With the rapid expansion of the electric car market in China, quantifying the carbon emission reduction from electric vehicle adoption has become a critical aspect of climate change mitigation strategies. The China Certified Emission Reduction (CCER) methodology offers a standardized approach to evaluate greenhouse gas emission reductions from projects that replace conventional fossil fuel-based technologies with low-carbon alternatives. In this study, we apply the CCER methodology to assess the carbon emission reduction from electric car travel in a specific region of Hangzhou, China, by analyzing electricity consumption data from a charging station in 2022. We focus on calculating the carbon reduction achieved by replacing gasoline-powered vehicles with electric cars and examine key influencing factors such as the carbon emission factor of electricity, vehicle type, and energy consumption per kilometer. Through sensitivity analysis, we evaluate the impact of these factors on the overall carbon reduction benefits, providing insights for policymakers and stakeholders in the China EV industry.
The CCER methodology, specifically CM-098-V01, is employed to calculate the carbon emission reduction from electric car travel. This methodology is applicable to projects where electric vehicles replace conventional fuel vehicles, and charging stations provide the necessary electricity. The system boundary encompasses emissions from vehicle operation and electricity production and transmission, excluding infrastructure construction, facility operation, and charging losses. The baseline scenario is defined as the continued use of gasoline-powered vehicles for travel, while the project scenario involves the use of electric cars charged at the station. The additionality of the project is demonstrated by the low market penetration of electric vehicles in the region, which was below 20% in 2022, ensuring that the project would not have occurred without carbon reduction incentives.
The carbon emission reduction is calculated as the difference between baseline emissions and project emissions, with no leakage considered. The formulas are as follows:
Baseline emissions (BEy) for year y are calculated as:
$$ BE_y = \sum_i f_{i,y} \times EC_{PJ,i,y} \times NCV_{fuel,i,y} \times EF_{CO2,i,y} \times IR_t $$
where \( f_{i,y} \) is the energy consumption ratio per kilometer for vehicle type i, \( EC_{PJ,i,y} \) is the electricity consumption from the charging station for vehicle type i, \( NCV_{fuel,i,y} \) is the net calorific value of the fuel, \( EF_{CO2,i,y} \) is the CO2 emission factor of the fuel, and \( IR_t \) is the technological progress factor (default 0.99).
The energy consumption ratio \( f_{i,y} \) is given by:
$$ f_{i,y} = \frac{SFC_{fuel,i,y}}{SFC_{elec,i,y}} $$
where \( SFC_{fuel,i,y} \) is the fuel consumption per kilometer for baseline vehicles, and \( SFC_{elec,i,y} \) is the electricity consumption per kilometer for project vehicles.
Project emissions (PEy) are calculated as:
$$ PE_y = \sum_i EF_{elec,i,y} \times EC_{PJ,i,y} \times (1 + TDL_{i,y}) $$
where \( EF_{elec,i,y} \) is the carbon emission factor of electricity, and \( TDL_{i,y} \) is the transmission and distribution loss.
Leakage (Ly) is zero:
$$ L_y = 0 $$
Thus, the emission reduction (ERy) is:
$$ ER_y = BE_y – PE_y $$
We utilize data from a charging station in Hangzhou for the year 2022, with a total electricity consumption of 25,418.4161 MWh. The parameters for baseline and project vehicles are derived from automotive websites and guidelines. For instance, the average fuel consumption for gasoline cars is 6.5 L/100 km, and for electric cars, it is 12.7 kWh/100 km. The carbon emission factor for electricity is the combined margin (CM) factor for the East China grid, which is 0.5896 tCO2/MWh in 2019. Other parameters are summarized in the table below.
| Parameter | Symbol | Value | Unit | Source |
|---|---|---|---|---|
| Electricity consumption | ECPJ,i,y | 25,418.4161 | MWh | Monitoring data |
| Net calorific value of gasoline | NCVfuel,i,y | 44.8 | GJ/t | Guideline |
| Fuel consumption per km | SFCfuel,i,y | 0.00004745 | t/km | Calculated from automotive data |
| Electricity consumption per km | SFCelec,i,y | 0.000127 | MWh/km | Calculated from automotive data |
| CO2 emission factor of gasoline | EFCO2,i,y | 0.0679 | tCO2/GJ | Guideline |
| Carbon emission factor of electricity | EFelec,i,y | 0.5896 | tCO2/MWh | East China grid CM factor |
| Transmission and distribution loss | TDLi,y | 6.6 | % | Industry data |
Using these parameters, we compute the baseline emissions, project emissions, and the resulting carbon reduction for 2022. The calculated carbon emission reduction is 12,624.04 tCO2e, demonstrating the significant potential of electric car travel in reducing greenhouse gas emissions compared to conventional gasoline vehicles. This outcome underscores the importance of promoting China EV adoption as part of broader carbon neutrality efforts.
We further investigate the impact of various factors on carbon reduction, starting with the carbon emission factor of electricity. The carbon emission factor (EFelec) is a critical parameter that influences project emissions. As the electricity grid becomes cleaner, EFelec decreases, leading to lower project emissions and higher carbon reduction. We examine historical CM factors for the East China grid from 2006 to 2019, as detailed in the table below.
| Year | OM | BM | CM |
|---|---|---|---|
| 2006 | 0.980 | 0.750 | 0.865 |
| 2016 | 0.700 | 0.550 | 0.625 |
| 2017 | 0.680 | 0.540 | 0.610 |
| 2018 | 0.590 | 0.580 | 0.585 |
| 2019 | 0.590 | 0.580 | 0.585 |
By applying different CM factors, we recalculate the carbon reduction. For example, with the 2006 CM factor (0.865 tCO2/MWh), the project emissions increase, reducing the carbon reduction to 5,188.89 tCO2e, which is 58.9% lower than the 2019 value. This analysis highlights the substantial impact of grid decarbonization on enhancing the carbon reduction benefits of electric cars. As China continues to transition to renewable energy sources, the carbon emission factor of electricity is expected to decline, further improving the environmental performance of China EV.
Next, we explore the effect of vehicle type on carbon reduction. We compare scenarios where electric cars replace gasoline cars versus electric buses replace diesel buses. The parameters for buses differ significantly: diesel consumption per km is 0.000336 t/km, and electricity consumption per km is 0.00112 MWh/km. The calculation reveals that for the same electricity consumption, the carbon reduction for buses is only 6,189.14 tCO2e, which is 51.0% lower than for cars. This discrepancy arises from the higher energy efficiency of diesel buses compared to gasoline cars, resulting in lower baseline emissions. Thus, the choice of vehicle type is a crucial factor in determining the carbon reduction potential of electric vehicle projects.
Technological advancements are anticipated to reduce the energy consumption per kilometer for both electric and gasoline vehicles. We project the parameters for 2025, 2030, and 2035 based on literature, as shown in the table below.
| Year | Electric car SFCelec (MWh/km) | Gasoline car SFCfuel (t/km) |
|---|---|---|
| 2022 | 0.000127 | 0.00004745 |
| 2025 | 0.000114 | 0.00004457 |
| 2030 | 0.000108 | 0.00004098 |
| 2035 | 0.000103 | 0.00003810 |
Assuming a decrease in electricity consumption while keeping other parameters constant, the carbon reduction increases. For instance, in 2035, with SFCelec reduced by 18.9%, the carbon reduction reaches 19,288.09 tCO2e, an increase of 52.8% from 2022. Conversely, if gasoline consumption decreases, the carbon reduction diminishes. In 2035, with SFCfuel reduced by 19.7%, the carbon reduction drops to 6,988.45 tCO2e, a decrease of 44.6%. These findings emphasize the importance of improving the energy efficiency of electric cars to maximize carbon reduction benefits in the China EV market.
We conduct a sensitivity analysis by varying three key factors: carbon emission factor of electricity (EFelec), electricity consumption per km (SFCelec), and fuel consumption per km (SFCfuel). The variation range is ±10%, ±20%, and ±30%. The results are summarized in the table below.
| Factor | Change | Carbon Reduction (tCO2e) | Change Percentage |
|---|---|---|---|
| EFelec | -30% | 17,416.79 | +38.0% |
| -20% | 15,799.42 | +25.2% | |
| -10% | 14,182.05 | +12.4% | |
| 0% | 12,624.04 | 0% | |
| +10% | 11,006.67 | -12.8% | |
| +20% | 9,389.30 | -25.6% | |
| +30% | 7,831.29 | -38.0% | |
| SFCelec | -30% | 24,881.13 | +97.1% |
| -20% | 21,004.08 | +66.4% | |
| -10% | 17,127.03 | +35.7% | |
| 0% | 12,624.04 | 0% | |
| +10% | 9,746.99 | -22.8% | |
| +20% | 6,869.94 | -45.6% | |
| +30% | 6,024.08 | -52.3% | |
| SFCfuel | -30% | 4,044.08 | -68.0% |
| -20% | 6,869.94 | -45.6% | |
| -10% | 9,746.99 | -22.8% | |
| 0% | 12,624.04 | 0% | |
| +10% | 15,501.09 | +22.8% | |
| +20% | 18,378.14 | +45.6% | |
| +30% | 21,204.00 | +68.0% |
The sensitivity analysis indicates that SFCelec has the most pronounced impact on carbon reduction, particularly when it decreases. A 30% reduction in SFCelec leads to a 97.1% increase in carbon reduction. The sensitivity degree for SFCelec ranges from 1.74 to 3.24, confirming its high sensitivity. In contrast, EFelec and SFCfuel show moderate to high sensitivity, with changes in SFCfuel having a symmetric but opposite effect. These results underscore the critical role of enhancing the energy efficiency of electric cars to amplify carbon reduction benefits in the China EV sector.
In summary, the application of the CCER methodology to electric car travel in Hangzhou reveals substantial carbon reduction potential, with 12,624.04 tCO2e reduced in 2022. The carbon reduction is highly sensitive to the electricity consumption per kilometer of electric cars, emphasizing the need for continuous technological improvements in battery efficiency and vehicle design. The carbon emission factor of electricity also plays a pivotal role, and as the grid evolves towards cleaner energy sources, the benefits of electric cars will escalate. Vehicle type influences carbon reduction, with electric cars replacing gasoline vehicles yielding greater benefits than electric buses replacing diesel vehicles. Future research should address the evolving baseline scenario as electric car market penetration increases, ensuring the methodology remains relevant. The expansion of the China EV market is essential for achieving carbon neutrality objectives, and policies should foster the development of efficient electric cars and robust clean energy infrastructure. By prioritizing these factors, stakeholders can maximize the environmental advantages of electric car adoption and contribute to global sustainability goals.