# A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network

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## Abstract

**:**

## 1. Introduction

_{2}emitter in the world [6], China aims to achieve 60–65% carbon intensity reduction by 2030 (compared to 2005) and to reach carbon neutrality around 2060 [7]. Considering China’s environment and the carbon emission of the aviation industry, it is urgent to solve the contradiction between the development of the aviation industry and the environmental crisis.

_{2}emissions while improving service levels. These studies recognized the critical role played by adjusting aircraft type and route network. Müller, C. et al. [11] investigated the impacts of emission thresholds and retrofit options on airline flight plans with an optimization model. Parsa, M. et al. [12] designed a hub-and-spoke route network using a multi-objective mixed integer planning model. According to data from the U.S. aviation sector, the network would not only save fuel costs but also reduce the U.S. aviation industry’s carbon dioxide emissions. Lozano, S. et al. [13] searched for a multi-objective data envelopment analysis approach that took environmental factors into account. Capaz, R.S. et al. [14] proposed a method to produce clean aviation fuel from waste.

## 2. Key Problem Statement

#### 2.1. Carbon Emission Trading Process

#### 2.2. The Game between Government and Airlines Based on the Emissions Trading Mechanism

## 3. Methodology

#### 3.1. Problems and Assumptions

#### 3.2. Model Formulation

- Minimize carbon emissions: The cap of carbon emission allowances in scene r is allocated into the free allowances (i.e., ${M}_{F}(r)$) and non-free allowances (i.e., ${M}_{P}(r)$). The airline’s carbon emissions can be expressed as the sum of the free credits and trading credits allocated to the airline by the government, which can be seen in Equation (7).$$U(r)={M}_{F}(r)+{M}_{P}(r)$$
- Allocation degree of satisfaction: The multi-objective optimization aims to obtain a set of trade-off solutions between contradictory objectives (carbon emissions and satisfaction) by adjusting free allowances. The allocation satisfaction of airlines reflects the airlines’ attitude toward the government, which is dependent on the number of free carbon allowances allocated to airlines by the government. In the upper layer, the objective function of satisfaction is set to the quotient of ${M}_{F}(r)$ and $ACG(r)$. The more free carbon allowances an airline receives, the higher the allocation satisfaction [41]. The allocation degree of satisfaction for each airline is defined as Equation (8).$$Y(r)=\hspace{0.17em}\frac{{M}_{F}(r)}{ACG(r)\hspace{0.17em}}$$
- Allocation of carbon credits by the government: As the policy maker in the field of carbon emission allowances, the main question for the government is how to achieve emissions reduction targets [42]. The government makes decisions based on its carbon reduction requirements and the airline’s total carbon emissions from the previous cycle. This leaves the percentage of the free allowances in scene r to the total allocated allowances to $\beta (r)$. This ratio is based on the airline’s carbon emissions in the previous year and the government’s reduction target [43].$$\beta (r)=\frac{{M}_{F}(r)}{{M}_{F}(r)+{M}_{P}(r)}$$
- Demand constraints: To ensure the operation of airlines, the free allowances allocated to airlines (i.e., ${M}_{F}(r)$) cannot be less than their minimum requirement to operate all flights (i.e., ${d}_{\mathrm{min}}(r)$). This demand is based on the airline’s flight schedule and passenger demand from the previous cycle, the mathematical expression can be seen in Equation (10).$${M}_{F}(r)\ge {d}_{\mathrm{min}}(r)$$
- Airlines’ aircraft selection plan: The government’s goal is to minimize the carbon emissions of all airlines, so the free carbon allowances that the government allocates to each airline are lower than their original carbon emission. Therefore, airlines must maximize their economic efficiency by adjusting their fleet planning options based on the number of carbon credits allocated.
- Economic benefit: Airlines’ revenue comes from the sale of carbon credits and ticket sales. The airfare revenue of aircraft type k in flight leg i in scenario r is calculated from the airfare for aircraft type k multiplied by the number of seats for itinerary j. If airlines do not fully use the free emission allocation allocated by the government, they may sell them. Airlines may also purchase additional emission credits if the free credits allocated to them do not meet their needs. For different airlines, the amount of carbon traded by the airline may equal either the number of carbon allowances sold or the number of carbon allowances purchased, so the product of the amount of carbon traded and the price of carbon may represent both costs and revenues. Fleet operating costs consist of variable operating costs (i.e., ${C}_{ik}$) and fixed costs (i.e., ${A}_{k}$). The variable operating cost per aircraft is called the variable cost, which is the variable cost of operating flight i with aircraft type k and is positively related to flight duration. The fixed cost is the average acquisition cost of the aircraft type k. Considering the effect of uncertainty, the airfare and the operating cost are set as fuzzy random parameters. Therefore, the operating benefit function for an airline in scenario r can be transformed as Equation (11). It should be noted that the costs mentioned in this paper must be directly related to the aircraft types and the accounting policy, economics, and aircraft utilization rate, which differ among countries around the world. So, the profit presented in this paper does not represent the net income of the airline.$$\mathrm{max}f(r)={\displaystyle \sum _{j\in J}{p}_{j}{s}_{j}(r)-}{\displaystyle \sum _{i\in I}{\displaystyle \sum _{k\in K}{X}_{ik}}}(r){C}_{ik}-TC\times {M}_{P}(r)-{\displaystyle \sum _{k\in K}{A}_{k}{Z}_{k}(r)}$$

- 7.
- Aircraft number constraints: To allow for the consistent management of aircraft, airlines must ensure that the overnight airport remains the same for each aircraft. The number of type k aircraft flying to the first node at each airport is equal to the number of type k aircraft flying to the last node at that airport, as shown in Equation (14). At the first node, the number of aircraft of a type waiting for orders at all airports is equal to the number of aircraft of that type in the fleet. This mathematical expression is shown in Equation (15).$$\sum _{v\in V}{y}_{kv,{t}^{-}=1}(r)=}{\displaystyle \sum _{v\in V}{y}_{kv,{t}^{+}=last}(r)},\forall k\in K,r\in R$$$${Z}_{\mathrm{k}}(r)={\displaystyle \sum _{v\in V}{y}_{kv,{t}^{-}=1}}(r)\forall \mathrm{k}\in K,r\in R$$
- 8.
- Aircraft selection constraints: Once the fleet plan is established, the flight type assignment must meet the requirement of uniqueness. For each airline, only one type of aircraft can be selected on the air route in scenario r, as shown in Equation (16).$$\sum _{\mathrm{k}\in K}{X}_{ik}(r)}=1,\forall \mathrm{i}\in I,r\in R$$
- 9.
- Aircraft flow balance constraints: To optimize the utilization of aircraft, airlines must minimize the number of unused aircraft at airports. In scenario r, the number of aircraft of a type entering a node at any airport must be equal to the number of aircraft of the same type leaving that node, as shown in Equation (17).$$\begin{array}{c}{\displaystyle \sum _{r\in R}{\mathrm{y}}_{kv{t}^{-}}(r)}-{\displaystyle \sum _{r\in R}{\mathrm{y}}_{kv{t}^{+}}(r)}+{\displaystyle \sum _{i\in IN(k,v,t)}{X}_{ik}(r)}-{\displaystyle \sum _{i\in OUT(k,v,t)}{X}_{ik}(r)}=0,\\ \\ \forall k\in K,v\in V,t\in T\end{array}$$
- 10.
- Passenger flow constraints: For each airline, the number of seats allocated on each flight leg in scene r (i.e., ${s}_{j}(r)$) does not exceed the total number of seats for that aircraft type k (i.e., $Ca{p}_{k}$). At the same time, the number of seats provided for itinerary j (i.e., ${s}_{j}(r)$) is within the passenger demand in that itinerary (i.e., ${n}_{j}$). These mathematical expressions are shown in Equations (18) and (19).$$\sum _{j\in J}{\delta}_{ij}{s}_{j}(r)\le {\displaystyle \sum _{k\in K}{\displaystyle \sum _{r\in R}Ca{p}_{k}{X}_{ik}(r)}}},\forall i\in I$$$$0\le {s}_{j}(r)\le {n}_{j}\forall j\in J$$
- 11.
- Fleet consistency constraints: For each scenario r, the optimal fleet planning may differ between different demand scenarios. Fleet planning is a long-term decision that means an airline cannot simply change the structure of its fleet at short notice. Therefore, the fleet planning obtained should combine all scenarios and be appropriate for the entire planning cycle. As shown in Equation (20), the fleet in different scenes r is restricted to the same as the program variables (i.e., ${\overline{Z}}_{k}$); it represents the fleet planning obtained by integrating all scenarios.$${Z}_{k}(r)={\overline{Z}}_{k},\forall k\in K,r\in R$$

## 4. Results and Discussion

#### 4.1. A Hub-and-Spoke Case Presentation

- (1)
- Let the demand on each itinerary j (j = 1, 2, …, J) yield to the normal distribution ${d}_{j}~N({\mu}_{j},{\sigma}_{j})$ with cumulative probability distribution function ${F}_{j}$.
- (2)
- Specify the number of scenarios to be generated (denoted by R).
- (3)
- Set the value of R equally spaced quantiles of the distribution j $({d}_{j}[1],{d}_{j}[2],\cdots {d}_{j}[R])$ so as to generate more values from a range with a higher density of distribution and fewer values from low-density regions.
- (4)
- Generate each vector $({d}_{1}[r],{d}_{2}[r],\cdots {d}_{J}[r])$ by randomly permuting of the values ${d}_{j}$ for each itinerary, which represents a scene that includes all itineraries.
- (5)
- Combine all vectors into a 5000 × 62 matrix, where each row vector represents a scene, and each column vector corresponds to the demands of an itinerary in all scenes.

#### 4.2. Operation Results and Analysis

#### 4.2.1. Baseline Scenario

#### 4.2.2. Impact of Environmental Policy

#### 4.2.3. Impact of Passenger Demand Level

#### 4.2.4. Impacts of Parameters on the Carbon Trading System

#### 4.2.5. Analyses of Fleet Plans for the Four Scenarios

#### 4.3. Political and Industrial Implications

- In terms of industrial development, the flexibility of the fleet is a key element for effective emissions reduction and profitability in the air transport industry. When emissions reductions and industry development are valued, the air transport industry should encourage airlines to increase the diversity of fleet types.
- For airlines, the bi-level programming-based equilibrium strategy can be a guidance tool to adjust fleet planning, which can achieve a balance between operating revenue and carbon emissions. The results revealed that green airline fleets kept the relevant revenue, effectively reduced carbon emissions, and was less affected by the volatility of carbon trading prices.
- For regional governments, a carbon trading mechanism is an effective measure for management to reduce carbon emissions, but this strategy could increase operating costs. Therefore, the satisfaction of airlines should be taken into account when the carbon trading mechanism is introduced to the airline industry. Under this scenario, operating profit and carbon emissions are both reduced, but carbon emissions are reduced to a greater extent.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Sets and indices | Description |

$I$ | Set of flight legs, indexed by i; |

$J$ | Set of passenger itineraries, indexed by j; |

$K$ | Set of aircraft types, indexed by k; |

$V$ | Set of airports, indexed by v; |

$T$ | Set of departure and arrival nodes, indexed by t; |

$R$ | Set of scenes, indexed by r; |

$IN(k,v,t)$ | Set of flight legs into airport v, flown by aircraft type k which arrive at node t; |

$OUT(k,v,t)$ | Set of flight legs into airport v, flown by aircraft type k which depart at node t; |

Parameters | Description |

$TC$ | Price of carbon on the carbon trading market; |

${p}_{j}$ | Passenger fare in itinerary j; |

${n}_{j}(r)$ | Travel demand in itinerary j in scene r; |

${C}_{ik}$ | Variable costs for flights i operated by aircraft type k; |

${A}_{k}$ | Fixed costs by aircraft type k; |

${\delta}_{ij}$ | Binary variables, where ${\delta}_{ij}$ is equal to 1 if itinerary j is assigned to leg i; |

$Ca{p}_{k}$ | Capacity of aircraft type k; |

${G}_{ik}(r)$ | The emissions from flight leg i by aircraft type k in scene r; |

${h}_{i}$ | The length of the air route i; |

${\mathrm{dis}}_{i}$ | The cruising distance of flight i; |

${\mathrm{ratio}}_{cr}{}^{k}$ | The quotient of the fuel consumption ratio and the lift-drag ratio; |

${v}_{k}$ | The cruising speed of aircraft type k; |

$\mathrm{aircraft}\hspace{0.17em}\mathrm{bare}\hspace{0.17em}{\mathrm{weight}}_{k}$ | Variable costs for flights i operated by aircraft type k; |

$\mathrm{load}\hspace{0.33em}\mathrm{factor}$ | A ratio of the enplaned passenger on an airplane to the airplane seat capacity; |

$\mathrm{number}\hspace{0.33em}\mathrm{of}\hspace{0.33em}{\mathrm{seats}}_{k}$ | The rated seat capacity of aircraft type k; |

${\mathrm{seat}}_{i}$ | The actual number of passengers on flight i; |

Decision variables | Description |

${M}_{F}(r)$ | The free allowances from the government in scene r; |

${M}_{P}(r)$ | The non-free allowances from the government in scene r; |

${X}_{ik}(r)$ | Binary variable; 1 denotes that aircraft type k is selected of the air route i in scene r, otherwise 0; |

${\mathrm{y}}_{kvt-}(r)$ | The Number of aircraft type k on the ground at airport v drive into node t in scene r; |

${\mathrm{y}}_{kvt+}(r)$ | The number of aircraft type k on the ground at airport v depart from node t in scene r; |

${s}_{j}(r)$ | The number of passengers for itinerary j in scene r; |

${Z}_{k}(r)$ | The number of aircraft type k in the fleet in scene r. |

ETS | Emissions trading scheme; |

EU-ETS | European Union Emissions Trading Scheme; |

CORSIA | Carbon Offsetting and Reduction Scheme for International Aviation; |

ICAO | International Civil Aviation Organization. |

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**Figure 7.**Comparison of the number of passengers between the scenarios in different carbon benchmark prices.

**Figure 8.**Left: carbon emissions of each aircraft type in four scenarios. Right: comparison of total carbon emissions in four scenarios.

Aircraft Type Index k | Description | |||||
---|---|---|---|---|---|---|

A320 | A330 | A340 | B767 | B737 | B777 | |

Number of passenger seats | 156 | 334 | 324 | 226 | 194 | 361 |

Weight (kg) | 142,400 | 229,600 | 229,000 | 180,127 | 160,000 | 239,225 |

Cruising speed (km/h) | 852 | 896 | 896 | 851 | 796 | 905 |

Lift–drag ratio | 0.327 | 0.296 | 0.288 | 0.356 | 0.201 | 0.297 |

Operating costs (CNY/h) | 34,000 | 65,000 | 62,000 | 43,000 | 40,000 | 68,000 |

Unit purchase cost (CNY) | 40,000 | 70,000 | 70,000 | 50,000 | 48,000 | 76,000 |

Scenario | Objective | Passenger Demand Level | Carbon Trading Price |
---|---|---|---|

S1 * | First: minimize carbon emissions; second: maximize profit | 100% | 50 CNY/ton |

S2 | Single objective (profit maximization) | 100% | - |

S3 | First: minimize carbon emissions; second: maximize profit | 160% | 50 CNY/ton |

S4 | First: minimize carbon emissions; second: maximize profit | 100% | 100 CNY/ton |

$\mathit{\omega}$ | MF | MP | U | Y | U/Y | |
---|---|---|---|---|---|---|

1 | 0.5 | 457,417 | 114,354 | 0.3844 | 0.1156 | 3.3253 |

2 | 0.6 | 471,453 | 117,863 | 0.3953 | 0.2047 | 1.9311 |

3 | 0.7 | 485,798 | 121,450 | 0.4042 | 0.2958 | 1.3666 |

4 | 0.8 | 499,787 | 124,946 | 0.4154 | 0.3846 | 1.0801 |

5 | 0.9 | 513,755 | 128,438 | 0.4267 | 0.4732 | 0.9017 |

6 | 1.0 | 527,680 | 131,920 | 0.4383 | 0.5616 | 0.7804 |

7 | 1.1 | 541,599 | 135,399 | 0.4499 | 0.6500 | 0.6922 |

8 | 1.2 | 555,522 | 138,880 | 0.4616 | 0.7384 | 0.6251 |

9 | 1.3 | 569,479 | 142,369 | 0.4730 | 0.8270 | 0.5719 |

10 | 1.4 | 583,392 | 145,848 | 0.4846 | 0.9154 | 0.5294 |

Aircraft Type | A320 | A330 | A340 | B767 | B737 | B777 |

Amount | 13 | 0 | 0 | 1 | 5 | 0 |

Economic Statistics | Passenger Statistics | |||||

Operating profit (CNY) | 3,765,964 | Daily passenger demand | 4931 | |||

Normalized fleet profit (CNY/seat/km) | 0.3276 | Number of passengers carried | 4537 | |||

Normalized fleet profit (CNY/pax/km) | 0.4345 | Passenger load factor (%) | 62.12 | |||

Total cost (CNY) | 4,709,500 | Fraction of passenger carried (%) | 92.01 | |||

Normalized fleet operating cost (CNY/seat/km) | 0.4096 | Fraction of passenger carried—nonstop (%) | 79.22 | |||

Normalized fleet operating cost (CNY/pax/km) | 0.5434 | Fraction of passenger carried—connecting (%) | 20.78 | |||

Environmental Statistics | Fleet Statistics | |||||

Carbon emission (kg) | 527,763 | Passenger-kilometer (km) | 8,666,710 | |||

Normalized fleet emission (kg/seat/km) | 0.0459 | Seat-kilometer (km) | 11,496,476 | |||

Normalized fleet emission (kg/pax/km) | 0.0609 | Seating capacity | 7304 |

Aircraft Type | A320 | A330 | A340 | B767 | B737 | B777 |

Stochastic parameters | 13 | 0 | 0 | 1 | 5 | 0 |

Deterministic parameters | 12 | 0 | 0 | 3 | 4 | 1 |

Operating profit (CNY) | Total cost (CNY) | |||||

Stochastic parameters | 3,765,964 | Stochastic parameters | 4,709,500 | |||

Deterministic parameters | 3,549,988 | Deterministic parameters | 4,882,696 | |||

Carbon emission (kg) | Seating capacity | |||||

Stochastic parameters | 527,763 | Stochastic parameters | 7304 | |||

Deterministic parameters | 570,012 | Deterministic parameters | 7814 |

Aircraft Type | A320 | A330 | A340 | B767 | B737 | B777 |

Amount | 11 | 0 | 2 | 4 | 2 | 0 |

Economic Statistics | Passenger Statistics | |||||

Operating profit (CNY) | 4,028,132 | Daily passenger demand | 4931 | |||

Normalized fleet profit (CNY/seat/km) | 0.3108 | Number of passengers carried | 4817 | |||

Normalized fleet profit (CNY/pax/km) | 0.4310 | Passenger load factor (%) | 59.94 | |||

Total cost (CNY) | 5,066,300 | Fraction of passenger carried (%) | 97.69 | |||

Normalized fleet operating cost (CNY/seat/km) | 0.3909 | Fraction of passenger carried—nonstop (%) | 79.28 | |||

Normalized fleet operating cost (CNY/pax/km) | 0.5421 | Fraction of passenger carried—connecting (%) | 20.72 | |||

Environmental Statistics | Fleet Statistics | |||||

Carbon emission (kg) | 649,322 | Passenger-kilometer (km) | 9,346,033 | |||

Normalized fleet emission (kg/seat/km) | 0.0501 | Seat-kilometer (km) | 12,959,462 | |||

Normalized fleet emission (kg/pax/km) | 0.0695 | Seating capacity | 8036 |

Passenger Demand Level | Passenger Load Factor | Profit (CNY) | Emission (kg) | A320 | A330 | A340 | B767 | B737 | B777 | |
---|---|---|---|---|---|---|---|---|---|---|

1 | 100% | 62.12% | 3,765,964 | 527,763 | 13 | 0 | 0 | 1 | 5 | 0 |

2 | 120% | 69.04% | 4,641,427 | 528,040 | 15 | 1 | 0 | 0 | 4 | 0 |

3 | 140% | 75.88% | 5,277,387 | 527,800 | 15 | 1 | 0 | 0 | 4 | 0 |

4 | 160% | 79.01% | 5,681,599 | 527,670 | 16 | 0 | 0 | 0 | 4 | 0 |

5 | 180% | 84.53% | 5,893,103 | 528,047 | 15 | 0 | 1 | 0 | 2 | 0 |

6 | 200% | 90.79% | 6,010,326 | 527,793 | 15 | 1 | 0 | 0 | 2 | 0 |

Aircraft Type | A320 | A330 | A340 | B767 | B737 | B777 |

Amount | 16 | 0 | 0 | 0 | 4 | 0 |

Economic Statistics | Passenger Statistics | |||||

Operating profit (CNY) | 5,681,599 | Daily passenger demand | 7890 | |||

Normalized fleet profit (CNY/seat/km) | 0.5035 | Number of passengers carried | 5804 | |||

Normalized fleet profit (CNY/pax/km) | 0.5427 | Passenger load factor (%) | 79.01 | |||

Total cost (CNY) | 4,728,800 | Fraction of passenger carried (%) | 73.56 | |||

Normalized fleet operating cost (CNY/seat/km) | 0.4191 | Fraction of passenger carried—nonstop (%) | 83.12 | |||

Normalized fleet operating cost (CNY/pax/km) | 0.4517 | Fraction of passenger carried—connecting (%) | 16.88 | |||

Environmental Statistics | Fleet Statistics | |||||

Carbon emission (kg) | 527,670 | Passenger-kilometer (km) | 10,467,824 | |||

Normalized fleet emission (kg/seat/km) | 0.0468 | Seat-kilometer (km) | 11,283,230 | |||

Normalized fleet emission (kg/pax/km) | 0.0504 | Seating capacity | 7346 |

$\mathit{p}$ | Profit (CNY) | Emission (kg) | A320 | A330 | A340 | B767 | B737 | B777 | |
---|---|---|---|---|---|---|---|---|---|

1 | 50 | 3,765,964 | 527,763 | 13 | 0 | 0 | 1 | 5 | 0 |

2 | 60 | 3,765,133 | 527,763 | 13 | 0 | 0 | 1 | 5 | 0 |

3 | 70 | 3,764,301 | 527,763 | 13 | 0 | 0 | 1 | 5 | 0 |

4 | 80 | 3,763,470 | 527,763 | 13 | 0 | 0 | 1 | 5 | 0 |

5 | 90 | 3,762,639 | 527,763 | 13 | 0 | 0 | 1 | 5 | 0 |

6 | 100 | 3,762,872 | 527,697 | 13 | 0 | 0 | 1 | 5 | 0 |

7 | 110 | 3,762,789 | 527,697 | 13 | 0 | 0 | 1 | 5 | 0 |

8 | 120 | 3,762,705 | 527,697 | 13 | 0 | 0 | 1 | 5 | 0 |

9 | 130 | 3,762,622 | 527,697 | 13 | 0 | 0 | 1 | 5 | 0 |

10 | 140 | 3,762,544 | 527,692 | 13 | 0 | 0 | 1 | 5 | 0 |

Aircraft Type | A320 | A330 | A340 | B767 | B737 | B777 |

Amount | 13 | 0 | 0 | 1 | 5 | 0 |

Economic Statistics | Passenger Statistics | |||||

Operating profit (CNY) | 3,762,872 | Daily passenger demand | 4931 | |||

Normalized fleet profit (CNY/seat/km) | 0.3273 | Number of passengers carried | 4507 | |||

Normalized fleet profit (CNY/pax/km) | 0.4348 | Passenger load factor (%) | 61.71 | |||

Total cost (CNY) | 4,709,500 | Fraction of passenger carried (%) | 91.40 | |||

Normalized fleet operating cost (CNY/seat/km) | 0.4096 | Fraction of passenger carried—nonstop (%) | 78.50 | |||

Normalized fleet operating cost (CNY/pax/km) | 0.5441 | Fraction of passenger carried—connecting (%) | 21.50 | |||

Environmental Statistics | Fleet Statistics | |||||

Carbon emission (kg) | 527,697 | Passenger-kilometer (km) | 8,655,190 | |||

Normalized fleet emission (kg/seat/km) | 0.0459 | Seat-kilometer (km) | 11,496,476 | |||

Normalized fleet emission (kg/pax/km) | 0.0609 | Seating capacity | 7304 |

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## Share and Cite

**MDPI and ACS Style**

Wang, Y.; Yuan, K.; Zhu, M.; Li, S.
A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network. *Sustainability* **2023**, *15*, 5832.
https://doi.org/10.3390/su15075832

**AMA Style**

Wang Y, Yuan K, Zhu M, Li S.
A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network. *Sustainability*. 2023; 15(7):5832.
https://doi.org/10.3390/su15075832

**Chicago/Turabian Style**

Wang, Yu, Kaibo Yuan, Mengyuan Zhu, and Shuijin Li.
2023. "A Time-and-Space-Network-Based Green Fleet Planning Model and Its Application for a Hub-and-Spoke Network" *Sustainability* 15, no. 7: 5832.
https://doi.org/10.3390/su15075832