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Journal of Resources and Ecology >

2023 , Vol. 14 >Issue 2: 372 - 382

DOI: https://doi.org/10.5814/j.issn.1674-764x.2023.02.015

Resources and Environment

Projections of the Emission Reductions of Carbon Dioxide and Conventional Pollutants in the Major Transport Sectors of the Beijing-Tianjin-Hebei Region, China

  • GUO Xiurui , * ,
  • GONG Xiaoqian ,
  • LIU Yao ,
  • ZHANG Yiling
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  • Key Laboratory of Beijing on Regional Air Pollution Control, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
*GUO Xiurui, E-mail:

Received date: 2022-04-28

  Accepted date: 2022-06-28

  Online published: 2023-02-21

Supported by

The National Natural Science Foundation of China(51978011)

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Abstract

Many stakeholders recognize that the transport sector should be a major focus for reducing the emissions of carbon and air pollutants since it is the third largest sector for energy consumption in China. This study analyzed and projected the energy consumption and emissions of CO2 and conventional air pollutants (CO, NOX, SO2, and PM2.5) from four transport sectors (highway, waterway, railway and aviation) based on the LEAP model, compared the emission reduction potentials of different transport sectors under different scenarios in 2020-2060, and finally explored the co-reduction effect for CO2 and the four pollutants under different control measures. The results showed that the CO2 emissions from the transportation sectors in the Beijing-Tianjin-Hebei (BTH) region would increase greatly under the baseline scenario. Estimates indicate that the CO2 emissions of Beijing, Tianjin and Hebei Province would increase by 263.72%, 225.87% and 405.43% in 2060, respectively. Under the comprehensive policy scenario, the emission reductions would be 88.78%, 76.86% and 83.20% respectively, and the maximum emission reduction rate of pollutants is expected to reach 78.73%-99.34%. The sectors with major reduction potentials for CO2 and conventional pollutants are the aviation and road transport sectors, which contribute 38.19%-99.85% of the total, respectively. The co-reduction achieved by optimizing the energy structure in the road transport and aviation sectors would be the best. The results of this study can provide a basis for the formulation of low-carbon reduction strategies for the transport sectors in the BTH region.

Key words： transport sector; reduction potential; scenario analysis; Beijing-Tianjin-Hebei region

Cite this article

GUO Xiurui , GONG Xiaoqian , LIU Yao , ZHANG Yiling . Projections of the Emission Reductions of Carbon Dioxide and Conventional Pollutants in the Major Transport Sectors of the Beijing-Tianjin-Hebei Region, China[J]. Journal of Resources and Ecology, 2023 , 14(2) : 372 -382 . DOI: 10.5814/j.issn.1674-764x.2023.02.015

1 Introduction

In order to cope with the increasingly severe climate change problem, at the United Nations General Assembly the Chinese government proposed that China should strive to peak its carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. The emissions of greenhouse gases are known to come mainly from energy consumption. Presently the transportation sector has become the third largest contributor of CO2 emissions in China, with greater contributions only from the power plants and industrial sectors. In 2018, the energy consumption of China’s transportation sector was 496 million tons of standard coal, accounting for 10.7% of the country’s total (Yuan et al., 2021). With the rapid economic development in China, the energy consumption of the transportation sectors will continue to grow up in the future. On the other hand, most atmospheric pollutants (such as PM, CO, NOx and VOCs) are also produced by the combustion of fossil fuels. That is, greenhouse gases and atmospheric pollution are both closely related to energy consumption. Therefore, it is very important to explore and study the co-reduction of the greenhouse gases and pollutants contributed from the transportation sectors.
Currently, the relevant studies mainly focus on the road mobile sources, and employ various methods including the grey prediction model GM(1,1) (Lu et al., 2009; Kazancoglu et al., 2021), ASIF (Activity Structure-Energy Intensity-Fuel) framework modeling (Aggarwal and Jain, 2016), the FEEI (Fuel Economy and Environmental Impacts) model (Huo et al., 2012), the Bottom-Up method (Ning et al., 2014; Bu et al., 2021; Guo et al., 2021) and other methods (Lv, 2018; Yang et al., 2020; Zhang et al., 2020; Zhu et al., 2020). Some researchers used the LEAP (Long Range Energy Alternatives Planning System) model to predict the energy consumption, carbon emissions and pollutant emissions from the transportation sectors at the national, provincial and municipal levels. For example, Azam et al. (2016), Hong et al. (2016) and Aliakbar et al. (2012) calculated and forecasted the emissions of carbon dioxide and air pollutants from the transport industries in Malaysia, South Korea and Iran, respectively. Some scholars have applied the LEAP model to predict the energy demand and major greenhouses in Beijing under different scenarios in future years, and concluded that developing new energy sources and tightening emission standards would have great potential for energy conservation and emission reduction (Fan et al., 2017; Lv et al., 2020; Li and Song, 2021). Some studies have predicted and analyzed the energy consumption, and CO2 and pollutant emissions from road traffic under different scenarios, and compared the synergistic effect of emission reduction in the Beijing-Tianjin-Hebei region, China (Guo et al., 2017; Tan and Yang, 2017). A few studies have estimated carbon emissions from the transportation sector at the city level (Costa et al., 2018; Gao, 2019; Nie et al., 2019). Liu et al. (2018) estimated the emissions of carbon dioxide and air pollutants in China's transportation industry from 2010 to 2050 using the LEAP model. However, the studies that have focused on the co-reduction of greenhouse gases and air pollutants mainly concentrated on the coal-fired power generation industry (Yu et al., 2020; Du et al., 2021; Yang and Song, 2021) and the steel industry (Yang et al., 2018; Li et al., 2019), and there is very limited literature on collaborative emission reductions from the transport sectors (Feng et al., 2021).
In addition, the studies on greenhouse gas and pollutant emissions from the transport sectors have mainly concentrated on road vehicles. Relatively few studies have focused on the whole spectrum of transportation sectors (road, railway, aviation and waterway), and there is a lack of comparative analyses of the emission reduction between different transport sectors. Therefore, this study projected the emissions of the greenhouse gas (CO2) and four major air pollutants (CO, NOX, SO2 and PM2.5) contributed from four transport sectors (highway, railway, aviation, and waterway) during 2020 to 2060 in the Beijing-Tianjin-Hebei (BTH) region of China, and then analyzed and compared the reduction potentials and co-reduction effects for different transport sectors under different scenarios. The results could be helpful and provide scientific support for the formulation of effective control measures to simultaneously reduce CO2 and conventional pollutants from the transport sectors. Regarding the organization of this paper, after the introduction section, the methodology and policy scenarios are defined and presented, then the predicted emissions and emission reduction potentials of CO2 and pollutants are explored, and the main conclusions are described.

2 Methodology

In order to predict and estimate the emission potential for co-reduction of carbon dioxide and pollutants from the transportation sectors in the BTH region, this study mainly used the LEAP model to predict energy consumption, combined with the emission factor method to estimate the emissions of CO2 and pollutants. Finally, the scenario analysis method was used to compare and analyze the co-reduction effects of different measures for the individual transport sectors.

2.1 LEAP model

The LEAP (Long-range Energy Alternatives Planning system) model developed by the Stockholm Environment Institute, has been extensively used in many studies on energy and environmental planning based on bottom-up scenario analysis.
This study developed the LEAP-BTH/TR model based on the original LEAP model framework. The sectors are divided into the three departments of urban passenger transport, intercity passenger and freight, and there are four sub-sectors (highway, railway, aviation and waterway) for each sector. Fig.1 shows the detailed framework of this model.
Fig. 1 The framework of the LEAP-BTH/TR model developed in this study
After the framework was constructed, the values of some parameters need to be determined. The socio-economic and energy consumption data for different sectors in the base year of 2018, were derived from the statistical yearbooks, the survey data of the three sub-regions and relevant literature. The macroeconomic indicators (GDP, industrial structure, population, household consumption, etc.), technical variables (energy use efficiency of a specific process or equipment, energy intensity), and industrial development indicators were defined based on the comprehensive consideration of sectoral planning, research reports by academic institutions, and relevant references.

2.2 Calculation of energy consumption

The energy consumption levels of each of the transport sectors in the BTH region were calculated according to the corresponding data of activity level and energy intensity for each of the specific transportation types. The calculation formula is as follows:
${{E}_{e}}=\mathop{\sum }^{}\left( {{N}_{m}}\times {{L}_{m}}\times {{V}_{m,e}} \right)+\mathop{\sum }^{}\left( {{Q}_{j}}\times {{R}_{j,e}} \right)$
where Ee is the energy consumption; e is the corresponding energy type, such as gasoline, diesel, or electricity; j is road, railway, aviation, or waterway passenger/freight; m is private cars, motorcycles, taxis, buses and other vehicles; Nm is the population of vehicle m; Lm is the annual travelled distance of vehicle m; Vm,e is the energy consumption per 100 km for vehicle m; Qj is the passenger/freight turnover of traffic type j; and Rj,e is the energy consumption per unit turnover for traffic type j.

2.3 Calculation of the emissions of CO2 and air pollutants

Transportation carbon emissions mainly include direct emissions from fossil energy combustion, and indirect emissions of carbon dioxide from electricity consumption and heat consumption. The calculation formula is as follows:
CE=∑ECij×EFij
where CE is the carbon emission of transportation; ECij is the energy demand of transport type j in sub-sector i; and EFij is the emission factor of transport type j in sub-sector i.
The emission estimations of the CO, NOx, SO2 and PM2.5 pollutants were obtained using the emission factor method based on the activity level, and the calculation method is as follows:
$A{{E}_{p}}=\underset{i}{\mathop \sum }\,\underset{j}{\mathop \sum }\,{{A}_{i,j}}\times E{{F}_{i,j,p}}$
where AEp is the emission of pollutant p; p is the pollutants (CO, NOx, PM2.5 and SO2); i is the type of sector; Ai,j is the energy demand of traffic type j in sector i; and EFi,j,p denotes the emission factor of air pollutant p of traffic type j in sector i.
When applying the above formulas to calculate the CO2 and pollutant emissions, activity level data were obtained according to the China Energy Statistical Yearbook, and EF data referred to the National Greenhouse Gas Emission Inventory Guidelines. The emission factors for conventional pollutants mainly referred to the IPCC report and other literature (Zhao et al., 2012). The detailed emission factors of CO2 and the four conventional pollutants for the different sectors are shown in Table 1.
Table 1 Emission factors of CO2 and air pollutants (Unit: kg tce‒1)
Sub-sector Energy type CO2 CO NOx SO2 PM2.5
Highway Gasoline 2025.28 234.16 17.56 0.58 0.44
Diesel 2168.69 29.27 23.42 5.41 1.06
LPG 1861.35 234.16 17.56 0.00 0.01
CNG 1624.54 11.71 17.58 0.00 0.03
Railway Diesel 2168.69 29.27 35.12 5.41 0.01
Aviation Kerosene 2106.84 2.93 8.78 5.83 0.96
Waterway Fuel oil 2201.98 29.27 43.91 5.83 0.80
In this study, the emission factors for energy consumption of electricity considered the indirect emissions from the power producers i.e., the CO2 and pollutant emissions generated by the fossil energy consumption of the power sectors. The specific emission factors calculated for electric vehicles are shown in Table 2.
Table 2 Emission factors of CO2 and air pollutants from electric vehicles in the BTH region (Unit: g kWh‒1)
Region CO2 CO NOx SO2 PM2.5
Beijing 615 0.00 0.05 0.03 0.01
Tianjin 811 0.00 0.06 0.02 0.01
Hebei 903 0.00 0.09 0.04 0.01

2.4 Definitions of policy scenarios

In order to project the future CO2 and conventional pollutant emissions from the major transport sectors, explore the reduction potential from different sectors and compare the co-control effects of various measures in the BTH region, this study defined five policy scenarios: the business as usual (BAU) scenario, transport structure adjustment (TSA) scenario, energy efficiency improvement (EEI) scenario, energy structure optimization (ESO) scenario and the comprehensive scenario (CP). The BAU scenario, which assumed that the government and relevant departments would not adopt additional reduction measures in future years, is the baseline situation for the comparative analysis. Three of the other scenarios (TSA, EEI, ESO) represented different policy options of low carbon and emission reduction, and the CP scenario assumes that all the possible control measures would be adopted, which could achieve the maximum emission reductions of CO2 and pollutants. The specific data for each policy scenario were determined according to the relevant national and local plans, such as, “China’s Medium and Long-term Energy Development Strategy Research Report”, “China’s Carbon Neutrality Research Report before 2060”, “The overall urban planning of Beijing (2016-2035)”, “Tianjin Comprehensive Transportation 14th Five-Year Plan”, “Hebei Province’s 14th Five-Year Plan and 2035 Vision Suggestions”, and others. The specific descriptions of the three policy scenarios are shown in Table 3.
Table 3 Detailed descriptions of the three policy scenarios considered in this study
Scenario Scenario definition Specific description
Transportation Structure Adjustment
(TSA)		 The proportion of railway transport in the BTH region would be increased, vigorously develop railway freight and waterway freight transport, build a national comprehensive three-dimensional transportation network with railway as the main trunk based on the highway, and make use of the comparative advantages of water transport and civil aviation In 2060, railway freight would account for 80% in Beijing, 60% railway and 35% waterway freight in Tianjin, and 70% railway and 25% waterway freight in Hebei Province
The population of private cars in Beijing, Tianjin and Hebei would be controlled within 5, 4 and 30 million, respectively
The sharing rate of passenger public transport in the city would be increased
Energy Efficiency Improvement (EEI) With the continuous innovation and progress of science and technology, the energy consumption per unit activity level of the terminal equipment in various transportation sectors would decrease; the continuous optimization of fuel economy and the popularization of new technologies would be increasingly extensive in the transport sectors Energy consumption of highway passenger turnover would decrease by 1.5%, road freight by 1%, air freight by 2%, fuel efficiency of private cars and taxis by 1.5%, and bus fuel efficiency by 1%. Energy consumption of highway passenger and freight traffic would decrease by 1% per year, air passenger and freight by 2% and 1%, fuel efficiency of private taxis by 1%, and bus fuel efficiency by 0.5%
Optimizing the Energy Structure (ESO) The rate of clean energy consumption in transportation sectors would increase, and the proportion of clean energy sources such as electricity, natural gas, biofuels and hydrogen energy used for all kinds of vehicles would increase In 2040, all buses and taxis in Beijing would be all electricity-driven; in 2050, road passenger transport and private cars would be all driven by new energies; in 2060, road freight would all be new energy driven; railway electrification would account for 80%, and aviation biofuel would account for 80%. In 2060, new energy used in the road passenger and freight in Tianjin and Hebei Province would account for more than 80%, the electrification of railway passenger and freight would account for 80%, aviation biofuel would account for 70%, clean energy such as biofuel applied in the water transportation industry would account for 60%

3 Results and discussion

3.1 Projection of energy consumption

In this study, the regression analysis method was used to establish a forecast function of transportation turnover, which further predicted the energy consumption of the transportation sector in the BTH region from 2019 to 2060.The predicted results of energy consumption under the baseline scenario and the four policy scenarios are shown in Fig. 2. With the continuous and rapid development of the transportation industry under BAU, the energy consumption of the transportation departments in Beijing, Tianjin and Hebei Province would show rapid growth trends.
Fig. 2 Projected total energy consumption from the transport sectors under each of the different scenarios in the BTH region, China

Note: tce, called coal equivalent, a unit of energy measurement for summing and comparing the different types of energy source according to the calorific value of standard coal.

In terms of single policy scenarios, ESO was the most effective for reducing energy consumption, especially after 2040. It could significantly reduce energy consumption, and the energy consumption levels of Beijing, Tianjin and Hebei in 2060 would be even slightly lower than their current values. The energy-saving effects under the TSA scenario in this region are quite different. The energy consumption in Tianjin and Hebei Province would be reduced effectively, however the effect would not be obvious in Beijing. Under the EEI scenario, the growth trend of energy consumption in the transportation sectors in the BTH region would be flattened, especially in Beijing, where the energy consumption of the transport sectors in Beijing in 2060 would only increase by 45% relative to 2018.

3.2 Projection of CO2 emissions

Based on the projections of energy consumption from the transportation sectors in the BTH region under different scenarios and assuming that the average emission factor of a given type of energy consumed by a certain transportation mode in the future years remains unchanged, the future CO2 emissions from the major transport sectors could be projected after the application of formula (2). The results for Beijing, Tianjin and Hebei are shown in Fig. 3a, 3b, 3c, respectively.
Fig. 3 Predictions of CO2 emissions from the transport sectors under the different scenarios in the BTH region, China
The CO2 emissions from the transport sectors in the BTH region under BAU would show an obvious upward trend. The expected emissions of Beijing, Tianjin and Hebei Province would increase by 59.3%, 80.2% and 80.1%, respectively, in 2030; and by 263.7%, 225.9% and 405.4%, respectively, in 2060. The main reason for the continuous growth in the emissions is the continuous growth of passenger and freight turnover caused by the economic and social development of this region.
Comparing the CO2 emission trends under different policy scenarios, the ESO would produce the best reduction effect of carbon emissions for the three sub-regions. The expected CO2 emissions in Beijing, Tianjin and Hebei Province would reach their peaks in 2040, 2035 and 2039, respectively. In 2060, the CO2 emission reductions of the transport sectors in Beijing, Tianjin and Hebei Province would be 76.7%, 58.1% and 64.2%, respectively, under this scenario. The CO2 emission trend would be flattened under the EEI scenario due to the considerable reduction from improved energy efficiency, especially in Beijing and Tianjin. The CO2 emission would present an obvious upward trend with a slight decrease in the total quantity under the TSA scenario, while the effect would be considerable in Hebei province. However, there would be almost no carbon reduction effect in Beijing under the TSA scenario, because the emissions would be dominated by aviation transportation.
Under the CP scenario, the CO2 emissions of the transport sectors in Beijing and Tianjin would decrease greatly year by year, and they are expected to meet the national target of carbon peaking in 2030, but the emissions in Hebei Province are expected to peak in 2034, which would not meet the national requirements. Finally, the CO2 emissions from the transportation sectors in Beijing, Tianjin, and Hebei would be reduced by 59.17%, 24.61%, and 46.75% relative to their levels in 2018.

3.3 Projection of pollutant emissions

Based on the energy consumption of the transportation sectors in the BTH region predicted by the LEAP model and the corresponding pollutant emission factors, the emissions of four conventional atmospheric pollutants (CO, NOX, SO2 and PM2.5) from 2020 to 2060 were predicted. The results are shown in Fig. 4.
Fig. 4 Projected emissions of pollutants from the transportation sectors under different scenarios in the BTH region, China

3.3.1 CO emissions

The energy-related CO emissions of the transport sectors in Beijing, Tianjin, and Hebei Province from 2020 to 2060 are shown in Fig. 4a. Under the BAU scenario, the CO emissions in the three sub-regions would increase to about 1.65, 1.90 and 14.70 million t, respectively, in 2060. The CO emissions from the transport sectors in Hebei under the TSA scenario would be reduced by 57%, which is more than in Beijing and Tianjin. The emission reduction effect of CO under the EEI scenario would be similar for the three sub-regions, at about 45%. The best reduction effect could be achieved under the ESO scenario for the BTH region, with a decrease rate of 94%-99% compared to the BAU scenario. The CO emissions mainly come from the road transport sector, which accounts for 69.45%-92.54% of the emissions in the whole region.

3.3.2 NOX emissions

The energy-related NOX emissions from the transport sectors in the BTH region from 2020 to 2060 are shown in Fig.4b. Under the BAU scenario, the NOx emissions from each of the transport sectors in Beijing, Tianjin and Hebei Province all showed clear growth trends. The NOx emission reduction rates under the CP scenarios would amount to 93.84%, 78.73% and 93.13% in 2060 for Beijing, Tianjin and Hebei, respectively. Comparing the reduction effects under the different scenarios, the order would be ESO>EEI>TSA. In the future, the aviation sector would become the main source of NOx emissions from transportation in Beijing. In 2060, the contribution rate of this sector is expected to be about 54.61%-92.24%. Tianjin’s waterway transport sector and road transport sector will be the main contributors to NOx emissions, and are expected to account for 47.09% and 33.54% of the total emissions in 2060, respectively. The NOx emissions in Hebei Province mainly come from the road transportation sector.

3.3.3 SO2 emissions

The SO2 emissions from the transportation sector in the BTH region would keep increasing and reach 2.3-3.8 times the current value in 2060 under the BAU scenario. The SO2 emission reduction rates would reach 92.97%, 84.24% and 83.19% in Beijing, Tianjin and Hebei, respectively. In terms of the policy scenarios, the SO2 reduction effect under the ESO is the best, with about 69.40%-81.06% reductions. In the future, the aviation sector in Beijing would be the main contributor of SO2 emissions, accounting for 92.40% of the total emissions. The SO2 emission of Tianjin’s transportation sector would mainly come from aviation and waterways. The road transport sector in Hebei Province contributes most of SO2 emissions, accounting for 60.03% of total emissions.

3.3.4 PM2.5 emissions

The PM2.5 emissions from the transport sectors in 2060 are expected to be 3.26, 2.46 and 3.60 times higher than the base year in Beijing, Tianjin and Hebei, respectively. Under the CP scenario, the PM2.5 emission reduction rates in 2060 would be about 93.59%, 91.71% and 94.78% in Beijing, Tianjin and Hebei respectively. Compared with the BAU scenario, the PM2.5 emissions would achieve a considerable reduction effect under the EEI and ESO scenarios in Beijing, with rates of 62.16% and 82.08%, respectively. Meanwhile, there is little effect on the reduction of PM2.5 under the TSA scenario. The emission reduction rates under the TSA, EEI and ESO scenarios in Tianjin were 35.10%, 51.08% and 73.72%, respectively, and the reduction rates of PM2.5 emission in Hebei Province would be 46.14%, 47.75% and 88.46%, respectively. Therefore, the emission reduction effect of ESO is the most significant. aaa

3.4 Potential for reduction of CO2 and the four pollutants

The potential for reduction of CO2 and four conventional pollutants from the major transport sectors under different policy scenarios in the BTH region in 2060 are shown in Fig. 5. The aviation sector has great potential for emission reduction in Beijing. The expected CO2 emissions would be reduced by 84.99%, and NOX, SO2 and PM2.5 emissions would decrease by 66.69%, 92.39% and 87.77%, respectively. However, the highway sector has the greatest contribution to the CO emission reduction, with a proportion of 92.03%.
Fig. 5 Emission reductions of CO2 and four common pollutants from the major transport sectors under different scenarios in the BTH region in 2060
The aviation and road transport sectors have the greatest potential for future CO2 emission reduction in Tianjin. Under the CP scenario in 2060, both sectors are expected to have reduced CO2 emissions by 15.49 million and 15.22 million t, respectively. The highway transport sector has greater potential for CO emission reduction, contributing 92.22% of the total. The waterway transportation sector is expected to have its NOX emissions reduced by 203,400 t relative to the baseline scenario, with a contribution rate of 49.22%. The aviation sector would have great potential for reducing SO2 and PM2.5 emissions, with reductions of 428 and 7000 t in 2060, contributing 52.45% and 44.32% of the total, respectively.
Due to its transportation structure, the reduction potentials of CO2 and the four pollutants in Hebei Province are mainly from the road transportation sector. Under the comprehensive scenario in 2060, the emission reductions of CO2, CO, NOX, SO2 and PM2.5 would be 130.40 Mt, 14.58 Mt, 1.23 Mt, 67100 t and 33600 t, accounting for 92.40%, 99.85%, 94.63%, 78.84% and 88.67% of the total reduction, respectively.

3.5 Evaluation of the co-reduction effect for CO2 and the four pollutants

Using the method of the coordinated system from Mao et al. (2021), with the X-axis representing the CO2 emission reduction, and the Y-axis illustrating the air pollutant reduction, the co-reduction effects of different measures for the major transport sectors were analyzed and the results are shown in Fig. 6. There are big differences in the co-reduction effects for different sub-regions. In Beijing, the best coreduction effect would be seen in the aviation sector under the EEI, ESO and CP scenarios. The waterway and aviation sectors in Tianjin would both have better co-reduction effects. Obviously, the best co-reduction effect would be achieved for the highway sector under all the policy scenarios in Hebei Province. Overall, the reductions of CO2 and pollutant emissions in the four transport sectors in the BTH region are ranked as CP>ESO>EEI>TSA.
Fig. 6 The co-reduction effects on CO2 and pollutants from different transport sectors under different scenarios in the BTH region in 2060

4 Conclusions

This study used the LEAP model to project the energy consumption, greenhouse gases and conventional pollutants from four transport sectors (highway, railway, waterway and aviation) in the BTH region from 2020 to 2060, and their emission reduction potentials were then analyzed and the co-reduction effects on different transport sectors under different policy scenarios were compared.
This analysis indicated that the CO2 emissions from the transport sectors in the BTH region would increase by 2.3-4.1 times relative to the levels in 2018 under the BAU scenario. The estimated CO, NOx, SO2 and PM2.5 emissions from the transport sectors in this region would increase by 3.09, 2.81, 3.18 and 3.22 times in 2060 compared to the base year. Under the CP scenario, the CO2 emissions from the transport sectors in Beijing, Tianjin, and Hebei Province would be reduced by 38.2%, 42.8%, and 36.3%, respectively, in 2030, compared with the baseline scenario, while they would be reduced by 88.78%, 76.86% and 83.20% in 2060, respectively. In terms of conventional pollutants, the emission of CO, NOX, SO2 and PM2.5 from the transport sectors in Beijing, Tianjin and Hebei would be reduced by 48.60%, 35.01%, 33.79% and 42.48% in 2030, respectively. The maximum emission reduction rate is expected to reach 78.73%-99.34% in 2060.
Among the single-measure scenarios, ESO has the best carbon reduction effect, as the CO2 emission reduction of the transport sectors would be about 58.11%-76.65% in 2060.The energy structure optimization measures have the best effect on pollutant emission reduction, especially for CO where the reduction rate could be more than 88.64%, and the emission reduction rates for other pollutants are about 69.40%-92.48%. The best co-reduction effects could be achieved for the road transport and aviation sectors under the energy structure optimization scenario.
Therefore, in order to achieve the “double carbon” policy goal, the transportation department of the BTH region should actively promote the transportation structure optimization, reduce the volume of road freight and increase the volume of railway freight, to take advantage of the roles of railways and waterways in the long-distance transportation of bulk materials. It is also urgent and necessary to vigorously promote energy-saving and new energy vehicles, improve the cleaning of ships and ports, actively explore the application of biomass fuel to replace aviation kerosene, and other measures, to promote the energy transformation of the transport sectors as soon as possible.

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