# Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework

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## Abstract

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_{2}) emissions. Results show that, under a deep decarbonization scenario, the transportation, commercial, and residential sectors will contribute to emission reduction by 6.9, 1.6, and 1 million ton CO

_{2}-eq in 2050, respectively, compared with their 2020 levels. This is mainly achieved by (i) replacing fossil fuel cars with electric-based vehicles in private and public transportation sectors; (ii) replacing fossil fuel furnaces with electric heat pumps to satisfy heating demand in buildings; and (iii) improving the efficiency of buildings by isolating walls and roofs.

## 1. Introduction

_{2}), resulting in global warming. A projection of the current trends [1] shows that the global temperature may exceed the Paris Accord goal of 1.5 °C by 2030. To avoid dangerous disruptions in the climate system, we must implement urgent mitigation measures.

## 2. Literature Review

## 3. Methodology

#### 3.1. ETEM Model Description

#### 3.2. Adapting the ETEM to the Greater Montreal Region

_{2}-eq emissions [35]. The region imported 201 PJ of electricity, and generated 34 PJ internally. In the transportation sector, the region consumed 101 PJ, 2.7 PJ, and 0.3 PJ of gasoline, diesel, and electricity, respectively. In total, the transportation sector emitted 6.9 Mt CO

_{2}-eq. In the residential and commercial sectors, there were 67 and 51 PJ of energy consumed, respectively, resulting in emissions of 2.9 Mt CO

_{2}-eq.

_{2}emissions. Further details on the energy database of the GM region are provided in Appendix B.

## 4. Scenarios

_{2}emission constraints on the main energy subsectors, including secondary energy generation, transportation, commercial, and residential. More specifically, the CO

_{2}constraint imposes a maximum emission ceiling to the main generation units and final energy consumption sectors. The generation units include power plants, biofuel, and oil product generation units. The energy consumption sectors include transportation (light-duty vehicles, public transportation, trains, and metros), commercial, and residential (heating and cooling demand). Below, we give the details of each scenario:

- Business as usual (BAU): This scenario is a reference scenario that includes all current provincial policies, such as governmental financial incentives for a large adoption of electric vehicles. However, this scenario imposes no limitation on GHG emissions. In other words, this scenario is a disengagement from the state targets in the sense that no further climate measures are enforced beyond those already in place.
- GHG1: A GHG emission reduction scenario with a 37.5% reduction target by 2030, and a 53% reduction target by 2050 (relative to 1990).
- GHG2: A more stringent reduction scenario with a 37.5% GHG-reduction target by 2030, and continuing the same reduction trend until 2050, which yields a 73% emission reduction (relative to 1990).
- GHG3: A deep decarbonization scenario which assumes a linear GHG reduction to achieve a 44% reduction by 2030 and a 93% reduction by 2050 (relative to 1990).

## 5. Results

#### 5.1. GHG Emissions

_{2}-eq has the largest share in 2020 (60% of the total), as the current transportation system mostly relies on petroleum fuels. Over time, the share of the electric vehicles (EVs) increases following an assumed price reduction for these technologies. This price reduction is partly due to governmental incentives to promote the purchase EVs, and partly to a long-term technological price reduction. Consequently, the share of the transportation sector in the total GHG emission, in the BAU scenario, reduces to 15% by 2050. Imposing CO

_{2}emission constraints further reduces the share of the transportation sector in the total emission by replacing conventional and hybrid cars with plug-in electric vehicles. In particular, emissions of the transportation sector reduce to almost zero in 2050 in GHG3.

_{2}-eq in 2050 in GHG3. Finally, the commercial sector is responsible for 23% of the total CO

_{2}emission in 2020. In the BAU scenario, emissions of this sector increases from 2.2 in 2020 to 2.5 Mt CO

_{2}-eq in 2050. However, under a deep decarbonization scenario (GHG3), this sector will only emit 0.6 Mt CO

_{2}-eq in 2050.

_{2}emission reduction when a deep decarbonization constraint (in GHG3) is imposed. Specifically, this constraint reduces the total emission to around 3 and 10 Mt CO

_{2}-eq in 2030 and 2050, respectively, compared with the BAU scenario. This figure also reveals that imposing such environmental constraint mostly affects the residential sector. On the other hand, the lower effect is on the transportation sector, as it is already largely decarbonized in the BAU scenario, following an assumed price reduction for EVs.

#### 5.2. Final Energy Consumption

_{2}emissions (in GHG1, GHG2, and GHG3), encourages a higher penetration of electrical baseboard heaters and heat pumps, and consequently reduces further the share of natural gas in total energy consumption. This also triggers larger investments in residential buildings insulation, which reduces final energy consumption. Finally, one can note that the electrification rate is highly sensitive to the severity of the CO

_{2}reductions, with by 2050 levels of 28%, 63%, and 92%, in GHG1, GHG2, and GHG3, respectively. Transitioning to near-zero emissions in this sector requires thus stringent environmental restrictions.

#### 5.3. Sensitivity Analysis

_{2}emissions, depend on the mode of transportation. If a larger share of the mobility demand is met by public transportation facilities, the required trip per passenger-kilometer demand reduces, resulting in lower primary energy consumption. In this section, we evaluate the sensitivity of our results to the share of each transportation mode (public and private) in the total mobility demand. To do so, we exogenously shift 20% and 50% of the mobility demand from light-duty vehicles to public transport (buses and metros). These transport modal shifts start in 2030 and continue until 2050.

_{2}-eq emissions of the transportation sector under the different modal shifts, while MS_0% represents the default modal share, and MS_20% and MS_50% correspond to the 20% and 50% modal shifts, respectively. This figure relates to the BAU scenario. In 2030, total emissions under MS_50% are 36% lower than in MS_0%. However, this emission gap reduces afterwards following the electrification of light-duty vehicles. In other words, shifting to public transportation can be a temporary strategy to reduce emissions before electrifying the transportation sector.

## 6. Discussion

_{2}emitted from increasing utilization of concrete); (iii) we abstract some details of the energy system, such as technological choices in industry, agriculture, and specific consumption in the transportation, residential, and commercial sectors; and (iv) we have not considered some modes of transportation, such as bikes and e-bikes.

## 7. Conclusions

_{2}-eq emissions. We evaluated the impact of imposing different CO

_{2}-eq emission reduction constraints on energy transition pathways for the GM region. Results show that the transportation sector, with a 6.9 million ton (Mt) emission reduction compared with the 2020 level, plays an important role in a deep decarbonization (GHG3 scenario) of the GM region. This reduction can be achieved by electrifying private and public vehicles. Moreover, commercial and residential sectors will contribute to the deep decarbonization by, respectively, reducing 1.6 Mt and 1 Mt CO

_{2}-eq (compared with their 2020 levels). The most important decarbonization strategies in these sectors include (i) replacing fossil-fuel-based furnaces with electric-based heat pumps to satisfy heating demands, and (ii) reducing energy consumption by increasing buildings insulation.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclatures

$t\in \mathbb{T}$ | Index for time period | ${\beta}_{t,s,p}$ | Capacity factor |

$s\in \mathbb{S}$ | Index for time slices | ${\eta}_{c}$ | Network efficiency |

$p\in \mathbb{P}$ | Index for technologies | ${\eta}_{f,{f}^{\prime}}^{t}$ | Technology efficiency |

$c\in \mathbb{C}$ | Index for energy commodities | ${\lambda}_{t,s,l,{l}^{\prime},c}^{\u2033}$ | Transmission cost |

$c\in \mathbb{CS}$ | Index for energy storage | ${\lambda}_{t,s,c}^{\prime}$ | Export cost |

$f\in \mathbb{F}$ | Index for energy flows | ${\lambda}_{t,s,c}$ | Import cost |

$l\in \mathbb{L}$ | Index for buses (geographical zones) | ${\nu}_{t,s,c}$ | Maximum deviation from |

$j\in \mathbb{J}$ | Index for seasons | nominal demand response | |

$i\in \mathbb{I}$ | Index for period-seasons $(t,j)$ | ${\nu}_{t,p}$ | Variable cost |

${\mathbb{P}}_{c}^{C}\subseteq \mathbb{P}$ | Set of technologies consuming c | ${\mathsf{\Omega}}_{t,l,p}$ | Available capacity of technology p |

${\mathbb{P}}_{c}^{P}\subseteq \mathbb{P}$ | Set of technologies producing c | ${\pi}_{t,p}$ | Fixed production cost |

${\mathbb{P}}^{R}\subseteq \mathbb{P}$ | Set of intermittent technologies | $\rho $ | Discount factor |

${\mathbb{C}}^{I}\subseteq \mathbb{C}$ | Set of imported commodities | ${\theta}_{p}^{c}$ | Proportion of output c from technology |

${\mathbb{C}}^{\mathcal{D}}\subseteq \mathbb{C}$ | Set of useful demands | p that can be used in peak period | |

${\mathbb{C}}^{EX}\subseteq \mathbb{C}$ | Set of exported commodities | ${l}_{p}$ | Life duration of technology p |

${\mathbb{C}}^{TR}\subseteq \mathbb{C}$ | Set of transmitted commodities | ${\mathsf{\Theta}}_{t,l,d}$ | Annual final demand |

${\mathbb{C}}_{f}\subseteq \mathbb{C}$ | Set of commodities linked to flow f | ${\upsilon}_{t,s,c}$ | Nominal demand response |

${\mathbb{C}}^{\mathcal{G}}\subseteq \mathbb{C}$ | Set of commodities with margin reserve | ${\varrho}_{t,s,c}$ | Required reserve for commodity $c\in {\mathbb{C}}^{\mathcal{G}}$ |

${\mathbb{S}}^{j}\subseteq \mathbb{S}$ | Set of time slices s in season j | ${\mathit{C}}_{t,l,p}$ | Variable for new capacity addition |

${\mathbb{S}}^{s}\subseteq \mathbb{S}$ | Set of successive time slices of s | ${\mathit{C}}_{t,l,p}^{T}$ | Total installed capacity |

${\mathbb{S}}^{\mathcal{G}}\subseteq \mathbb{S}$ | Set of time slices in peak period | ${\mathit{P}}_{t,s,l,p,c}$ | Variable for activity of technology p |

${\mathbb{FI}}_{p}\subseteq \mathbb{F}$ | Set of inputs to technology p | ${\mathit{I}}_{t,s,l,c}$ | Variable for import |

${\mathbb{FO}}_{p}\subseteq \mathbb{F}$ | Set of outputs from technology p | ${\mathit{E}}_{t,s,l,c}$ | Variable for export |

${\alpha}_{t,p}$ | Investment cost | ${\mathit{T}}_{t,s,l,{l}^{\prime},c}$ | Variable for regional transmission |

${\mathit{V}}_{t,s,l,d}$ | Variable for demand response |

## Appendix A. ETEM Formulation

## Appendix B. GM Energy Database

Sector | Demand Type | Unit | Number of Technologies | Fuels ^{1} |
---|---|---|---|---|

residential | Heating | TJ | 17 | NGA, ELC, LFO, PRO, BIO |

Cooling | TJ | 6 | ELC | |

Other | TJ | 2 | NGA, ELC, LFO, PRO, BIO | |

Commercial | Heating | TJ | 21 | NGA, ELC, HET, LFO, HFO, PRO |

Cooling | TJ | 9 | NGA, ELC | |

Other | TJ | 10 | NGA, ELC, HET, LFO, HFO, PRO | |

Trnasportation | Light-duty vehicles | tkmv/d | 7 | GSL, ETH, ELC, DST, BSL |

Public transportation | tkmv/d | 10 | GSL, ETH, ELC, DST, BSL, NGA | |

Metro | tkmv/d | 2 | ELC | |

Train | tkmv/d | 2 | DST, BSL | |

Other | tkmv/d | 1 | GSL, ETH, ELC, DST, BSL, NGA, LFO, HFO, ATR |

^{1}ATR—jet fuel; BIO—biomass; BSL—biodiesel; DST—diesel; ELC—electricity; ETH—ethanol; GSL—gasoline; HET—centrally produced heat; HFO—heavy fuel oil; LFO—light fuel oil; PRO—propane; NGA—natural gas.

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**Figure 1.**Overview of the reference energy system (RES) in the Greater Montreal region. SOL—solar; HYD—hydro; NGA—natural gas; PP—power plant; DG—distributed generation; ELC—electricity; EBAT—the electricity of batteries; IND—industry; AGRI—agriculture; Spa—space; TRNS—transportation; EV—electric vehicles; RES—residential; COM—commercial.

**Figure 2.**A summary of inputs, outputs, and the main characteristics of the ETEM adapted for the Greater Montreal region.

**Figure 5.**Contribution of each sector in the total GHG reductions in the deep decarbonization environmental scenario (GHG3) compared with the reference scenario (BAU).

**Figure 10.**GHG emissions from the transportation sector for different modal shifts in BAU, as the mobility demand is partially shifted from light-duty vehicles to public transportation.

**Figure 11.**Final energy consumption for the transportation sector in 2050 under different scenarios and transport modal shifts.

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**MDPI and ACS Style**

Aliakbari Sani, S.; Maroufmashat, A.; Babonneau, F.; Bahn, O.; Delage, E.; Haurie, A.; Mousseau, N.; Vaillancourt, K.
Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework. *Energies* **2022**, *15*, 3760.
https://doi.org/10.3390/en15103760

**AMA Style**

Aliakbari Sani S, Maroufmashat A, Babonneau F, Bahn O, Delage E, Haurie A, Mousseau N, Vaillancourt K.
Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework. *Energies*. 2022; 15(10):3760.
https://doi.org/10.3390/en15103760

**Chicago/Turabian Style**

Aliakbari Sani, Sajad, Azadeh Maroufmashat, Frédéric Babonneau, Olivier Bahn, Erick Delage, Alain Haurie, Normand Mousseau, and Kathleen Vaillancourt.
2022. "Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework" *Energies* 15, no. 10: 3760.
https://doi.org/10.3390/en15103760