Time-Varying Dynamics and Socioeconomic Determinants of Energy Consumption and Truck Emissions in Cold Regions

Abstract

Facing the increasingly severe challenges of global climate change, China has established clear “dual carbon” goals, with the core objective of achieving carbon peak by 2030 or earlier. However, carbon emissions from the road freight industry have remained higher for many years; understanding and estimating the characteristics of truck carbon emissions are critical for developing a low-carbon transportation system. This study takes Heilongjiang Province, a typically cold region, as a case study. By employing the growth curve method, we predicted the time for achieving carbon peak and constructed an improved STIRPAT model to identify key drivers and pathways for emission reduction in the road freight system. The research results show that only by committing to using the economy to reduce carbon emissions and improve energy intensity can the overall carbon emissions of Heilongjiang Province’s cargo transportation system achieve the “dual carbon” goals as soon as possible. If we develop according to the optimistic scenario proposed in this article, by 2030, the total quantity of trucks will reach about 933,720, and the carbon emissions per vehicle will reach about 178.14 t. If we actively increase the proportion of new energy trucks in the overall quantity of trucks, the peak time is expected to be achieved around 2030. The improvement of technological efficiency (e.g., lowering energy intensity) and the advancement of economic development have been identified as effective pathways for carbon emission reduction. Empirical studies indicate that these measures can achieve emission reduction impacts that are approximately 60 times and 10 times greater, respectively, in terms of efficiency, compared to baseline scenarios. Furthermore, energy intensity improvements and structural shifts toward low-carbon vehicles are critical to expediting peak attainment. This study provides a methodological framework for cold-region emission projections and offers actionable insights for policymakers to design tailored emission reduction pathways in the road freight transportation industry.

1. Introduction

Since the 1990s, global climate warming has increasingly received widespread attention from the international community. Carbon emissions are one of the leading causes of climate warming. The emissions from the transportation sector account for a significant proportion in global carbon emissions, with trucks accounting for approximately 15% of global CO₂ emissions [1]. Freight diesel vehicles, with its large work volume and hard working environment, lead to huge carbon emissions. Trucks remain a concern as they undertake critical long-distance transportation [2]. However, the transportation industry supports the country’s economic development and maintains the people’s basic life. Data shows that, in 2021, the positive dynamic proportion of smart energy in Northeast China has decreased significantly [3]. Therefore, how to ensure the rapid development of the economy in Northeast China and achieve the severe test of the goal of “dual carbon” has become a key area for us to increase research efforts. Improving the carbon emission statistics and monitoring system and forming quantitative monitoring tools are undoubtedly the driving force behind energy conservation and emission reduction.

More and more calculation methods of carbon emissions enter the researchers’ eye. Various methods have been used to characterize the GHG emission performance of vehicles. Early GHG evaluations in China used non-native vehicle emission factor models that were not validated against real-world emissions data, leading to inaccurate predictions [4]. Based on analysing the correlation between transportation characteristics and economic, social, and environmental factors, key variables were identified and subsequently used to forecast air passenger and cargo demand. The study revealed that changes in demand have significant implications for energy consumption and CO₂ emissions [5]. Numerous studies have centred on forecasting transportation energy demand, exploring diverse predictive models and methodologies; Recent advances include non-parametric approaches (e.g., MARS [6]), hybrid AI frameworks (CNN-WHO [7]), and topology-aware deep learning architectures (graph-convolutional RNNs [8]).

Quantitative evaluation of carbon emissions establishes a foundation for making carbon emission reduction policy. In previous studies, it was believed that car characteristics, driving characteristics, and the road traffic environment mainly affect the carbon emissions of a single vehicle [9]. For the study of the total carbon emissions in a region or a sector, it is believed that factors such as the quantity of vehicles and fuel structure also affect the total carbon emissions [10].

Scholars at home and abroad have conducted extensive research on carbon emissions in the transportation industry, covering multiple geographical scales such as national, regional, and urban areas; for example, ref. [11] calculated the total carbon emissions of diesel trucks operating in various cities in Yunnan Province. From 2016 to 2020, the total carbon emissions of diesel trucks operating in the province showed a trend of first decreasing and then increasing and will continue to rise after 2020. It is proposed that Yunnan Province needs to adjust its current transportation structure as soon as possible, fully tap the potential of transportation resources, and formulate targeted transportation emission reduction policies. According to ref. [12] there remains a notable gap in specialized research focusing on Northeast China, particularly provinces with unique geographical and climatic conditions like Heilongjiang Province. Heilongjiang Province (located at 121°11′–135°05′ E longitude and 43°26′–53°33′ N latitude) presents a compelling and critical case study for several reasons:

As a major province in China’s frigid zone, Heilongjiang endures extremely cold, prolonged winters, imposing unique demands on its freight industry. Low temperatures severely degrade battery performance and hinder charging for new energy trucks—particularly battery electric vehicles (BEVs)—due to freezing conditions [13,14], creating a critical barrier to electrification-based decarbonization (the dominant pathway in milder climates). Nevertheless, the province faces a dual challenge: revitalizing its economy (historically reliant on heavy industry, agriculture, and resource extraction, with road freight dominating logistics) while meeting ambitious carbon reduction targets. Data from 2023 shows that road freight accounts for 68% of total freight volume, with winter fuel consumption per 100 km 20–30% higher than the national average. This reflects a carbon intensity profile distinct from warmer regions, shaped by economic structure and climate-driven efficiency losses.

Current research lacks systematic analysis of Heilongjiang’s freight emission drivers and scenario-specific predictions for its socioeconomic and environmental context. National/regional models often underestimate the impact of extreme cold on vehicle operation and energy use. Targeted research for Heilongjiang is vital not only for the province but also for informing freight decarbonization strategies in other cold-climate regions globally. Developing accurate monitoring tools and predictive models for such environments is key to designing effective, feasible carbon reduction policies.

Therefore, this study aims to bridge this research gap by focusing on the freight transportation sector in Heilongjiang Province. Our primary objectives were the following: (1) to analyse the key factors influencing carbon emissions from freight trucks under Heilongjiang’s unique cold-climate conditions; (2) to develop a more accurate quantitative framework for monitoring and predicting freight carbon emissions in this specific context; and (3) to conduct scenario analyses exploring potential pathways for reducing emissions while supporting regional economic needs. The findings are expected to provide a scientific basis for formulating targeted and effective carbon reduction policies for the freight sector in Heilongjiang and similar cold regions.

The remainder of this paper is organized as follows: Section 2 reviews the relevant literature. Section 3 presents a prediction model for the quantity of trucks, calculating the quantity of trucks in the future. Section 4 studies various influencing factors on carbon emissions quantitatively. Section 5 summarizes energy-saving and low-carbon emission reduction paths based on the degree of influence of the factors. Finally, the paper concludes with future directions in Section 6.

2. Literature Review

2.1. Transportation Carbon Emission Measurement Method

Traffic carbon emissions primarily come from mobile sources. In comparison to fixed sources such as industry and construction, traffic emissions have more uncertainties in measurement. The methods for calculating mobile source carbon emissions can be classified into two categories: top-down models and bottom-up models [15].

The top-down model estimates emissions at multiple scales by using fuel consumption data from specific regions and calculates transport-related emissions based on energy consumption and emission factors [16]. Based on this method, the researchers measured road transport emissions in Ireland, India, and Colombia [17,18,19]. Many emission inventories have been developed using the top-down method in China [20]. The top-down approach is generally considered more accurate for quantifying overall fuel consumption, but it is difficult to represent the carbon emissions of specific transportation activities. When calculating the change of carbon emissions of a single truck, the bottom-up approach is more flexible than the top-down approach.

The top-down approach is generally considered more accurate for quantifying overall fuel consumption, but it is difficult to represent the carbon emissions of specific transportation activities. When calculating the change of carbon emissions of a single truck, the bottom-up approach is more flexible than the top-down approach. The bottom-up approach relies on vehicle-level emissions data by estimating carbon emissions based on changes in the quantity of vehicles, the distance travelled by each vehicle, and the fuel used per kilometre [21,22]. Existing research has applied this method to carry out a large quantity of studies on the carbon emissions of freight transportation in China, India, Brazil, and other countries [23,24,25,26]. Recent empirical research underscores the complexity of emission reductions in diesel trucks [27]. Considering that existing research rarely addresses cold climate conditions, this study focuses on Heilongjiang Province—a representative cold region in China—to investigate the dynamics of carbon emissions from road freight transportation.

2.2. Drivers of Carbon Emissions from Trucks

Relevant studies have shown that carbon emissions of road freight transport account for the highest proportion in the total carbon emissions of freight transport [28]. In order to effectively reduce carbon emissions of road freight transport, it is crucial to determine the driving factors affecting carbon emissions so that effective strategies can be determined [29].

Existing studies have employed a range of approaches to explore the driving factors of the variation in carbon emissions; Logarithmic Mean Divisor Index (LMDI) decomposition [30,31] and regression models are commonly used methods. The LMDI algorithm has excellent adaptability, usability and interpretability of results, and is renowned for its capacity to dissect and quantify the contributions of various factors to carbon emission changes [32]. However, the LMDI model is mainly used to decompose factors at the macrolevel, and its application at the microlevel is limited, and its implications for policy are relatively limited. And it has high data requirements, usually requiring complete time series data.

When selecting models to analyse the factors affecting carbon emissions, the STIRPAT model is based on the IPAT model and has a clear theoretical framework for environmental impact, making it suitable for analysing the impact of human activities on the environment. It allows the introduction of multiple explanatory variables, flexible handling of factors such as population, wealth, technology, etc., and can expand variables according to research needs. And it can capture nonlinear relationships between variables in exponential form, which is more in line with the complexity of actual economic and environmental systems.

Previous applications of the STIRPAT model in transportation sectors primarily considered variables like GDP, population growth, and traffic volume but often overlooked region-specific constraints such as climate-induced energy inefficiencies. This study improves the STIRPAT model by incorporating socioeconomic variables that reflect the regional characteristics of Heilongjiang Province—such as per capita secondary industry output, energy intensity, urbanization rate, and freight turnover—in order to more accurately identify the key factors influencing road freight carbon emissions in cold regions.

2.3. Truck Emission Reduction Route

After determining the driving factors affecting carbon emissions, the future carbon emissions are predicted, and later corresponding emission reduction strategies are proposed. In developing CO₂ reduction strategies, economic measures (like reducing empty runs, enhancing efficiency, and improving consolidation) play a key role in the short-to-medium term, whereas societal measures (such as national planning, efficient transportation towns, and new infrastructure) are crucial for long-term reductions [33]. For example, through the intelligent control of the thermal environment in the room, the energy consumption for the operation of the building facilities is reduced [34]. Technical means are the most direct means for trucks to achieve individual emission reduction, including driver assistance technology [35], electrification technology [36,37,38], and alternative fuel [39,40,41].

The widespread adoption of electric vehicles (EVs) presents a significant opportunity for reducing carbon emissions in the transportation sector [42]. As zero tailpipe emission vehicles, EVs are becoming an essential component of the transition to a low-carbon economy. Numerous studies have highlighted the substantial potential of EVs to lower carbon emissions from road transport [43]. However, in cold regions such as Heilongjiang Province, the promotion of EVs faces certain challenges, particularly the reduced battery efficiency at low temperatures [44]. Nevertheless, with the continuous advancement of battery technology, batteries optimized for cold climates are being developed, which is expected to mitigate these issues [45]. The integration of EVs, along with the construction of supporting charging infrastructure, can significantly reduce carbon emissions and contribute to the achievement of global decarbonization goals.

Unlike previous studies that mainly propose emission reduction policies in generalized contexts, this study develops tailored policy scenarios—namely, optimistic, baseline, and pessimistic—for cold regions where electrification faces distinct implementation challenges, thereby enhancing the realism and policy relevance of decarbonization pathways.

2.4. Research Gaps and Contributions

Compared to previous research, the gaps addressed by this study are the following: (1) Existing studies predominantly focus on the national level or southern regions of China. In contrast, this study focuses specifically on Heilongjiang Province, a representative cold region, thereby filling a geographical research gap in low-temperature transportation contexts. (2) While the prior literature often employs linear regression, the LMDI method, or traditional STIRPAT models, this study integrates the Gompertz growth curve to forecast vehicle ownership and further enhances the STIRPAT model by incorporating energy intensity and other localized socioeconomic factors. This methodological improvement better captures nonlinearities and region-specific emission drivers. (3) Many existing studies pay limited attention to climate adaptability or policy implementation scenarios when constructing carbon reduction pathways. This study develops three scenario trajectories—pessimistic, baseline, and optimistic—that align with China’s 2030 carbon peaking target, thereby enhancing the policy relevance and cold-region adaptability of the carbon emission forecasts and improving their practical value for decision-making. Thus, this study addresses the research gap in freight transport carbon emissions under cold climate conditions and offers valuable insights into how integrating region-specific socioeconomic factors, vehicle structure dynamics, and scenario-based forecasting can inform effective low-carbon policy design in cold regions like Heilongjiang.

3. Methodology

3.1. Data Collection and Analysis

The data of trucks typically comes from government statistics [46]. The data in this article is sourced from the Automobile Industry Association including national public security license plate registration data. All registered and annually audited vehicles are recorded in the database. However, vehicles that have been scrapped and those that have been off the trial for more than two years are excluded. The actual data is therefore considered to be a close approximation of the real quantity. In other words, the total quantity of vehicles registered by the DMV minus the quantity of scrapped vehicles and vehicles that have been out of review for more than two years is equal to the actual quantity. This calculation method is recognized by the public. This paper employs three methods, including a manual survey method, a technical survey method, and a literature and information method, to conduct a comprehensive survey of the data on trucks in the Heilongjiang province, China.

3.1.1. Basic Data

The Heilongjiang Statistical Yearbook (2023) indicates that the freight transport volume of Heilongjiang Province reached 595 million tons by 2022. Additionally, the quantity of civilian trucks reached 824,000, comprising 411,000 ordinary trucks and 413,000 special trucks. This has further promoted the rapid development of the freight transport industry. The weight of the truck itself and the payload, as is known to all, will impact carbon emissions during the process of cargo transport. In accordance with national regulations on the classification of trucks, this article has separately studied based on the total mass of the different designs. The resulting data in the road freight system is in

Table 1. The quantity of various types of trucks in Heilongjiang Province in 2022.

Classification		Quantity (Vehicles)	Total (Vehicles)
Classified by load capacity	Heavy-duty trucks	249,474	824,436
	Medium-duty trucks	77,661
	Light-duty trucks	497,217
	Micro trucks	74
Classified by fuel type	Diesel trucks	770,106	824,436
	Petrol trucks	48,641
	Natural gas trucks	2061
	Liquefied petroleum trucks	3545
	Other trucks	1237

.

Concurrently, the mass fractions of carbon in different types of fuel vary. In accordance with the national classification criteria, trucks utilizing disparate fuels warrant distinct study and calculation. The various types of fuel employed are distinguished between gasoline, diesel, and other fuel trucks. Given the limited penetration of electric and hydrogen trucks, which are clean energy sources that do not produce carbon dioxide or produce only minimal quantities of carbon dioxide during combustion, this study focuses exclusively on diesel, petrol, natural gas, and liquefied petroleum gas (LPG) trucks to ascertain the specific quantities of each type of truck, as shown in

Table 2. The quantity of roads in Heilongjiang Province from 2014 to 2022 (vehicles).

Year	2014	2015	2016	2017	2018	2019	2020	2021	2022
Quantity	617,730	603,118	613,708	610,734	647,419	683,939	740,699	793,363	824,436

.

3.1.2. Data Analysis

By the conclusion of 2022, the distribution of operating trucks in terms of fuel type within the province was as follows: 5.90% were petrol trucks, 93.41% were diesel trucks, 0.43% were LPG trucks, natural gas trucks accounted for 0.25%, dual-fuel trucks accounted for 0.004%, and purely electric trucks accounted for 0.009%. The specific situation of the above data is shown in Figure 1. At the conclusion of 2022, the distribution of no-individual operating trucks in the province is as follows: heavy-duty trucks (30.26%), medium-duty trucks (9.42%), light-duty trucks (60.31%) and micro trucks (0.009%), as shown in Figure 2.

From 2014 to 2022, the average annual growth rate of the quantity of trucks in Heilongjiang Province was 3.73%, as shown in Figure 3. Furthermore, the growth rate of the number of trucks accelerated from 2017 onwards, with an average growth rate of 5.08%.

3.2. Truck Holdings Forecasting Model

3.2.1. Application of the Growth Curve Method

The Gompertz curve model was first developed in 1825 by the British statistician and mathematician B. Gompertz. It has been widely used to study the laws of growth and development of things. Initially, the model displays a slow growth phase, followed by a period of accelerated growth, and finally, a phase of gradual convergence towards a limit value. The basic equation of the Gompertz model is shown as follows:

$$y=K{a}^{b^t}$$

(1)

where y is the quantity of trucks over the course of a year in Heilongjiang Province, China; K represents the maximum limit value of y; and K > 0, 0 < a < 1, and 0 < b < 1.

The following will be combined with the actual data using the “three sums method” for the specific calculation of the parameters. The specific calculation formula can be found in the Appendix A.

By repeatedly calculating the evaluation indicators of the model at different saturation rates, it was determined that, when the saturation value is $170,845.98$, the predictive performance of the model is good, and the predicted values for historical data are close to real data.

The least squares method is employed in MATLAB (R2020a) to fit Equation (2) linearly. And the derived parameters were substituted into the Gompertz function model, as shown in the following:

$$y=170845.98\times {0.3175}^{{0.965}^t}$$

(2)

As shown in

Table 3. Forecast quantity of truck in Heilongjiang Province (vehicles).

Year	2023	2024	2025	2026	2027	2028	2029	2030
Quantity	786,859	808,503	829,954	851,193	872,205	892,972	913,482	933,720

, the quantity of truck in Heilongjiang Province from 2023 to 2030, as derived from Equation (2), is 933,720.

The prediction results show that the quantity of road trucks in Heilongjiang Province is expected to increase, possibly due to the high demand for the transportation of bulk commodities such as grain, coal, and timber, as a major agricultural and resource rich province. The railway and waterway transportation networks within the province may be relatively insufficient, and road transportation still dominates, which is difficult to change in the short term. Secondly, Heilongjiang Province has a vast territory, and some areas have inconvenient transportation. Highway transportation is more flexible and suitable for dealing with complex geographical and climatic conditions. In some regions, the application of logistics technology may be relatively lagging behind, and trucks are still the main mode of transportation, leading to an increase in the quantity of trucks.

This study used historical data for model parameter estimation, calculating indicators such as average relative error, relative error variance, average absolute relative error, and Pearson correlation coefficient. The indicator calculation formula is shown in (3)–(8). Assuming a zero order residual, it is shown in Formula (3).

$${\epsilon }_1^{(0)}=x_i^0-{\hat{x}}_i^0,i=1,2,\dots ,n$$

(3)

The relative error is shown in Formula (4).

$${\delta }_i={\displaystyle \frac{{\epsilon }_i}{x_i}}\times 100\%$$

(4)

The average relative error is shown in Formula (5).

$$\overline{\phi }={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n{\delta }_i}$$

(5)

The formula for calculating the relative error variance is shown in Formula (6).

$$s^2={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n{({\delta }_i-\overline{\phi })}^2}$$

(6)

The formulas for calculating the average absolute relative error are shown in Formula (17).

$$MAD={\displaystyle \frac{1}{n}}{\displaystyle \sum _{i=1}^n\left|{\delta }_i\right|}$$

(7)

The Pearson correlation coefficient is used to measure the strength and direction of the linear relationship between predicted and actual values, and the calculation formula is shown in Formula (8).

$$r={\displaystyle \frac{{\displaystyle \sum (x_i-\overline{x})(y_i-\overline{y})}}{\sqrt{{\displaystyle \sum {(x_i-\overline{x})}^2}{\displaystyle \sum {(y_i-\overline{y})}^2}}}}$$

(8)

Among them, $x_i$ represents the predicted value, ${\overline{x}}_i$ represents the average of the predicted values, $y_i$ represents the observed value, and ${\overline{y}}_i$ represents the average of the observed values.

3.2.2. Scenario Analysis Setting

Scenario analysis is a statistical method used for prediction and decision-making, often combined with the STIRPAT model, to analyse possible scenarios under uncertain conditions, study the development of carbon emission factors, and provide data support for carbon reduction policies. It is widely used in energy demand and carbon emission prediction [35,36,37,39]. In order to gain further insight into the impact of the quantity of trucks on the change of carbon emissions from the freight transport system in Heilongjiang Province, this study employs the carbon emission reduction policy as a reference point. Additionally, it considers the potential influence of structural adjustments to the urban freight transport system and the application of emerging technologies on the fuel and shape of trucks in the future. This paper combines the carbon peak target with the fuel structure of trucks in 2022, updates the fuel structure of trucks, increases the proportion of clean energy vehicles, reduces the proportion of vehicles with high carbon emissions, and sets a pessimistic scenario, a baseline scenario, and an optimistic scenario to predict the fuel structure of trucks. This is based on the fuel structure of trucks in 2022.

When setting up scenarios, many factors need to be considered, including current and future policies that may have a significant impact on the direction and speed of urban transportation development. So that research is synchronized with the actual situation of urban development. In addition, the development of technology has a significant impact on urban infrastructure, transportation, and energy use.

The Action Plan for Carbon Peak by 2030 issued by the State Council proposes to promote efficient and low-carbon transportation vehicles and accelerate the elimination of high-energy-consuming and high-carbon-emitting vehicles. The “Development Plan for New Energy Vehicle Industry (2021–2035)” proposes that, by 2025, the sales volume of new energy vehicles should reach about 20% of the total sales volume of new vehicles. The “Three Year Action Plan for Winning the Blue Sky Defense War in Heilongjiang Province” points out the importance of reducing the pollution of diesel trucks by developing an action plan. On the one hand, the specific measures proposed include balancing energy, road, and vehicle development, and implementing green diesel vehicles (engine), green transportation, green oil actions. So that the total pollution emissions of diesel trucks have significantly decreased. On the other hand, the specific measures proposed include strengthening the supervision and management of the production, sales, registration, use, inspection, and maintenance of diesel trucks. It is more important to implement emission testing and mandatory maintenance systems for vehicles in use.

According to research data from the transportation industry management department of Heilongjiang Province, the proportion of various types of trucks in the highway freight system of Heilongjiang Province from 2013 to 2022 is shown in Figure 4. From 2013 to 2018, the largest proportion of road freight systems in Heilongjiang Province was light diesel trucks, followed by heavy natural gas trucks. However, the proportion of light diesel trucks will sharply decrease in 2022, while the proportion of heavy natural gas trucks will exceed 80%, which is in line with the policy guidance trend of the country and Heilongjiang Province.

According to the “Development Plan for New Energy Vehicle Industry (2021–2035)”, it is recommended that, by 2025, the sales of new energy vehicles should reach about 20% of the total sales of new cars. Based on historical data from Heilongjiang Province, as of 2023, there will be 13 pure electric trucks, accounting for 0.01%, and even fewer vehicles of other new energy types. The overall development of new energy freight vehicles in Heilongjiang Province is insufficient, and so in an optimistic scenario, we assume that the share of alternative fuel vehicles will reach 20% by 2030. The research object of this article is the freight vehicles of the regional freight system, which is specific and has a small scope. There is no specific document regulation on the number of vehicles in the province. According to the “Implementation Plan for Large scale Equipment Renewal and Consumer Goods Trade in in in Heilongjiang Province by 2025”, it is pointed out that, by 2025, more than 10,000 old operating trucks in Heilongjiang Province will be scrapped and replaced, accounting for about 8% of the total inventory. According to the “Action Plan for Fully Implementing Xi Jinping’s Ecological Civilization Thought and Promoting the Development of Green Transportation in the Province” (referred to as the “Action Plan”), Heilongjiang Province will eliminate 30,000 diesel trucks below National III level by 2026, accounting for about 24% of the total number of vehicles. From the historical inventory data, the total inventory shows a decreasing trend, and the increase in the number of new energy trucks cannot fully fill the gap of elimination. Taking into account the above considerations, this study assumes that the share of new energy vehicles will reach 20% by 2030 under an optimistic scenario.

According to the data provided by the transportation industry management department of Heilongjiang Province, the proportion of operating trucks in Heilongjiang Province in 2022 is shown in Figure 5. This paper is based on the fuel and vehicle structure of trucks in 2020, combined with the carbon peak target, to update the fuel structure of trucks, increase the proportion of clean energy vehicles, reduce the proportion of vehicles with high carbon emissions, and set pessimistic, baseline, and optimistic scenarios for analysis. Assuming three scenarios, the proportion of each fuel type in 2030 will change at an average rate from 2022 to 2030, as shown in the

Table 4. Parameters in different scenarios.

Scenario	Diesel Trucks (%)	Petrol Trucks (%)	Natural Gas Trucks (%)	LPG Trucks (%)	Others (%)
Pessimistic	62.00	17.00	5.00	8.00	8.00
Baseline	60.00	17.00	5.00	8.00	10.00
Optimistic	50.00	13.00	7.00	10.00	20.00

. According to the “New Energy Vehicle Industry Development Plan (2021–2035)”, it is proposed that, by 2025, the sales of new energy vehicles should reach about 20%. Based on this, this study assumes that the proportion of new energy trucks has reached 20% in the optimistic scenario, while it is only 10% in the baseline scenario and 8% in the pessimistic scenario.

The proportion of trucks in each predicted year is as follows.

(1)

Pessimistic Scenario

The pessimistic scenario takes into account the recent talent loss and limited economic development in Heilongjiang Province, which has hindered the development of the road freight industry, accelerated the elimination of old cars. It is considered that new energy vehicles are unable to adapt to the low-temperature and harsh weather conditions, as well as the road freight market in Heilongjiang Province.. By 2030, the projected composition of truck types would be diesel vehicles (62%), petrol vehicles (17%), natural gas vehicles (5%), LPG vehicles (8%), and other vehicle types (8%). Correspondingly, the annual growth rates from 2023 to 2030 are estimated at −3.49%, 1.23%, 0.53%, 0.95%, and −0.78%, respectively. Based on these scenario assumptions, the projected fuel structure for trucks is shown in

Table 5. Pessimistic scenario: truck fuel mix projections.

Year	Diesel Trucks (%)	Petrol Trucks (%)	Natural Gas Trucks (%)	LPG Trucks (%)	Others (%)
2023	86.43	8.39	1.23	2.00	1.95
2024	82.94	9.62	1.76	2.95	2.73
2025	79.45	10.85	2.30	3.79	3.61
2026	75.96	12.08	2.84	4.64	4.48
2027	72.47	13.31	3.38	5.48	5.36
2028	68.98	14.54	3.92	6.32	6.24
2029	65.49	15.77	4.46	7.16	7.12
2030	62.00	17.00	5.00	8.00	8.00

.

(2)

Baseline Scenario

The baseline scenario reflects the continuation of the current trend, with various factors maintaining a moderate development speed. Relevant departments such as transportation have not taken any measures, and the urban socioeconomic, freight volume, turnover, and motor vehicle ownership are developing according to historical trends. The transportation structure and energy ratio remain unchanged. Based on the energy ratio and freight volume forecast results in Table 3, the data for 2030 under the baseline scenario was obtained. By 2030, the expected composition of truck types is projected as follows: diesel vehicles (60%), petrol vehicles (17%), natural gas vehicles (5%), LPG vehicles (8%), and other vehicle types (10%). Correspondingly, the annual growth rates from 2023 to 2030 are estimated at −3.71%, 1.23%, 0.53%, 0.95%, and −1%, respectively. The scenario assumptions are based on the accelerated development of clean diesel engine technology and the widespread adoption of infrastructure such as charging piles, which enhanced the performance-price advantage for hydrogen and electric trucks. These trucks are capable of meeting the needs of short-range road freight transport in terms of payload and mileage, and they are also deployed in the gradual transfer of long-distance freight transport to rail. By 2030, the proportion of electric logistics vehicles will have reached 20% of the anticipated level, in accordance with the policy objective. The projected fuel structure for trucks is shown in

Table 6. Base case: truck fuel mix projections.

Year	Diesel Trucks (%)	Petrol Trucks (%)	Natural Gas Trucks (%)	LPG Trucks (%)	Others (%)
2023	85.99	8.39	1.23	2.00	2.39
2024	82.28	9.62	1.76	2.95	3.39
2025	78.57	10.85	2.30	3.79	4.49
2026	74.86	12.08	2.84	4.64	5.58
2027	71.15	13.31	3.38	5.48	6.68
2028	67.44	14.54	3.92	6.32	7.78
2029	63.73	15.77	4.46	7.16	8.88
2030	60.00	17.00	5.00	8.00	10.00

.

(3)

Optimistic scenario

This scenario incorporates policy requirements from national and Heilongjiang provincial documents on “carbon peak, carbon neutrality” and transportation development. While actively promoting the implementation of development plans, the development of new energy vehicle technology is accelerating, highlighting its performance and price advantages. Charging stations are steadily becoming popular, greatly improving battery range. In this scenario, battery electric vehicles and hybrid electric vehicles develop synchronously. The proportion of truck structure in the scenario of the synchronous development of new energy is shown in Figure 4, Figure 5 and Figure 6. By 2030, the projected composition of truck types is estimated as diesel vehicles (50%), petrol vehicles (13%), natural gas vehicles (7%), LPG vehicles (10%), and other vehicle types (20%). Correspondingly, the annual growth rates from 2023 to 2030 are projected at −4.82%, 0.79%, 0.77%, 1.06%, and 2.20%, respectively. This scenario assumes that the development of engine technology using cleaner fuels accelerates cost reductions, the implementation effect of emission reduction and low-carbon policies achieves greater success, charging infrastructure becomes widely available, and unmanned freight transport technology develops extensively. The electrification rate of logistics vehicles by 2030 has exceeded the policy estimates of 20%. Under this scenario, the fuel structure of trucks is projected as shown in

Table 7. Optimistic scenario: truck fuel mix projections.

Year	Diesel Trucks (%)	Petrol Trucks (%)	Natural Gas Trucks (%)	LPG Trucks (%)	Others (%)
2023	83.77	7.51	1.65	2.55	4.52
2024	78.95	8.30	2.42	3.61	6.72
2025	74.13	9.09	3.19	4.67	8.92
2026	69.31	9.88	3.96	5.73	11.12
2027	64.49	10.67	4.73	6.79	13.32
2028	59.67	11.46	5.50	7.85	15.52
2029	54.85	12.25	6.27	8.91	17.72
2030	50.00	13.00	7.00	10.00	20.00

.

3.2.3. Forecast of Truck Model Structure

Based on historical trends in the truck model structure by different fuel types, the future composition of heavy-duty, medium-duty, light-duty, and micro freight trucks in Heilongjiang Province is projected using 2022 data as a reference. However, as the country gradually emphasizes carbon emissions from freight systems and implements policies to mitigate them, the adoption of new energy vehicles in the road freight system cannot be simply characterized. To improve the calculation of carbon emissions from the operation of trucks in Heilongjiang Province, based on the prediction of fuel structure of trucks, it is assumed that the structural evolution of vehicle models under different fuel types follows a consistent trend across all scenarios. The detailed results are shown in

Table 8. Forecasts of the vehicle type structure of truck holdings (%).

Year	Diesel Truck			Petrol Truck		Natural Gas Truck				LPG Truck
	Heavy	Medium	Light	Light	Mini	Heavy	Medium	Light	Mini	Medium	Light
2023	35.51	4.39	56.99	99.97	0.03	35.05	5.91	58.98	0.17	9.83	90.17
2024	37.87	4.19	55.28	99.97	0.03	37.18	6.47	56.27	0.17	11.28	88.72
2025	40.23	3.99	53.57	99.97	0.03	39.31	7.03	53.56	0.17	12.73	87.27
2026	42.59	3.79	51.86	99.97	0.03	41.44	7.59	50.85	0.17	14.18	85.82
2027	44.95	3.59	50.15	99.97	0.03	43.57	8.15	48.14	0.17	15.63	84.37
2028	47.31	3.39	48.44	99.97	0.03	45.70	8.71	45.43	0.17	17.08	82.92
2029	49.67	3.19	46.73	99.97	0.03	47.83	9.27	42.72	0.17	18.53	81.47
2030	52.03	2.99	45.02	99.97	0.03	49.96	9.83	40.01	0.17	19.98	80.02

.

3.2.4. Prediction Results Output

Based on the projected fuel structure of trucks under the pessimistic scenario in Table 5 and the forecasted truck model distribution in Table 8, the integrated prediction results for truck under this scenario are derived in

Table 9. Pessimistic scenario: truck quantity forecast results (vehicles).

Year	Diesel Truck			Petrol Truck		Natural Gas Truck				LPG Truck
	Heavy	Medium	Light	Light	Mini	Heavy	Medium	Light	Mini	Medium	Light
2023	241,497	29,856	387,579	65,998	20	3392	572	5708	16	1547	14,190
2024	253,946	28,097	370,692	77,755	23	5291	927	8007	24	2690	21,160
2025	265,276	26,310	353,240	90,023	27	7504	1342	10,224	32	4004	27,451
2026	275,373	24,505	335,309	102,793	31	10,018	1835	12,292	41	5600	33,895
2027	284,123	22,692	316,992	116,056	35	12,845	2403	14,192	50	7471	40,326
2028	291,416	20,881	298,377	129,799	39	15,997	3049	15,903	60	9639	46,797
2029	297,145	19,084	279,557	144,013	43	19,487	3777	17,405	69	12,120	53,286
2030	301,205	17,309	260,624	158,685	48	23,324	4589	18,679	79	14,925	59,773

.

Based on the projected fuel structure percentages of trucks under the baseline scenario in Table 6 and the forecasted truck model distributions in Table 8, the predicted truck population under the baseline scenario are shown in

Table 10. Baseline scenario: truck quantity forecast results (vehicles).

Year	Diesel Truck			Petrol Truck		Natural Gas Truck				LPG Truck
	Heavy	Medium	Light	Light	Mini	Heavy	Medium	Light	Light	Medium	Light
2023	240,268	29,704	385,606	65,998	20	3392	572	5708	16	1547	14,190
2024	251,925	27,873	367,743	77,755	23	5291	921	8007	24	2690	21,160
2025	262,338	26,019	349,327	90,023	27	7504	1342	10,224	32	4004	27,451
2026	271,385	24,150	330,454	102,793	31	10,018	1835	12,292	41	5600	33,895
2027	278,948	22,279	311,218	116,056	35	12,845	2403	14,192	50	7471	40,326
2028	284,910	20,415	291,716	129,799	39	159,977	3049	15,903	60	9639	46,797
2029	289,160	18,571	272,044	144,013	43	19,487	3777	17,405	69	12,120	53,286
2030	291,489	16,751	252,216	158,685	48	23,324	4589	18,679	79	14,926	59,773

.

Based on the projected truck fuel mix percentages under the optimistic scenario in Table 7 and the forecasted truck model distributions in Table 8, the integrated truck market projections under the optimistic scenario are derived as shown in

Table 11. Optimistic scenario: truck quantity forecast results (vehicles).

Year	Diesel Truck			Petrol Truck		Natural Gas Truck				LPG Truck
	Heavy	Medium	Light	Light	Mini	Heavy	Medium	Light	Light	Medium	Light
2023	234,065	28,937	375,651	59,075	18	4551	767	7657	22	1972	18,093
2024	241,729	26,745	352,859	67,086	20	7275	1266	110,10	33	3292	25,895
2025	247,513	24,548	329,587	75,420	23	10,408	1861	141,801	45	4934	33,825
2026	251,265	22,360	305,954	84,073	25	13,968	2558	171,401	57	6916	41,857
2027	2528,371	20,193	282,086	93,036	28	17,975	3362	198,601	70	9257	49,966
2028	252,085	18,063	258,106	102,304	31	22,445	4278	223,121	83	11,973	58,126
2029	248,869	15,983	234,138	111,868	34	27,395	5309	244,681	97	15,082	66,309
2030	242,907	13,959	210,180	121,347	36	32,654	6425	26,151	111	18,656	74,716

.

This study conducts a sensitivity analysis on carbon emissions under optimistic scenarios and investigates the impact of small changes in the proportion of various types of vehicles on carbon emissions. The changes in total carbon emissions were compared for diesel trucks, gasoline trucks, natural gas trucks, and liquefied petroleum gas trucks with slight changes in their share in different years, as shown in

Table 12. Sensitivity analysis of the share of different energy types of trucks.

Share Change (%)		2023	2024	2025	2026	2027	2028	2029	2030
Diesel	−1	−548,755.47	−557,786.22	−565,862.64	−573,874.32	−581,411.85	−587,932.77	−594,494.09	−599,915.1
	−0.5	−274,377.73	−278,893.11	−282,931.32	−286,937.16	−290,705.93	−293,966.38	−297,247.04	−299,957.6
	0	0	0	0	0	0	0	0	0
	0.5	274,377.73	278,893.11	282,931.32	286,937.16	290,705.93	293,966.38	297,247.04	299,957.55
	1	548,755.47	557,786.22	565,862.64	573,874.32	581,411.85	587,932.77	594,494.09	599,915.1
Gasoline	−1	−69,086.22	−69,935.51	−71,044.06	−72,181.17	−72,916.34	−73,848.78	−74,357.44	−75,164.46
	−0.5	−34,543.11	−34,967.76	−35,522.03	−36,090.58	−36,458.17	−36,924.39	−37,178.72	−37,582.23
	0	0	0	0	0	0	0	0	0
	0.5	34,543.11	34,967.76	35,522.03	36,090.58	36,458.17	36,924.39	37,178.72	37,582.23
	1	69,086.22	69,935.51	71,044.06	72,181.17	72,916.34	73,848.78	74,357.44	75,164.46
Natural gas	−1	−556,309.31	−565,709.55	−572,917.25	−582,386.25	−590,308.34	−596,058.81	−603,080.82	−608,225.2
	−0.5	−278,154.66	−282,854.78	−286,458.62	−291,193.13	−295,154.17	−298,029.41	−301,540.41	−304,112.6
	0	0	0	0	0	0	0	0	0
	0.5	278,154.66	282,854.78	286,458.62	291,193.13	295,154.17	298,029.41	301,540.41	304,112.6
	1	556,309.31	565,709.55	572,917.25	582,386.25	590,308.34	596,058.81	603,080.82	608,225.2
Liquefied petroleum gas	−1	−346,847.45	−352,992.41	−358,872.11	−365,672.51	−368,506.61	−373,530.19	−378,181.55	−379,930.67
	−0.5	−173,423.72	−176,496.21	−179,436.06	−182,836.26	−187,3763.69	−186,765.09	−189,090.77	−189,965.33
	0	0	0	0	0	0	0	0	0
	0.5	173,423.72	176,496.21	179,436.06	182,836.26	184,253.31	186,765.09	189,090.77	189,965.33
	1	346,847.45	352,992.41	358,872.11	365,672.51	368,506.61	373,530.19	378,181.55	379,930.67

.

Through sensitivity analysis of the share of various energy types of trucks, we can see that diesel and natural gas freight vehicles are the main sources of carbon emissions in the freight system of Heilongjiang Province, and changes in their share have caused significant changes in the total carbon emissions. The change in the share of gasoline trucks causes little change in the total carbon emissions, so carbon reduction should focus on adjusting the share of diesel and natural gas types.

3.3. Carbon Emission Estimation Methods

3.3.1. Carbon Emission Calculation Modelling

The formula for calculating the annual carbon emissions generated by a single truck is as follows:

$$Q_{i,j}={ F}_{i,j}·l_i·E_j$$

(9)

where $Q_{i,j}$ is the annual carbon emissions from a single vehicle, t; i is the lorry classification; j is different fuel types; $F_{i,j}$ is the 100 km fuel consumption per vehicle, L·(100 km)⁻¹ or kg·(100 km)⁻¹; $l_i$ is the average annual operating mileage of trucks per vehicle in Heilongjiang Province, 100 km; $E_j$ is the carbon emission factors for trucks of different fuel types, kg·L⁻¹ or kg·kg⁻¹.

Based on the previous calculation of carbon emissions from trucks and the prediction of the scale and structure of trucks, the total amount of carbon emissions from the operation of trucks in Heilongjiang Province is predicted and calculated, and the specific formula is shown:

$$Q=Q_d+Q_g+Q_n+Q_l$$

(10)

where $Q$ is the overall carbon emissions from trucks in Heilongjiang Province, 10⁴ t; $Q_d$ is the carbon emissions from diesel trucks in Heilongjiang Province, 10⁴ t; $Q_g$ is the carbon emissions from petrol trucks in Heilongjiang Province, 10⁴ t; $Q_n$ is the carbon emissions from natural gas trucks in Heilongjiang Province, 10⁴ t; and $Q_l$ is the carbon emissions from LPG trucks in Heilongjiang province, 10⁴ t. The carbon emissions from the operation of different types of trucks are further calculated using the following formula:

$$Q_d=y_{HDT} \times Q_{HDT}+y_{MDT} \times { Q}_{MDT}+y_{LDT} \times Q_{LDT}$$

(11)

$$Q_g=y_{LDT} \times Q_{LDT}+{ y}_{MT} \times Q_{MT}$$

(12)

$$Q_n=y_{HDT} \times Q_{HDT}+y_{MDT} \times Q_{MDT}+y_{LDT} \times Q_{LDT}+y_{MT} \times { Q}_{MT}$$

(13)

$$Q_{l }=y_{MDT} \times Q_{MDT}+y_{LDT} \times Q_{LDT}$$

(14)

where y_HDT, y_MDT, y_LDT, and y_MT indicate heavy, medium, light, and minivan ownership units and Q_HDT, Q_MDT, Q_LDT, and Q_MT indicate the annual carbon emissions of a single vehicle that is heavy, medium, light, and a minivan, respectively, in 10⁴.

3.3.2. Determination of Energy Consumption and Operating Distance Parameters

(1)

Energy consumption per truck

Heilongjiang Province is located in the northeastern part of China, with the characteristics of the climatic environment of the cold region. Because the average annual temperature is only 2.57 °C, the energy consumption during the operation of trucks will be higher than the average energy consumption of the domestic region. This paper derives the single-vehicle energy consumption of different types of trucks under a certain payload, and the results are shown in

Table 13. Average unit energy consumption of trucks in 2020.

Energy Types	Truck Classification	Average Vehicle Energy Consumption
Diesel (L·(100 km)−1)	Heavy	35.3
	Medium	20.0
	Light	12.8
Petrol (L·(100 km)−1)	Light	11.0
	Mini	9.6
Natural gas (kg·(100 km)−1)	Heavy	30.8
	Medium	17.5
	Light	11.2
	Mini	8.4
LPG (kg·(100 km)−1)	Medium	12.0
	Light	8.0

, which is based on the national standards and regulations on the upper limit of energy consumption of light and heavy commercial vehicles, combined with the data in Energy Consumption of Cargo Vehicle Operation published by the State Administration for Market Supervision and Regulation and the National Standardization Administration, and supported by the field market research on energy consumption as well as statistical data.

Based on the collection of GPS data of trucks on network freight transport platforms in the literature [47], combined with the market research on the trucks in Heilongjiang Province, this paper estimates the single vehicle operation mileage, the quantity of operation days, and the annual operation mileage of different vehicle types and structures. In this paper, the single-day operation mileage of heavy trucks is 600 km, and the average annual operation mileage is about 180,000 km; the single-day operation mileage of medium trucks is 400 km, and the average annual operation mileage is about 120,000 km; the single-day operation mileage of light trucks is 300 km, and the average annual operation mileage is about 90,000 km; and the single-day operation mileage of mini-trucks is 150 km, and the average annual operation mileage is about 45,000 km. The average annual operating distance is about 45,000 km, and the average annual operating days are set at 300 days, as shown in

Table 14. Average annual operating miles of different types of trucks.

Truck Classification	Miles Operated per Day (100 km)	Days of Operation per Year (d)	Annual Operating Mileage (100 km)
Heavy	6.0	300	1800
Medium	4.0		1200
Light	3.0		900
Mini	1.5		450

.

3.3.3. Carbon Emissions Calculation and Projection

(1)

Measurement of single-truck energy consumption

According to the technology roadmap published in the national Energy Conservation and New Energy Vehicle Technology Roadmap 2.0, this paper sets the goal of reducing the single-vehicle energy consumption of trucks by more than 10% by 2030 compared with 2020 and the single-vehicle energy consumption level of trucks by about 30% by 2060 compared with 2020, as shown in

Table 15. Projected average unit energy consumption for trucks from 2023 to 2030.

Energy Types	Truck Classification	Average Vehicle Energy Consumption
		2023	2024	2025	2026	2027	2028	2029	2030
Diesel (L·(100 km)−1)	Heavy	34.5	34.1	33.7	33.3	32.9	32.5	32.1	31.7
	Medium	19.6	19.4	19.2	19.0	18.8	18.6	18.4	18.2
	Light	12.5	12.4	12.2	12.1	12.0	11.8	11.7	11.5
Petrol (L·(100 km)−1)	Light	10.8	10.6	10.5	10.4	10.3	10.2	10.0	9.9
	Mini	9.4	9.3	9.2	9.1	8.9	8.8	8.7	8.6
Natural gas (kg·(100 km)−1)	Heavy	30.1	29.8	29.4	29.1	28.8	28.4	28.1	27.7
	Medium	17.1	16.9	16.7	16.6	16.4	16.2	16.0	15.8
	Light	11.1	11.0	10.8	10.7	10.6	10.4	10.3	10.2
	Mini	8.2	8.1	8.0	8.0	7.9	7.8	7.7	7.6
LPG (kg·(100 km)−1)	Medium	11.7	11.6	11.5	11.4	11.2	11.1	11.0	10.8
	Light	7.8	7.7	7.6	7.6	7.5	7.4	7.3	7.2

.

(2)

Measuring the annual carbon emissions of a single truck

By sorting out previous studies and the IPCC Guidelines for National Greenhouse Gas Inventories, the carbon emission coefficients of diesel, petrol, natural gas, and liquefied petroleum gas in the operation of trucks, which are commonly used in China, are 0.72 kg·L⁻¹, 0.63 kg·L⁻¹, 0.80 kg·L⁻¹, and 3.14 kg·L⁻¹, respectively, and then combined with the data in Table 2 and Table 3, the annual carbon emissions of a single vehicle are measured. Combined with the data in Table 14 and Table 15, the annual carbon emissions of a single vehicle were measured, and the results are shown in

Table 16. Annual carbon emissions per vehicle for trucks from 2023 to 2030.

Energy Types	Truck Classification	Annual Carbon Emissions from Vehicles (t)
		2023	2024	2025	2026	2027	2028	2029	2030
Diesel (L·(100 km)−1)	Heavy	44.71	44.19	43.68	43.16	42.64	42.12	41.60	41.08
	Medium	16.93	16.76	16.59	16.42	16.24	16.07	15.90	15.72
	Light	8.10	8.04	7.91	7.84	7.78	7.65	7.58	7.45
Petrol (L·(100 km)−1)	Light	6.12	6.01	5.95	5.90	5.84	5.78	5.67	5.61
	Mini	2.66	2.64	2.61	2.58	2.52	2.49	2.47	2.44
Natural gas (kg·(100 km)−1)	Heavy	43.34	42.91	42.34	41.90	41.47	40.90	40.46	39.89
	Medium	16.42	16.22	16.03	15.94	15.74	15.55	15.36	15.17
	Light	7.99	7.92	7.78	7.70	7.63	7.49	7.42	7.34
	Mini	2.95	2.92	2.88	2.88	2.84	2.81	2.78	2.74
LPG (kg·(100 km)−1)	Medium	33.06	32.78	32.50	32.22	31.65	31.37	31.09	30.52
	Light	11.02	10.88	10.74	10.74	10.60	10.46	10.31	10.17
Total		193.32	191.27	188.99	187.27	184.96	182.68	180.63	178.14

.

From the calculation results, it can be seen that among several types of trucks, diesel heavy-duty trucks have the highest annual carbon emissions, up to 45.23 t, which can be attributed to the following reasons: high payload requires more traction. So, more diesel energy is needed once to do the work, while diesel energy is not flammable and cannot be completely burned in the process of operation, which produces black smoke. What is more, its after-treatment technology is not mature. This is followed by natural gas heavy-duty trucks and LPG medium-duty trucks, whose annual carbon emissions per vehicle are 43.92 and 33.63 t, respectively, indicating that petrol is the more environmentally friendly and environmentally friendly of the energy types compared in the table.

Heavy-duty trucks have the highest annual carbon emissions per vehicle, 45.23 t and 43.92 t, respectively, which are 1.65 and 1.64 times higher than medium-duty trucks of the same energy type and 4.50 and 4.45 times higher than light-duty trucks of the same energy type. It is not difficult to understand that such trucks are heavy and have to consume more energy during operation, resulting in higher carbon emissions per vehicle.

3.3.4. Measurement of Total Annual Carbon Emissions

Based on the model established in the previous section and the calculated data of annual carbon emissions per truck, the trend of changes in carbon emissions in the truck operation chain was further measured.

Combining the analyses and forecasts of quantity and energy structure in Heilongjiang Province under the three different scenarios in the previous section, as well as the trend of changes in energy consumption of trucks per vehicle, the total carbon emissions are predicted in the operation of trucks under different scenarios.

(1)

Pessimistic scenario

It is assumed that the carbon emission factors in the combustion process of diesel, petrol, natural gas, and other energy will remain unchanged in the long term. And the energy consumption of trucks will remain basically unchanged. At the same time, according to the prediction of quantity and energy structure in the pessimistic scenario, the process of promoting and applying new energy trucks will be relatively slow. Specifically, the proportion of new energy in the quantity of trucks in 2030 will be only eight percent. Under this scenario, the development of carbon emissions from different types of trucks in the operation chain is shown in

Table 17. Trend of carbon emission of different vehicle types under pessimistic scenario (10⁴ t).

Year	Diesel Trucks			Petrol Trucks		Natural Gas Trucks				LPG Trucks		Total
	Heavy	Medium	Light	Light	Mini	Heavy	Medium	Light	Light	Medium	Light
2023	1079.73	50.55	313.94	40.39	0.01	14.70	0.94	4.56	0.00	5.11	15.64	1525.57
2024	1122.19	47.09	298.04	46.73	0.01	22.70	1.49	6.34	0.01	8.82	23.02	1576.44
2025	1158.73	43.65	279.41	53.56	0.01	31.77	2.15	7.95	0.01	13.01	29.48	1619.74
2026	1188.51	40.24	262.88	60.65	0.01	41.97	2.92	9.47	0.01	18.04	36.40	1661.11
2027	1211.50	36.85	246.62	67.78	0.01	53.27	3.78	10.83	0.01	23.64	42.75	1697.04
2028	1227.45	33.56	228.26	75.02	0.01	65.43	4.74	11.91	0.02	30.24	48.95	1725.58
2029	1236.13	30.34	211.90	81.66	0.01	78.84	5.80	12.91	0.02	37.68	54.94	1750.23
2030	1237.35	27.21	194.16	89.02	0.01	93.04	6.96	13.71	0.02	45.55	60.79	1767.83

.

The carbon emissions and growth rates of different models under the pessimistic scenario are shown in Figure 7. From the calculation results, the total annual carbon emissions in Heilongjiang Province from 2023 to 2030 will continue to increase at an average rate of 2.29% per year, and the total carbon emissions by 2030 will be 17,678,300 t, which is 1.2 times the total carbon emissions in 2023. From 2023, the annual growth rate decreases, and carbon is expected to peak in the future.

(2)

Baseline scenario

Assuming that the carbon emission factors in the combustion process of diesel, petrol, natural gas, and other energy remain unchanged in the future long term. And the level of the energy consumption of trucks remains basically unchanged. At the same time, according to the prediction of quantity and energy structure in the baseline scenario, the promotion and application of new energy trucks are relatively slow. Specifically, the proportion of new energy in the quantity of trucks will be 10% in 2030, and the changes in carbon emissions of different types of trucks in the operation of this scenario are shown in Table 17. Under this scenario, the changes in carbon emissions of different types of trucks in operation are shown in

Table 18. Trend of carbon emission of different truck types under the baseline scenario (10⁴ t).

Year	Diesel Trucks			Petrol Trucks		Natural Gas Trucks				LPG Trucks		Total
	Heavy	Medium	Light	Light	Mini	Heavy	Medium	Light	Light	Medium	Light
2023	1017.10	53.33	326.29	33.51	0.00	7.53	0.48	2.60	0.00	2.69	9.81	1453.35
2024	1061.74	49.78	310.03	39.66	0.01	14.56	0.93	4.52	0.00	5.07	15.44	1501.74
2025	1100.41	46.24	290.88	46.26	0.01	22.40	1.48	6.23	0.01	8.74	22.73	1545.39
2026	1132.25	42.72	273.87	53.11	0.01	31.44	2.14	7.87	0.01	12.90	29.48	1585.81
2027	1157.18	39.22	257.09	60.03	0.01	41.54	2.89	9.38	0.01	17.73	35.93	1621.01
2028	1174.93	35.80	238.08	67.08	0.01	52.53	3.74	10.63	0.01	23.44	42.18	1648.43
2029	1185.23	32.46	221.12	73.60	0.01	64.72	4.68	11.80	0.02	29.97	48.25	1671.85
2030	1187.87	29.19	202.67	80.79	0.01	77.73	5.73	12.78	0.02	36.99	54.19	1687.97

.

The carbon emissions and growth rates of different models under the baseline scenario are shown in Figure 8. From the calculation results, the total annual carbon emissions in Heilongjiang Province from 2023 to 2030 will continue to increase at an average rate of 1.71% per year, and the total carbon emissions by 2030 will be 16,879,700 t, which is 1.1 times the total carbon emissions in 2023. From 2023, the annual growth rate decreases, and carbon is expected to peak in the future.

(3)

Optimistic scenario

Assuming that the carbon emission factors in the combustion process of diesel, petrol, natural gas, and other energy remain unchanged in the future. And the energy consumption of trucks is basically unchanged. At the same time, according to the prediction of quantity and energy structure in the optimistic scenario, the promotion and application process of new energy trucks are rapidly advancing. Specifically, the quantity of the new energy truck share will be 20% in 2030, and the development of carbon emission of different types of trucks in the operation chain under this scenario is as shown in Table 18. Under this scenario, the development of carbon emissions from different types of trucks in the operational chain is shown in

Table 19. Carbon emission trend of different truck types under optimistic scenario (10⁴ t).

Year	Diesel Trucks			Petrol Trucks		Natural Gas Trucks				LPG Trucks		Total
	Heavy	Medium	Light	Light	Mini	Heavy	Medium	Light	Light	Medium	Light
2023	1068.20	44.83	283.70	40.32	0.01	31.22	2.05	8.72	0.01	10.79	28.17	1518.01
2024	1081.14	40.73	260.70	44.88	0.01	44.07	2.98	11.03	0.01	16.04	36.33	1537.90
2025	1084.46	36.71	239.87	49.60	0.01	58.53	4.08	13.20	0.02	22.28	44.95	1553.71
2026	1078.10	32.79	219.46	54.33	0.01	74.54	5.29	15.15	0.02	29.30	52.96	1561.96
2027	1061.78	29.03	197.45	59.13	0.01	91.80	6.65	16.71	0.02	37.56	60.80	1560.94
2028	1035.29	25.41	177.48	63.43	0.01	110.84	8.16	18.16	0.03	46.89	68.37	1554.05
2029	997.86	21.94	156.58	68.08	0.01	130.26	9.75	19.19	0.03	56.94	75.99	1536.63
2030	1016.20	53.20	327.42	31.76	0.00	9.73	0.62	3.35	0.00	3.21	11.66	1457.16

.

The carbon emissions and growth rates of different models under the optimistic scenario are shown in Figure 9. From the calculation results, the total annual carbon emissions in Heilongjiang Province from 2023 to 2030 will continue to increase at an average rate of −0.25% per year, and the total carbon emissions by 2030 will be 14,571,600 t, which is 0.98 times the total carbon emissions in 2023. From 2023 onwards, the annual growth rate decreases, and the growth rate becomes negative in 2027, reaching the carbon peak target.

A comparison of carbon emissions in Heilongjiang Province under different scenarios, as shown in Figure 10, reveals a growing trend in carbon emissions in the province. In the overall comparison, the carbon emissions from operations under the optimistic scenario will reach their peak earliest and achieve the carbon peak in 2027. However, the carbon peak time under the other scenarios requires further investigation. The carbon emissions under pessimistic and baseline scenarios did not reach the carbon peak target in the predicted year, possibly due to the low proportion of new energy trucks and the excessive proportion of traditional diesel and petrol trucks, resulting in an annual increase in carbon emissions in the entire highway freight transportation system in Heilongjiang Province. In an optimistic scenario, due to the higher proportion of new energy vehicles compared to other scenarios, vehicles with higher carbon emissions in the highway freight system are gradually being phased out by new energy trucks, resulting in a decrease in the carbon emissions of the entire highway freight system in the latter half of the forecast year. According to China’s plan, it is expected that carbon emissions will peak in 2025 and achieve carbon neutrality in 2030. The optimistic scenario is in line with this policy.

4. Decomposition of Factors Influencing Carbon Emissions

4.1. Influence Factor Identification and Selection

In light of the literature review presented in reference [47] and the findings of previous studies, this paper identifies three key influencing factors of carbon emissions and explores the primary avenues for reducing carbon emissions.

According to reference [3], transportation energy intensity, unit turnover energy consumption, transportation intensity, and per capita GDP have an impact on the carbon emissions of trucks. According to the literature, per capita GDP and urbanization rate are the main driving factors for carbon emissions growth. Therefore, based on the literature and the actual situation of the transportation industry in Heilongjiang Province, the influencing factors are supplemented. This article selects the urbanization rate of residents, per capita GDP (yuan), freight volume (10,000 tons), freight turnover (100 million tons of kilometres), energy intensity, and per capita secondary industry output (100 million yuan/person) as the influencing factors of carbon emissions during the operation stage of the highway freight system in Heilongjiang Province and divides them into three aspects, technology, population, and economy:

(1)

Economic development level: In general, as economic development increases, living standards rise, consumption grows, and social wealth flows faster. This, in turn, drives social production, creating greater demand for goods transport, which in turn produces more carbon emissions from freight transport. In this paper, GDP per capita and secondary industry output per capita are used to reflect the economic development level of the Heilongjiang region.

(2)

Population size: It is an inevitable consequence of population growth that the quantity of human activities will increase. The movement of a variety of goods, including daily necessities and industrial supplies, will also become more frequent as a result of this growth. To illustrate, the growth of network shopping, enabled by advances in logistics, has led to an increase in demand for goods transportation. This, in turn, has resulted in a significant rise in the volume of goods transportation. Consequently, this paper has selected the urbanization rate and freight turnover as key indicators to reflect the population size of the Heilongjiang region.

(3)

Technology level: Technological advancement, such as the development of exhaust gas treatment technology for trucks, can effectively reduce the carbon emissions of the goods transport system. This paper uses the energy consumption intensity to characterize the adequacy of energy use in the Heilongjiang region. The energy data primarily encompasses raw coal, petrol, paraffin, diesel, energy oil, and natural gas and are converted to standard coal uniformly.

4.2. STIRPAT Model Construction and Verification

In terms of research method selection, the STIRPAT model is widely used due to its flexibility and adaptability. It can adjust model parameters according to the specific conditions of different regions and provide more accurate analysis results [33]. The STIRPAT, EIA model is a statistical model that evaluates the relationship between three independent variables (population, affluence, and technology) and a dependent variable. The specification of the STIRPAT model is the following:

$$I_i=a_i{P}_i^b{A}_i^c{T}_i^d{\mathit{\epsilon }}_i$$

(15)

where I denotes environmental impacts, with a particular focus on carbon emissions; i denotes an economy and in this paper denotes the goods transport industry; a is the model coefficient; b, c, and d represent the driving indices for population, affluence, and technology level, respectively; P denotes population; A denote affluence; T denotes the level of technology; and ε denotes random error.

In this paper, combining the literature [48] and the availability of data from the study of the freight transport system in Heilongjiang Province, we establish an improved STIRPAT model based on the general form of this STIRPAT model to study the driving relationship between the urbanization rate, per capita GDP, per capita secondary industry output, energy consumption intensity, freight turnover, and carbon emissions.

Therefore, we establish the STIRPAT model of the freight transport system in Heilongjiang Province as follows:

$$C=a{A}^{{\mathsf{\beta }}_1}{B}^{{\mathsf{\beta }}_2}{D}^{{\mathsf{\beta }}_3}{E}^{{\mathsf{\beta }}_4}{F}^{{\mathsf{\beta }}_5}\mathit{\epsilon }$$

(16)

where C is carbon emissions for freight traffic in Heilongjiang Province, in 10⁴ t; a is a constant term; A is GDP per capita, yuan/person; B is per capita secondary sector output, billion yuan/person; A and B represent the level of economic development of Heilongjiang Province, corresponding to the wealth term of the original model; D is the urbanization rate, %, representing social influence factors; E is freight turnover, billion tonne-kilometres, representing the effect of traffic itself in Heilongjiang Province, corresponding to the population size term in the original model; and F is the energy intensity. It is equal to the total energy consumption of freight divided by the total turnover of goods. Its unit is ten thousand tons of standard coal divided by hundreds of millions of tons of kilometres, corresponding to the technical terms of the original model; $\mathsf{\epsilon }$ is the random perturbation term; β₁, β₂, β₃, β₄, and β₅ are the elasticity coefficients, which indicate that, when A, B, D, E, and F change by 1%, this will causes a change in C of β₁%, β₂%, β₃%, β₄%, and β₅%, respectively.

In order to facilitate the use of a regression analysis to determine the parameters, taking logarithms on each side of Equation (17) gives the following:

$$\ln C=\ln a+{\mathsf{\beta }}_1\ln A+{ \mathsf{\beta }}_2\ln B+{\mathsf{\beta }}_3\ln D+{\mathsf{\beta }}_4\ln E+{\mathsf{\beta }}_5\ln F+\ln \mathit{\epsilon }$$

(17)

4.3. Analysis of the Contribution of Influencing Factors

This paper selects data from Heilongjiang Province for the period 2012–2022 for the study. It uses the Formulas (3) and (4) to calculate carbon emissions from trucks in Heilongjiang Province, 2012–2022. The data on the urbanization rate of the resident population, GDP per capita (yuan), freight volume (million tonnes), freight turnover (billion tonne kilometres), energy intensity, and secondary industry output per capita (billion yuan per person) were obtained from the Heilongjiang Provincial Statistical Yearbook (2022). The specific data is presented in

Table 20. Impact indicators of transport carbon emissions in Heilongjiang Province from 2012 to 2022.

Year	Carbon Emission (104 t)	Urbanization Rate of Resident Population (%)	GDP per Capita (Yuan)	Volume of Freight (104 t)	Freight Turnover (Billion Tonne-Kilometres)	Energy Intensity (Tonnes of Standard Coal/Billion Tonne Kilometres)	Per Capita Secondary Sector Output (Yuan per Person)
2012	897.32	56.9	29,352	47,465	929.0	0.994	15,351.10
2013	940.27	58.0	32,068	45,288	972.9	0.900	15,007.82
2014	1009.21	59.2	33,464	47,173	1008.5	0.821	9169.14
2015	1048.13	60.5	32,759	44,200	929.3	0.840	5012.13
2016	1103.88	61.1	34,025	42,897	904.8	0.876	5307.90
2017	1174.78	61.9	35,887	44,127	913.5	1.094	5526.598
2018	1245.33	63.5	38,199	42,943	810.7	0.968	5844.447
2019	1315.59	64.6	41,156	37,624	795.1	1.111	8601.604
2020	1425.06	65.6	42,432	35,521	694.0	1.329	39,716.352
2021	1526.06	65.7	47,199	42,086	815.8	1.235	9784.062
2022	1589.05	66.2	51,096	38,616	846.1	1.311	11,032.701

.

For the purpose of linear analysis, the data collected above is logarithmically processed, and the results are shown in

Table 21. Results of logarithmic processing of transport carbon emission impact indicators in Heilongjiang Province (2012–2022).

Year	Carbon Emission (104 t)	Urbanization Rate of Resident Population (%)	GDP per Capita (Yuan)	Volume of Freight (104 t)	Freight Turnover (Billion Tonne-Kilometres)	Energy Consumption Intensity (Tonnes of Standard Coal/Billion Tonne Kilometres)	Per capita Secondary Sector Output (Yuan per Person)
2012	6.7994	4.0413	10.2871	10.7677	6.8341	−0.0060	9.6389
2013	6.8462	4.0604	10.3756	10.7208	6.8803	−0.1054	9.6163
2014	6.9169	4.0809	10.4182	10.7616	6.9162	−0.1972	9.1236
2015	6.9548	4.1026	10.3969	10.6965	6.8344	−0.1744	8.5196
2016	7.0066	4.1125	10.4349	10.6666	6.8077	−0.1324	8.5770
2017	7.0688	4.1255	10.4881	10.6948	6.8173	0.0898	8.6173
2018	7.1272	4.1510	10.5506	10.6676	6.6979	−0.0325	8.6732
2019	7.1820	4.1682	10.6251	10.5354	6.6785	0.1053	9.0597
2020	7.2620	4.1836	10.6557	10.4779	6.5425	0.2844	10.5895
2021	7.3304	4.1851	10.7635	10.6475	6.7042	0.2111	9.1885
2022	7.3709	4.1927	10.8415	10.5614	6.7406	0.2708	9.3086

.

(1)

Covariance analysis

SPSS (25.0) software was used to analyse the covariance between carbon emissions and influence factors. The results are shown in

Table 22. Impact factor covariance statistics.

Impact Factor	Covariance Statistics
	Tolerances	VIF
Freight volume (million tonnes)	0.029	34.678
Freight turnover (billion tonne-kilometres)	0.054	18.475
Energy intensity (tonnes of standard coal/billion tonne kilometres)	0.117	8.520
Per capita secondary sector output (yuan per person)	0.085	11.727
Urbanization rate of resident population (%)	0.156	6.430
GDP per capita (yuan)	0.277	3.616

.

A tolerance is less than 0.1, and a VIF is greater than 10. These indicate that the urbanization rate of the resident population, per capita gross regional product, and freight turnover are affected by a significant issue of multiple covariance. This paper employs principal component analysis to eliminate the covariance.

(2)

Principal Component Analysis

The application of SPSS software to the population urbanization rate, per capita gross regional product, and freight turnover for principal component analysis is shown in

Table 23. Total variance explained.

Initial Eigenvalue			Extract the Sum of the Squares of the Loads
Total	Percentage of Variance	Cumulative (%)	Total	Percentage of Variance	Cumulative (%)
2.728	90.923	90.923	2.728	90.923	90.923
0.231	7.691	98.614
0.042	1.386	100.000

. The cumulative value of component 1 is 90.923%, indicating that it can explain 90.9% of the information contained in the original variables. Consequently, we extract a principal component, Z1.

The component matrix is shown in

Table 24. Component matrix.

Influencing Factors	Value
Urbanization rate of resident population (%)	0.983
GDP per capita (yuan)	0.954
Freight turnover (billion tonne-kilometres)	−0.923

.

The relationship between component ${ZX}_1$ and the urbanization rate of the resident population, GDP per capita, and freight turnover is illustrated in Equation (18)

$$Z_1={\displaystyle \frac{0.983}{\sqrt{{\mathit{\gamma }}_1}}}{ZX}_1+{\displaystyle \frac{0.954}{\sqrt{{\mathit{\gamma }}_1}}}{ZX}_2+{\displaystyle \frac{0.923}{\sqrt{{\mathit{\gamma }}_1}}}{ZX}_3$$

(18)

where ${\mathit{\gamma }}_1$= 2.2728, ${ZX}_1$ is the urbanization rate of the resident population, ${ZX}_2$ is GDP per capita, and ${ZX}_3$ is freight turnover.

(3)

Least squares regression analysis

The newly introduced variables are employed in lieu of the urbanization rate of the resident population, the gross regional product per capita, and the freight turnover. As shown in Figure 11, a linear relationship exists between the aforementioned variables and the dependent variable, thereby enabling the performance of regression analyses.

The results of the least squares analysis using SPSS software are shown in

Table 25. Summary of models.

R	R2	Revised R2	Errors in Standard Estimation	Durbin–Watson
0.991	0.983	0.969	36.70,532	1.486

. R² is 0.969, which indicates that all independent variables can explain 96.9% of the variation in carbon emissions. The result of Durbin–Watson test is 1.486, which is between 0 and 4, and basically it can be considered that data independence is met.

ANOVA analysis of the model was carried out as shown in

Table 26. ANOVA.

	Quadratic Sum	Degree of Freedom	Mean Square	F	Significance
Regression	384,179.010	4	96,044.752	71.288	0.000
Residual error	6736.404	5	1347.281
Total	390,915.414	9	-

, yielding F = 71.288, with p < 0.001 indicating successful model building.

The derived coefficients are shown in

Table 27. Coefficienta.

Modelling	Unstandardized Coefficient		Standardized Coefficient	t	Covariance Statistics
	B	Standard Error	Beta		Tolerances	VIF
Constant	547.419	323.706	-	1.691	-	-
Volume of freight	0.013	0.008	0.230	1.643	0.176	5.691
Energy intensity	93.097	169.700	0.077	0.549	0.176	5.683
Secondary sector output per capita	−0.001	0.002	−0.056	−0.590	0.383	2.613
Z1	143.377	22.595	1.136	6.346	0.107	9.303

.

The results show that the urbanization rate of the resident population, per capita gross regional product, freight turnover, energy intensity, and freight volume positively affect carbon emissions, while per capita secondary industry output negatively affects carbon emissions. Specifically, the regression equation obtained is as follows:

$$\ln C=547.419+1.508\ln A - 0.001\ln B+1.508\ln D+1.508\ln E+93.197\ln F+\ln \mathit{\epsilon }$$

(19)

where C is overall carbon emissions; A is GDP per capita (yuan/capita); B is per capita secondary sector output (billion yuan/capita); D is the urbanization rate (%), which represents the social impact factor; E is freight turnover (billion tonne-kilometres); F is energy intensity; and $\mathit{\epsilon }$ is the random perturbation term.

4.4. Analysis of Results

In order to gain a more nuanced understanding of the peak path of carbon emissions from the freight transport system in Heilongjiang Province, this paper analyses the design of the program from three key perspectives. These include the level of economic development, the social impact, the impact of the traffic itself, the size of the population, the impact of technology, and others. The objective was to ascertain the size of the emission reduction in each influencing factor, to establish a theoretical basis and data support for the subsequent emission reduction path, and to identify more effective emission reduction measures:

(1)

Degree of economic advancement: The level of economic development (GDP per capita) and population size are factors that contribute to the intensification of carbon emissions from transport. The GDP per capita in Heilongjiang province demonstrated a rapid growth trend from 2017 to 2022, exhibiting an average growth rate of 7.17% per year. A review of the literature reveals that a significant quantity of scholars anticipate that the economic growth rate may continue to increase. The present study posits a decrease of 0.5% per annum in the growth rate of per capita GDP in Heilongjiang Province, with an estimated per capita GDP of RMB 71,228 in 2030. This is in addition to the continued growth of carbon emissions at the average rate, resulting in an estimated 3213.85 tonnes.

The time series data of Jiangsu Province from 1987 to 2018 [12] reveals a long-term equilibrium relationship between the development of the secondary industry and carbon emissions. Furthermore, the development of the secondary industry has a bidirectional Granger causality with carbon emissions, and the optimization of the development mode of the secondary industry contributes to energy conservation and emission reduction. In light of the aforementioned findings, a model has been devised to project the evolution of the secondary industry in Heilongjiang Province. The per capita secondary industry output value in Heilongjiang Province from 2017 to 2022 has been observed to exhibit an average growth rate of 6.93% per year. In this paper, the growth rate of the per capita secondary industry value in Heilongjiang Province, set to a reduction of 0.5% per year, is projected, resulting in a per capita secondary industry output value of approximately 14,468 yuan in Heilongjiang Province by 2030. Concurrently, the carbon emission rate is anticipated to exceed the per capita secondary industry output value, which is expected to continue its average growth trajectory of approximately 2 t.

(2)

Population size, urbanization rate, and freight turnover: A research paper published in The Lancet in July 2020, by the Institute for Health Metrics and Evaluation at the University of Washington in the United States, concludes that the global population is expected to reach a peak of 9.73 billion in 2064, subsequently declining to 8.79 billion in 2100. The majority of domestic studies concur that China’s demographic dividend is already receding and that the country will gradually transition into an era of population aging. The prevailing view among scholars is that China will likely reach its peak population size around 2030. In light of the aforementioned studies, this paper posits a decline in the growth rate of urbanization in Heilongjiang province, with an estimated reduction of 0.5% per year. This would result in an urbanization rate of approximately 60.58% in Heilongjiang province by 2030. It is projected that this would lead to a reduction in carbon emissions of approximately 3392 t, compared to the projected average growth rate of urbanization.

A review of the literature on the demand for freight transport [47] reveals that historical trends and the Ministry of the Environment’s policies have significantly influenced the evolution of this sector. The analysis suggests that the demand for freight transport will continue to grow, particularly in light of the emission reduction policy that encourages the road freight sector to adopt more efficient practices. This is evidenced by the observed decline in freight turnover, which has been estimated to be −1.05% per annum. The average growth rate is reduced by 0.5 percent per year. In this scenario, the freight turnover in Heilongjiang Province in 2030 is 58,878 million tonne-kilometres, at which point carbon emissions are approximately 3485 t less than if freight turnover continued to grow at the average growth rate.

(3)

Technology level (energy intensity): In light of the findings of the pertinent literature and the stipulations set forth by the International Energy Agency’s Zero Carbon Emission Roadmap Report for Energy in 2050, it is imperative that the energy intensity in 2030 is diminished to approximately 15% of its 2020 level. However, due to the considerable coefficient value associated with the energy intensity in the formula, this paper judiciously modifies the growth rate of the energy intensity by establishing the energy intensity at the average growth rate is 3.23%, and an energy intensity rate of change of 0.5% per year is then constructed. In this scenario, by 2030, the freight turnover of 13.18 million tonnes of standard coal/billion tonne-kilometres is projected for Heilongjiang Province. At this time, carbon emissions are expected to exceed the energy consumption intensity at the average growth rate of approximately 205,981 t.

Following the discovery that per capita GDP, per capita secondary industry output, urbanization rate, freight turnover, and F (representing the intensity of energy consumption) effect the change in carbon emissions of the freight transport system in Heilongjiang Province, the rate of change of each driver was calculated. In accordance with the aforementioned rate of change and the average trend relative to the change in carbon emissions, the results of the reduction in carbon emissions for the various drivers are presented in

Table 28. Analysis of results.

Driving Factor	Reduced Carbon Emissions (t)
GDP per capita (yuan/person)	3213.85
Secondary sector output per capita (108 yuan/person)	2
Urbanization rate (%)	3392
Freight turnover (104 t-km)	3485
Energy intensity (104 t of standard coal/108 t-km)	205,981

. The results demonstrate that modifying the intensity of energy consumption can effectively achieve emission reduction and carbon reduction. The most pronounced impact is the potential to reduce 205,981 t CO₂, followed by economic influences, with changes in per capita GDP capable of reducing 3212.85 t CO₂. Based on these findings, a strategy for reducing emissions in the freight transport system in Heilongjiang Province can be proposed.

5. Discussion

Research indicates that the carbon emissions of the freight transportation system in Heilongjiang Province are influenced by a combination of economic, demographic, and technological factors. Among these, regulating energy intensity is the most effective carbon reduction strategy for Heilongjiang’s freight system, with its impact aligning with the International Energy Agency’s Zero Carbon Emission Roadmap Report for Energy. However, this stands in stark contrast to the modest emission reductions achieved through economic restructuring, highlighting the province’s continued reliance on carbon-intensive development models. Notably, the bidirectional Granger causality between the growth of the secondary industry and emissions, which has been demonstrated in Jiangsu Province, exhibits weaker coupling strength in Heilongjiang. This suggests the need for targeted industrial policies tailored to divergent industrialization pathways.

Another key finding relates to freight turnover, where efficiency improvements are associated with higher-than-expected emissions due to the Jevons Paradox—wherein technological advances that enhance efficiency stimulate demand. This phenomenon necessitates layered policy design. While technological breakthroughs, such as in electric heavy-duty vehicles, are critical—as evidenced by the energy intensity results—demand-side measures like distance-based carbon pricing may also be required to counteract rebound effects. Population projections further complicate this calculus. By 2030, Heilongjiang’s urbanization rate is expected to peak at 60.58%, below the national average. Without integrated urban–rural strategies, the province’s logistics network may face unique spatial constraints that could exacerbate per capita emissions.

However, this study is based on historical data and assumptions, overlooking nonlinear influences such as technological breakthroughs or abrupt policy shifts. Factors like cross-provincial freight flows, Sino-Russian freight movements, and climate-induced infrastructure disruptions were not considered, potentially leading to an underestimation of carbon emissions. Future research should incorporate dynamic modelling and spatial interactions to optimize predictions.

6. Conclusions

This study uses the scenario analysis method to predict the total carbon emissions from the operation of trucks in Heilongjiang Province, and through the improved STIRPAT model, it studies the factors influencing the carbon emissions of trucks, so as to provide data support for the later proposal of emission reduction paths suitable for the actual situation in Heilongjiang Province. The following main findings are obtained:

(1)

The scale of carbon emissions from truck operations is closely related to the scale and structure of the quantity of trucks on hand and the development of truck energy-saving technology. The calculations presented in this paper indicate that, by 2030, the quantity of trucks in Heilongjiang Province will reach 933,720. This figure includes 560,456 diesel trucks, 158,732 petrol trucks, 46,672 natural gas trucks, and 74,698 liquefied petroleum gas trucks; additionally, the projected quantity of vehicles includes 4698 LPG trucks, 314,813 heavy-duty trucks, 362,650 medium-duty trucks, 444,505 light-duty trucks, and 59,900 minivans. The findings of the study align with the “S” shape of the growth curve, with the quantity of trucks exhibiting a growing trend. This validates the model and demonstrates the reasonable predictability of the results.

(2)

Based on the findings of this study, a bottom-up carbon emissions measurement model is developed. It is estimated that the truck operation segment in China in 2030 will be approximately 168,797 million tonnes, which represents a significant challenge for achieving the carbon reduction target at the provincial level. Consequently, it is essential to investigate the underlying drivers of carbon emissions. Furthermore, it is predicted that the growth rate of carbon emissions will become negative around 2026 under the optimistic scenario, thereby achieving the goal of carbon peaking.

(3)

The quantitative relationship between each driver and carbon emissions is established using the STIRPAT model. The potential emission reduction in each driving factor is then quantitatively explored by setting the growth rate of each driving factor separately. Furthermore, energy intensity is an effective way to reduce carbon emissions, with a unit emission reduction effect 60 times that of other emission reduction methods. It is therefore evident that, in order to achieve carbon emission reduction in the operation of the Heilongjiang road freight transportation system, it is not only necessary to develop the economy, but also to accelerate the penetration of new energy technologies, which will become a key means of accelerating the achievement of the carbon peaking goal in the truck operation.