Abstract
We conducted a study to quantify the carbon emission reduction (CER) from electric vehicle (EV) travel using the China Certified Emission Reduction (CCER) methodology. By monitoring electricity data from a charging station in Hangzhou in 2022, we calculated the CER by comparing emissions from EVs with those from conventional fuel vehicles (FCVs) over the same driving distance. Key influencing factors, including the electric carbon emission factor, vehicle type, and energy consumption, were analyzed. The results show that the annual CER of the charging station was 12,624.04 tCO₂e, and the EV power consumption per kilometer had the most significant impact—reducing power consumption by 30% increased CER by 97.1%.
Keywords: electric vehicle; CCER methodology; carbon emission reduction; energy consumption; emission factor
1. Introduction
As the International Energy Agency (IEA) reported, China’s carbon emissions reached 9.894 billion tons in 2020, accounting for 30.9% of the global total. The road transportation, petroleum, and power sectors are major emission sources. Promoting electrification in transportation, led by electric vehicles, is crucial for China’s “Carbon Peak and Carbon Neutrality” strategy and energy structure transition.
The development of EVs heavily relies on charging infrastructure. By 2022, China had 5.2 million charging facilities, with EV ownership reaching 13.1 million vehicles (4.1% of total cars), and annual charging volume exceeding 40 billion kWh. Projections indicate that EV ownership may reach 80 million by 2030, with charging demand hitting 410 billion kWh. Such scale highlights the need to quantify the CER from EVs replacing FCVs.
Previous studies often used Life Cycle Assessment (LCA) to analyze EV emissions, but few applied CCER methodology. CCER allows offsetting excess emissions, with a 5% deduction ratio in the national carbon market. Under the backdrop of CCER restart, scientifically quantifying EV-induced CER is vital. We adopted the CM-098-V01 methodology to study CER from an EV charging station in Hangzhou, analyzing sensitivity to key factors.
2. Methodology
2.1 Methodology Applicability
The CM-098-V01 methodology applies to CER calculation from EVs replacing FCVs, focusing on emissions during EV operation and electricity generation/transmission. The reduction mechanism is that EVs emit less CO₂ than FCVs for the same travel service.
2.2 Accounting Boundary
The boundary includes emissions from EV operation and electricity production/transmission, excluding charging infrastructure construction or losses. CO₂ is the primary greenhouse gas considered.
2.3 Baseline Scenario Identification
The baseline assumes EV owners use FCVs, with fuel supplied by gas stations. This scenario reflects the “business as usual” case before the charging station project.
2.4 Additionality Argument
Additionality was verified by Hangzhou’s EV market share (12.7% in 2022, <20%), meeting the methodology’s criterion. When EV share exceeds 20%, additionality may vanish, affecting CER monetization.
2.5 Calculation Methods
2.5.1 Baseline Emissions (BE)
\(BE_y = \sum_i f_{i,y} \times EC_{PJ,i,y} \times NCV_{\text{fuel},i,y} \times EF_{\text{CO₂},i,y} \times IR^t\) where \(f_{i,y}\) is the energy consumption ratio per kilometer between baseline (FCV) and project (EV) vehicles, \(EC_{PJ,i,y}\) is the charging volume, \(NCV_{\text{fuel},i,y}\) is the net calorific value of fuel, \(EF_{\text{CO₂},i,y}\) is the CO₂ emission factor, IR is the technical progress factor (default 0.99), and t is the project year.
The energy consumption ratio:\(f_{i,y} = \frac{SFC_{\text{fuel},i,y}}{SFC_{\text{elec},i,y}}\) where \(SFC_{\text{fuel},i,y}\) and \(SFC_{\text{elec},i,y}\) are fuel and electricity consumption per kilometer for FCVs and EVs, respectively.
2.5.2 Project Emissions (PE)
\(PE_y = \sum_i EF_{\text{elec},i,y} \times EC_{PJ,i,y} \times (1 + TDL_{i,y})\) where \(EF_{\text{elec},i,y}\) is the electricity carbon emission factor, and \(TDL_{i,y}\) is the transmission and distribution loss rate.
2.5.3 Leakage (L)
Leakage was ignored:\(L_y = 0\)
2.5.4 Carbon Emission Reduction (ER)
\(ER_y = BE_y – PE_y – L_y\)
3. Data and Calculation
3.1 Data Sources
Vehicle energy consumption data came from automotive platforms (e.g., “Car Home”), while emission factors and parameters were obtained from academic literature and national guidelines. Table 1 lists key parameters for mainstream vehicles, and Table 2 summarizes baseline and project data.
Table 1: Performance Parameters of Mainstream Vehicles
| Vehicle Model | Fuel Type | Fuel Consumption (L/100 km) | Electricity Consumption (kWh/100 km) | Curb Weight (kg) | Seats |
|---|---|---|---|---|---|
| Audi A4L | Gasoline | 6.76 | – | 1,645 | 5 |
| BMW 3 Series | Gasoline | 6.9 | – | 1,588 | 5 |
| Volkswagen Lavida | Gasoline | 5.62 | – | 1,330 | 5 |
| Dongfeng Honda Civic | Gasoline | 6.28 | – | 1,800 | 5 |
| Qin PLUS EV | Electric | – | 11.6 | 1,586 | 5 |
| Song PLUS EV | Electric | – | 14.1 | 1,950 | 5 |
Table 2: Baseline and Project Activity Data Parameters
| Parameter | Scenario | Unit | Value | Data Source |
|---|---|---|---|---|
| \(EC_{PJ,i,y}\) | Baseline | MW·h | 25,418.4161 | Monitoring data |
| \(NCV_{\text{fuel},i,y}\) | Baseline | GJ/t | 44.8 | Guidelines |
| \(SFC_{\text{fuel},i,y}\) | Baseline | t/km | 0.00004745 | Automotive platforms |
| \(SFC_{\text{elec},i,y}\) | Project | MW·h/km | 0.000127 | Calculation |
| \(f_{i,y}\) | Baseline | t/(MW·h) | 0.37362205 | Calculation |
| \(EF_{\text{CO₂},i,y}\) | Baseline | tCO₂/GJ | 0.0679 | Guidelines |
| IR | Baseline | – | 0.99 | Default value |
| \(EF_{\text{elec},i,y}\) | Project | tCO₂/(MW·h) | 0.5896 | MEE publication |
| \(TDL_{i,y}\) | Project | % | 6.6 | MIIT data |
3.2 Calculation Results
Using the parameters above, we calculated the annual CER as 12,624.04 tCO₂e. The electricity emission factor (0.5896 tCO₂/(MW·h)) was derived from the combined marginal factor (CM) of East China’s power grid, a weighted average of the operational marginal (OM) and build marginal (BM) factors.
4. Results and Discussion
4.1 Impact of Electricity Carbon Emission Factor
EV emissions primarily depend on electricity generation. The CM factor for East China’s grid decreased from 2006 to 2019 (Figure 1), reflecting grid decarbonization. Reducing the electricity emission factor increases CER: a 30% decrease in the factor raised CER by 38.0%.
We analyzed OM and BM factors:
- \(EF_{\text{grid,OM,y}} = \frac{\sum_i (FC_{i,y} \times NCV_{i,y} \times EF_{\text{CO₂},i,y})}{EG_y}\)
- \(EF_{\text{grid,BM,y}} = \frac{\sum_m (EG_{m,y} \times EF_{\text{EL},m,y})}{\sum_m EG_{m,y}}\)
As coal-fired units upgrade and renewables grow, OM and BM are expected to decline, further reducing EV lifecycle emissions.
4.2 Influence of Vehicle Type
Comparing EV cars and buses, we found that EV buses replacing diesel buses had 51.0% lower CER than EV cars replacing gasoline cars (Table 3). Although diesel emits 3.4% more CO₂ per ton than gasoline, buses’ lower energy consumption ratio (25.1% less fuel per km) dominated the result.
Table 3: Vehicle Parameters for Cars and Buses
| Vehicle Type | Curb Weight (kg) | \(SFC_{\text{fuel},i,y}\) (t/km) | \(SFC_{\text{elec},i,y}\) (MW·h/km) | \(NCV_{\text{fuel},i,y}\) (GJ/t) | \(EF_{\text{CO₂},i,y}\) (tCO₂/GJ) |
|---|---|---|---|---|---|
| Gasoline Car | 1,500 (±20%) | 0.00004745 | – | 44.8 | 0.0679 |
| Electric Car | 1,500 (±20%) | – | 0.000127 | – | – |
| Diesel Bus | 13,300 | 0.000336 | – | 43.33 | 0.0726 |
| Electric Bus | 12,000 | – | 0.00112 | – | – |
4.3 Effect of Vehicle Energy Consumption
Reductions in EV power consumption per km significantly boost CER. Assuming future parameters (Table 4), a 30% decrease in EV power consumption increased CER by 97.1%. Conversely, improving FCV fuel efficiency reduced CER, as lower fuel consumption decreased baseline emissions.
Table 4: Technical Parameters of Electric and Gasoline Cars over Time
| Vehicle Type | Year | Curb Weight (kg) | \(SFC_{\text{fuel},i,y}\) (t/km) | \(SFC_{\text{elec},i,y}\) (MW·h/km) |
|---|---|---|---|---|
| Gasoline Car | 2022 | 1,500 (±20%) | 0.00004745 | – |
| 2025 | 1,500 (±20%) | 0.00004457 | – | |
| 2030 | 1,500 (±20%) | 0.00004098 | – | |
| 2035 | 1,500 (±20%) | 0.00003810 | – | |
| Electric Car | 2022 | 1,500 (±20%) | – | 0.000127 |
| 2025 | 1,500 (±20%) | – | 0.000114 | |
| 2030 | 1,500 (±20%) | – | 0.000108 | |
| 2035 | 1,500 (±20%) | – | 0.000103 |
4.4 Sensitivity Analysis
Sensitivity analysis showed that EV power consumption per km had the highest impact on CER, followed by FCV fuel consumption and electricity emission factor (Figure 2). A 30% decrease in EV power consumption increased CER by 97.1%, while a 30% decrease in FCV fuel consumption reduced CER by 68.0%. The electricity factor’s 30% decrease raised CER by 38.0%.
5. Conclusions
- The charging station in Hangzhou achieved 12,624.04 tCO₂e in CER in 2022, confirming EVs’ significant emission reduction potential compared to FCVs. As the power grid decarbonizes, this value will further increase.
- EV buses replacing diesel buses showed lower CER than EV cars replacing gasoline cars due to differences in energy consumption ratios, highlighting the need to consider vehicle-specific factors in CER calculations.
- EV power consumption per km was the most influential factor: a 30% reduction increased CER by 97.1%. Improving EV energy efficiency is crucial for maximizing emission reductions.
- With rising EV penetration, adjusting the baseline scenario in CCER methodology will be a key challenge for accurately quantifying CER in the future.