918,727.36 | 29.69 | 77.11 | 0.65 |
Swietokrzyskie | 386.04 | 20.39 | 35,350.27 | 14.62 | 1,273,927,489.91 | 30.04 | 44.87 | 0.58 |
Warmian-Masurian | 185.45 | 10.18 | 34,244.55 | 14.87 | 1,196,262,821.27 | 30.24 | 59.20 | 0.27 |
West Pomeranian | 389.47 | 4.73 | 40,577.00 | 15.98 | 1,684,729,176.45 | 32.42 | 68.61 | 0.21 |
Variable | Test for a Unit Root in 1st Difference | ||
---|---|---|---|
Method | Statistic | Prob. | |
EI | Levin, Lin and Chu t | −11.7584 | 0.0000 |
Im, Pesaran and Shin W-stat | −6.2791 | 0.0000 | |
ADF—Fisher Chi-square | 103.4630 | 0.0000 | |
PP—Fisher Chi-square | 132.1610 | 0.0000 | |
GDPC | Levin, Lin and Chu t | −8.0529 | 0.0000 |
Im, Pesaran and Shin W-stat | −2.0709 | 0.0192 | |
ADF—Fisher Chi-square | 68.1619 | 0.0002 | |
PP—Fisher Chi-square | 122.0060 | 0.0000 | |
GDP2C | Levin, Lin and Chu t | −6.6003 | 0.0000 |
Im, Pesaran and Shin W-stat | 1.9041 | 0.9716 | |
ADF—Fisher Chi-square | −1.4534 | 0.0731 | |
PP—Fisher Chi-square | 90.3566 | 0.0000 | |
UR | Levin, Lin and Chu t | −16.2017 | 0.0000 |
Im, Pesaran and Shin W-stat | −4.4527 | 0.0000 | |
ADF—Fisher Chi-square | 75.2130 | 0.0000 | |
PP—Fisher Chi-square | 75.9187 | 0.0000 |
Alternative Hypothesis: Common AR Coefs. (Within-Dimension) | ||||
---|---|---|---|---|
Items | Statistic | Prob. | Weighted Statistic | Prob. |
Panel v-Statistic | 0.0800 | 0.4681 | −1.4632 | 0.9283 |
Panel rho-Statistic | 1.1705 | 0.8791 | 0.5259 | 0.7005 |
Panel PP-Statistic | −4.8228 | 0.0000 | −8.4247 | 0.0000 |
Panel ADF-Statistic | −4.9171 | 0.0000 | −3.1796 | 0.0007 |
Alternative Hypothesis: Individual AR Coefs. (Between-Dimension) | ||||
Items | Statistic | Prob. | ||
Group rho-Statistic | 2.5246 | 0.9942 | ||
Group PP-Statistic | −10.5306 | 0.0000 | ||
Group ADF-Statistic | −3.2844 | 0.0005 |
Items | EI | GDPC | GDP2C | UR |
---|---|---|---|---|
EI (−1) | 0.9790 | 0.5230 | 78,887.0963 | −0.0002 |
(0.0219) | (0.8138) | (99,908.2625) | (0.0002) | |
[44.6792] | [0.6427] | [0.7896] | [−1.1924] | |
GDPC (−1) | −0.0002 | 1.1100 | 8561.7841 | −2.08 × 10−5 |
(0.0011) | (0.0394) | (4841.0987) | (8.3 × 10−6) | |
[−0.1935] | [28.1499] | [1.7686] | [−2.5127] | |
GDP2C (−1) | −2.14 × 10−9 | −5.66 × 10−7 | 1.0171 | 1.94 × 10−10 |
(9.6 × 10−9) | (3.7 × 10−7) | (0.0439) | (7.5 × 10−11) | |
[−0.2226] | [−1.5825] | [23.1631] | [2.5870] | |
UR (−1) | −0.1377 | −12.0600 | −1,423,994.5435 | 0.9996 |
(0.2859) | (10.6186) | (1,303,703.4377) | (0.0022) | |
[0.4815] | [−1.1357] | [−1.0923] | [448.5530] | |
C | 6.5394 | −941.2864 | −141,974,915.3680 | 0.5310 |
(25.4594) | (945.4870) | (116,082,509.5017) | (0.1984) | |
[0.2569] | [−0.9956] | [−1.2231] | [2.6763] | |
Adj. R-squared | 0.9583 | 0.9932 | 0.9916 | 0.9995 |
Log-likelihood | −761.4894 | −1339.8280 | −3214.7260 | 15.2233 |
Null Hypothesis: | W-Stat. | Zbar-Stat. | Prob. |
---|---|---|---|
GDPC does not homogeneously cause EI | 4.3617 | 4.2310 | 2.0 × 10−5 |
EI does not homogeneously cause GDPC | 2.7880 | 1.9828 | 0.0474 |
GDP2C does not homogeneously cause EI | 4.4810 | 4.4014 | 1.0 × 10−5 |
EI does not homogeneously cause GDP2C | 2.8991 | 2.1415 | 0.0322 |
UR does not homogeneously cause EI | 5.9101 | 6.4429 | 1.0 × 10−10 |
EI does not homogeneously cause UR | 1.9602 | 0.8002 | 0.4236 |
UR does not homogeneously cause GDPC | 1.8819 | 0.6884 | 0.4912 |
GDPC does not homogeneously cause UR | 4.9190 | 5.0271 | 5.0 × 10−7 |
UR does not homogeneously cause GDP2C | 2.1752 | 1.1075 | 0.2681 |
GDP2C does not homogeneously cause UR | 5.1051 | 5.2931 | 1.0 × 10−7 |
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Kłos-Adamkiewicz, Z.; Szaruga, E.; Gozdek, A.; Kogut-Jaworska, M. Links between the Energy Intensity of Public Urban Transport, Regional Economic Growth and Urbanisation: The Case of Poland. Energies 2023, 16, 3799. https://doi.org/10.3390/en16093799
Kłos-Adamkiewicz Z, Szaruga E, Gozdek A, Kogut-Jaworska M. Links between the Energy Intensity of Public Urban Transport, Regional Economic Growth and Urbanisation: The Case of Poland. Energies. 2023; 16(9):3799. https://doi.org/10.3390/en16093799
Chicago/Turabian StyleKłos-Adamkiewicz, Zuzanna, Elżbieta Szaruga, Agnieszka Gozdek, and Magdalena Kogut-Jaworska. 2023. "Links between the Energy Intensity of Public Urban Transport, Regional Economic Growth and Urbanisation: The Case of Poland" Energies 16, no. 9: 3799. https://doi.org/10.3390/en16093799
APA StyleKłos-Adamkiewicz, Z., Szaruga, E., Gozdek, A., & Kogut-Jaworska, M. (2023). Links between the Energy Intensity of Public Urban Transport, Regional Economic Growth and Urbanisation: The Case of Poland. Energies, 16(9), 3799. https://doi.org/10.3390/en16093799