Dual effects of digital technology on high quality development of road transportation in Heilongjiang Province China

Abstract

The profound advancement of digital transformation has fundamentally altered various sectors within urban environments, with road transportation being no exception. Digital technology is regarded as a key driver for achieving high quality development of road transportation (HQDRT) and building a leading transportation nation.However, existing research has not comprehensively elucidated the mechanisms through which the level of urban digital technology (UDTL) influences HQDRT, nor has it thoroughly explored the accompanying synergistic effects, especially within the context of the current revitalization strategy for Northeast China. This study employs panel data from 12 prefecture-level cities in Heilongjiang Province spanning from 2011 to 2022, utilizing econometric methods and a coupling coordination model to rigorously analyze the dual effects of UDTL on HQDRT. Additionally, kernel density estimation and geographic detectors are applied to investigate the temporal and spatial heterogeneity of the synergistic evolution between these two factors and their underlying driving forces. The results indicate that UDTL possesses a positive influence on HQDRT across various prefecture-level cities in Heilongjiang, and a significant synergistic relationship is observed between these variables. Further analysis reveals pronounced regional disparities in their collaborative development, exhibiting a typical core-periphery structure. Core cities demonstrate substantial regional spillover effects through technological accumulation and institutional innovation, whereas peripheral cities confront challenges associated with delayed synergistic evolution. Furthermore, the prefecture-level cities in Heilongjiang are gradually transitioning towards a multi-layered and multidimensional comprehensive development stage, as traditional single-factor driven models are increasingly inadequate to meet the complex collaborative development needs of UDTL and HQDRT. This research extends the boundaries of existing studies focused on the digital transformation of road transportation and provides valuable insights for promoting green and sustainable development within this sector.

Introduction

With the deepening development of the Fourth Industrial Revolution (Industry 4.0), the digital economy has emerged as an important engine for economic development across nations¹gradually becoming a key driving force for sustainable economic and social development². Digital technologies, supported by big data, cloud computing, 5G, the Internet of Things (IoT), and artificial intelligence (AI), have prompted profound changes in various countries and industries worldwide^3,4. For instance, the European Commission has advocated for the adoption of digital technologies to achieve a twin transition towards digitization and greening, ultimately facilitating sustainable development for enterprises and cities⁵. The application of digital technologies has also been recognized as a crucial pathway for promoting sustainable agriculture development in the United States⁶. In this context, the significance of digital technologies in the advancement of the transportation sector has increasingly come to the forefront^7,8,9 with road transportation being no exception. As an essential component of transportation, the high-quality development of road transport has attracted extensive attention¹⁰. China considers the high-quality development of road transport as a strategic support for accelerating economic and social progress. Following the “Outline for Building a Powerful Transportation Nation” and the “National Comprehensive Three-Dimensional Transportation Network Planning Outline,” China has introduced its first five-year development plan for road transportation, the “14th Five-Year Plan for Road Development,” which emphasizes the promotion of a path focused on high-quality road transport development driven by digital empowerment and innovation¹¹.

Specifically, the high-quality development of road transportation is guided by innovation and green low-carbon principles, aligned with national strategies, and is achieved through the collaborative optimization of road network structures, enhancement of service quality, and intensive resource utilization. Digital technologies play a crucial role in this process. On one hand, road transportation is the largest contributor to resource consumption and environmental pollution among various modes of transport¹²making its green transition directly related to the achievement of the “dual carbon” goals and high-quality development. As a disruptive innovation, digital technology is profoundly altering the organizational forms and value creation models of traditional industries. In the case of high-energy-consuming and high-pollution sectors, digital transformation has become a pivotal pathway to overcoming developmental bottlenecks and achieving sustainable and high-quality growth¹³and the road transportation sector is no exception². Digital technologies possess the potential to fundamentally restructure patterns of personnel and goods movement, thereby generating systematic impacts on transportation demand, energy consumption, and environmental effects¹⁴. For instance, Intelligent Transportation Systems (ITS) are regarded as effective measures to achieve energy-saving and emissions reduction targets in the transportation sector¹⁵. Data-driven digital platforms not only facilitate real-time optimization of transportation resource allocation but also provide effective avenues for reducing the overall carbon footprint¹⁶. On the other hand, digital technologies, with their significant technological spillover effects, are reshaping the inherent logic and development models of the road transportation system. The introduction of digital twin technology has enabled more precise and efficient resource allocation within transportation systems². By utilizing intelligent Internet of Things (IoT) sensors and machine learning, the digitalization of highways can be advanced, thereby improving road safety⁹. Consequently, as cutting-edge technologies such as big data, artificial intelligence, and the IoT are deeply integrated, the operational efficiency, safety levels, and service quality of road transportation have significantly improved. This integration can fundamentally reshape the allocation of elements, governance models, and innovation ecosystems of transportation systems⁷thereby promoting the high-quality development of road transportation. The digital transformation of the transportation sector is also considered a key pathway for India to achieve sustainable development¹⁶. In Europe, broader digitalization is recognized as an important factor in curbing the increase in energy consumption within the transportation sector¹⁷.

However, despite the significant role of digital technologies in achieving high-quality development in road transportation, previous research has exhibited certain limitations. On one hand, in terms of research scope, prior studies have primarily focused on the transportation sector as a whole, with little attention given to specific contexts within road transportation, particularly in the context of digital transformation. For example, Wang et al.¹⁸ examined urban transportation land optimization issues in the Yangtze River Economic Belt. Liu et al.¹⁹ empirically analyzed the regional differences in carbon emission intensity within the transportation sector across 30 provinces in China. Zhang et al.¹⁰ utilized a composite system synergy model to analyze the evolution of the integrated transportation network in the Yellow River Basin. On the other hand, regarding research content, while previous studies have addressed aspects such as carbon emission accounting in green low-carbon transitions²⁰influencing factors²¹and regional characteristics of disparities¹⁹there remains a lack of in-depth research focused on the impacts of digital technologies on road transportation. Although some literature has explored the influence of specific digital technology applications on road transportation, such as Liu et al.²¹who developed a digital highway system based on intelligent technology to alleviate traffic congestion, these studies are often confined to analyses of single technological dimensions^9,21. There is a notable absence of a more macro-level perspective from a cross-regional standpoint on road transportation, particularly concerning the systematic and in-depth examination of high-quality development in this sector.

Additionally, the northeastern old industrial base region faces systemic issues such as difficulties in urban transformation, insufficient innovation momentum, inefficient resource allocation, and relatively low penetration rates of digital technologies^22,23. These challenges may hinder the effective adaptation and integration of cross-regional road transportation systems with digital technologies, necessitating further in-depth research on the relationship between UDTL and HQDRT, especially considering the varying circumstances of different cities. In the context of revitalization in Northeast China, it is crucial to reveal the dynamic interaction mechanisms, synergistic effects, and feedback loop changes between digital technologies and transportation system integration. This understanding is essential not only for the sustainable development of the region but also provides valuable insights for other old industrial bases globally facing similar transformation challenges. Therefore, an in-depth analysis of practices in Northeast China will help identify universal strategies and models applicable to other transforming regions, thereby promoting regional economic recovery and sustainable development on a global scale. However, this interaction mechanism has not yet been thoroughly elucidated.

Based on this, this study adopts the principles of system synergy theory and utilizes panel data from 12 prefecture-level cities in Heilongjiang Province from 2011 to 2022. First, a econometric model will be employed to analyze the direct effects of UDTL on HQDRT. Next, the coupling coordination model will be used to reveal the synergistic effects between the two. Furthermore, spatial analysis methods, including kernel density estimation and geographic detectors, will be applied to explore their spatial-temporal differences and driving factors. The theoretical contributions of this research primarily lie in revealing the synergistic effects and the spatial-temporal dynamic evolution characteristics between UDTL and HQDRT through the exploration of their interrelationship. Through empirical analysis in Northeast China, the critical role of urban digitalization levels in optimizing urban road transportation systems, enhancing operational efficiency, and facilitating economic transformation will be elucidated. Additionally, the findings of this study provide important lessons for other emerging economies, such as Vietnam and Indonesia, during their development processes. Ultimately, this research enriches the theoretical discussion on the relationship between digital technologies and urban road transportation development and fosters a deeper understanding of urban road transportation development during the digital transformation process across different economic contexts, contributing to urban sustainability.

The structure of the remainder of this paper is organized as follows: Sect. 2 provides the theoretical analysis and analytical framework for this study. Section 3 describes the research methods and data sources employed in this study. Section 4 presents an analysis of the research results obtained from this study, while sect. 5 offers a further discussion of these findings. Lastly, sect. 6 summarizes the conclusions drawn from this study, the limitations of the research, along with suggestions for future research.

Theoretical analysis and model development

The dual effects mechanism of UDTL on HQDRT

Digital transformation has emerged as a predominant characteristic of contemporary urban development, profoundly impacting changes across various industries²⁴. As regional carriers encompassing multiple sectors, cities are also inevitably influenced by this trend. The potential impact of digital transformation on urban development cannot be overlooked. For instance, Yang et al.¹³ demonstrated through the application of panel regression models and mediation effect analysis that the construction of digital cities significantly reduces carbon intensity and intervenes in carbon emissions through three pathways: technological innovation, industrial structure adjustment, and energy structure optimization. Furthermore, the research conducted by Ma et al.²⁵ further validates that the level of urban digital development effectively enhances energy-saving and emission-reduction performance. This is particularly evident in traditional resource-based cities, where advancements in digitalization significantly promote the enhancement of green innovation capabilities^26,27. Concurrently, digitalization has also propelled cities towards greater intelligence, with the rise of smart cities further stimulating development across various sectors²⁸. For example, smart cities are better equipped to adapt to and drive the significant roles of digital twins (DT) in industries such as manufacturing, construction, and maintenance²⁹. Road transportation is no exception; as a foundational infrastructure for the normal functioning of urban services, the integration of UDTL and HQDRT demonstrates a dual effect mechanism. On one hand, advancements in digital technologies have significantly enhanced data collection and processing capabilities, thereby promoting the intelligent development of road traffic management³⁰. Specifically, the integration of the Internet of Things (IoT) and big data analytics enables real-time monitoring and analysis of critical data such as traffic flow, vehicle speed, and road conditions. This increase in information transparency not only strengthens the effectiveness of dynamic traffic management implementation but also allows cities to timely optimize traffic signal control and flow allocation, thereby effectively alleviating traffic congestion and reducing the incidence of accidents^15,31.

On the other hand, HQDRT also provides a practical foundation for the enhancement of UDTL. With the intelligent management of traffic systems, cities have increasingly demanded digital technologies, prompting greater investment and application of these technologies within the transportation sector and thus improving the overall level of digital transformation. The establishment of Intelligent Transportation Systems (ITS) has significantly enhanced the information exchange and collaboration models among traffic participants¹¹. For example, navigation systems based on real-time data can provide drivers with optimal route suggestions, directly reducing travel time and carbon emissions³². Additionally, the application of drone technology in road safety, traffic monitoring, and highway infrastructure management has gradually demonstrated its importance, offering new tools to enhance the intelligence level of traffic management². Finally, the integration of intelligent technologies in road transportation and digital techniques can elevate the overall smart management capabilities of cities, creating a positive feedback effect. HQDRT not only promotes the digitalization of other urban functions, such as logistics, public safety, and environmental monitoring, but the proliferation of digital technologies in cities continuously optimizes the structure and operational efficiency of the transportation system. Therefore, UDTL can directly promote HQDRT by optimizing traffic management, facilitating information sharing, and introducing new technologies. Concurrently, HQDRT also provides the driving force for improving UDTL.

Measurement of UDTL and HQDRT levels

Based on the preceding analysis, this study constructs a comprehensive research framework from the perspective of the “dual effect” to investigate the impact of UDTL on the HQDRT (Fig. 1). This framework comprises four core components, each examining the direct effect, synergistic effect, spatial-temporal heterogeneity, and underlying mechanisms of UDTL on HQDRT. First, an econometric model (Method 1) is employed to quantify the direct impact of UDTL on HQDRT. This model effectively addresses potential omitted variable bias by controlling for individual and time fixed effects, thereby ensuring the robustness of the estimation results. Second, a coupling coordination model (Method 2) is utilized to explore the synergistic effects between UDTL and HQDRT, revealing their coordinated development status across spatial and temporal dimensions, thereby deepening the understanding of their dynamic relationship.

Additionally, to unveil the spatial-temporal heterogeneity of UDTL and HQDRT, kernel density estimation (Method 3) is applied to non-parametrically illustrate the spatial distribution characteristics of both elements across different cities in Heilongjiang Province, thereby highlighting the spatial imbalance in high-quality development of urban road transportation. Finally, the geographic detector method (Method 4) is leveraged to identify the driving factors that influence the synergistic development of UDTL and HQDRT, quantifying the impact levels of various factors to explain spatial differentiation phenomena. Through the integration of multiple research methods, this study aims not only to clarify the direct and synergistic effects of UDTL on HQDRT but also to explore the spatial-temporal dynamics and driving factors between the two, thereby providing comprehensive empirical evidence to promote high-quality development of urban road transportation.

Fig. 1

Research framework.

Research methods and data

Measurement of UDTL and HQDRT levels

Index system construction

To accurately measure the relationship between UDTL and HQRT, this study first establishes a scientific and reasonable indicator system. Given the broad dimensions involved in both UDTL and HQRT, a multidimensional comprehensive indicator evaluation system has been constructed based on a systematic review and assessment of existing research methodologies (Appendix Table).

In terms of UDTL, this study encompasses four main dimensions: the level of digital economic development³³the status of digital infrastructure construction³⁴digital technology innovation capability³⁵and the government’s focus on digital economic policies³⁶. (1)Level of Digital Economic Development: This dimension employs the digital financial inclusion index and per capita telecom business volume as measurement indicators. These two indicators have been widely validated and effectively reflect the current state of the regional digital economy³⁷. The digital financial inclusion index measures the accessibility and convenience of financial services, while per capita telecom business volume reflects the actual consumption of digital services by residents. (2)Status of Digital Infrastructure Construction: This dimension includes indicators such as the number of mobile phone users at year-end, the proportion of employment in information transmission, computer services, and software industries, as well as the number of mobile phone users per hundred people³⁸. A complete digital infrastructure is a crucial foundation for driving the digital economy and green transformation. Its construction level directly affects the diffusion and application efficiency of digital technologies. The number of mobile phone users and the number of mobile phone users per hundred people can directly reflect the penetration level of digital technologies among residents³⁴. (3)Digital Technology Innovation Capability: Patent applications serve as a more direct representation of innovation outcomes, clearly reflecting the actual output of technological innovation. Compared to other innovation indicators (such as R&D investment), patent data offers advantages in terms of timeliness and comparability. Furthermore, among patent types, invention patents are more representative due to their originality and technological depth compared to utility model patents^39,40. Thus, this study employs patent data as a key evaluation indicator for digital technology innovation capability. (4)Government Focus on Digital Policies: The emphasis on digital policies is assessed by examining the focus on policies related to the digital economy. Policy focus reflects the government’s support for digital transformation, impacting resource allocation and market environment, and serves as an important guarantee for advancing digital transformation³⁶.

In terms of HQDRT, this study selects indicators for evaluation from four dimensions: basic scale, service quality, industry talent, and innovative potential, based on previous research^10,41. (1)Basic Scale Dimension: The indicators selected include highway mileage and year-end actual road area⁴². Highway mileage reflects the breadth and accessibility of the transportation network and is typically associated with the level of completeness of transportation infrastructure. The year-end actual road area demonstrates land resource utilization, indicating the spatial carrying capacity of infrastructure and providing essential support for the sustainable development of the transportation system. (2)Service Quality Dimension: The focus is on highway passenger traffic and highway freight volume. Highway passenger traffic, as a key indicator of public transportation services, reflects the convenience of residents’ travel and their reliance on public transport, while highway freight volume reveals the efficiency of regional logistics and commercial activities. Together, these two indicators present the ability of the transportation system to meet travel and economic demands⁴³. (3)Industry Talent Dimension: This dimension is assessed based on the proportion of employees in the transportation and warehousing sector and the average wage of employees in the transportation, warehousing, and postal sectors⁴⁴. The proportion of employees reflects the industry’s contribution to the economy, while the average wage indicates the industry’s attractiveness and development potential. These indicators are helpful in understanding the state of human resources in the transportation industry and its development prospects. (4)Innovative Potential Dimension: Indicators include the number of green invention patents filed during the year, the number of green utility model patents, and the number of students enrolled in universities, which can provide essential talent support for innovation and development in the transportation industry^45,46.

Entropy method

To objectively determine the weights of the relevant indicators for UDTL and HQDRT, the entropy method was employed in this study. This method has been widely used for determining the weights of various indicators, particularly in contexts involving the assessment of complex systems^47,48. The entropy method effectively overcomes the randomness and information redundancy issues associated with traditional subjective weighting methods by measuring the amount of information contained in each indicator and the degree of variation⁴⁹. A smaller entropy value indicates a greater variation among the indicators, providing more information; therefore, such indicators should be assigned higher weights⁵⁰. Consequently, the entropy method presents advantages over other methods for determining indicator weights, such as principal component analysis and multiple correlation coefficients. Considering the positive and negative directions of the evaluation indicators, the range method was utilized for the standardization of the data, and the specific calculation process of the entropy method is shown in the following formula.

(1)Data Standardization Process. To eliminate the effects of different dimensions among the indicators, the range method was applied to standardize the raw data. The positive and negative indicators are:

Positive index

$${Z_{ij}} = \frac{{{X_{ij}} - \min ({X_{ij}})}}{{\max ({X_{ij}}) - \min ({X_{ij}})}}$$

(1)

Negative index

$${Z_{ij}} = \frac{{\max ({X_{ij}}) - {X_{ij}}}}{{\max ({X_{ij}}) - \min ({X_{ij}})}}$$

(2)

In the equation, X_ij​represents the standardized value of the j-th indicator for city i, while x_ij refers to the raw value of the j-th indicator for city i. (2)Calculation of Information Entropy Values. First, calculate the weight of the characteristic for the i-th evaluation object under the j-th indicator p_ij:

$${p_{ij}}={Z_{ij}}/\sum\nolimits_{{j=1}}^{n} {{Z_{ij}}}$$

(3)

(3)Calculate the information entropy E_j​for the j-th indicator:

$${E_{ij}}= - k\sum\limits_{{j=1}}^{n} {{p_{ij}} \times \ln {p_{ij}} }$$

(4)

(4)Determine the weight W_j of the indicator and calculate the weight for the j-th indicator:

$${W_j}={{1 - {E_j}} \mathord{\left/ {\vphantom {{1 - {E_j}} {\sum\nolimits_{{j=1}}^{n} {(1 - {E_j})} }}} \right. \kern-0pt} {\sum\nolimits_{{j=1}}^{n} {(1 - {E_j})} }}$$

(5)

Econometric model

This study employs a two-way fixed effects model to analyze the direct effects of UDTL on the HQDRT, thereby revealing whether a potential synergistic mechanism exists between the two. Although dynamic panel data models and spatial econometric models are commonly used in panel data research, the two-way fixed effects model may be more suitable given the specific context of this study. The reason for this is that this study has a relatively limited sample size, and the research area consists of 12 prefecture-level cities within the same province. The geographic scope is small, and the level of regional economic development is relatively homogeneous. Consequently, the dynamic panel data model may yield biased or inconsistent results with small samples, and the weak spatial correlation complicates the global spatial correlation analysis. The two-way fixed effects model effectively controls for individual and time fixed effects, reduces endogeneity issues caused by omitted variables, and enhances the reliability of the regression analysis⁵¹ thereby accurately identifying the direct impact of UDTL on HQDRT. The formula is shown as (6).

$$HQDR{T_{it}}={\beta _0}+{\beta _1}UDT{L_{it}}+{\beta _2}Contro{l_{it}}+{\delta _i}+{\lambda _t}+{\varepsilon _{it}}$$

(6)

In the formula, i represents provinces, and t represents years. HQDRT_it is the dependent variable, indicating the level of high-quality development of highway transportation infrastructure, while UDTL_it is the independent variable, representing the level of digital technology in various prefecture-level cities in Heilongjiang Province. Control variables include the level of economic development (LED), the degree of government intervention (DGI), population density (PD), the degree of openness to the outside world (DOFT), and industrial structure (IS). Additionally, δ_i and λ_t represent time-fixed effects and individual-fixed effects, respectively, while ε denotes the random disturbance term. The descriptive statistics of each variable are presented in Table 1.

Table 1 Descriptive statistics.

Coupling coordination degree model

After employing econometric analysis to assess the direct effects of UDTL on HQDRT, this study further incorporates the Coupling Coordination Degree Model to quantitatively evaluate the “synergistic effects” between the two. The Coupling Coordination Degree Model is an important method for assessing the synergistic development of system elements, with its core focus on quantifying the interactive relationships between subsystems to evaluate the functional integration and optimization levels of the overall system⁵². This model has been widely applied in studies investigating the interactions between urban or regional transportation and other systems^53,54. Compared to traditional econometric regression models, the Coupling Coordination Degree Model emphasizes the overall synergistic state between systems rather than focusing solely on the marginal effects between variables. By introducing two key indicators, coupling degree and coordination degree^52,55 it overcomes the limitations of traditional single-indicator evaluation methods, allowing the research to comprehensively reflect the complex dynamic relationships between systems and providing an effective theoretical tool for regional coordinated development. Specifically, the econometric regression model has confirmed the significant promoting effect of UDTL on HQDRT, while the Coupling Coordination Degree Model further evaluates their level of coordinated development from the perspective of system interactions. By combining these two methods, this study not only reveals the direct relationship between UDTL and HQDRT but also comprehensively expresses the potential and current status of their joint development. The classification standards and categories of the Coupling Coordination Degree are detailed in Table 2. The relevant formulas are presented in (7), (8), and (9)

$$C = 2\sqrt[{}]{{{U_1}{U_2}}}/({U_1} + {U_2})$$

(7)

$$T = \alpha \times {U_1} + \beta \times {U_2}$$

(8)

$$D = \sqrt[{}]{{C \times T}}$$

(9)

In the formula, C represents the coupling degree of the ternary system, and T denotes the comprehensive evaluation value of the ternary system. U_i is the index of the i-th subsystem, while α and β represent the weighting coefficients of the i-th subsystem, where α + β = 1. In the absence of clear data supporting which subsystem—UDTL or HQDRT—is more important, this study follows the conventional approach of previous research^47,55 by treating both systems as equally important, with α = β = 0.5. Finally, D represents the coupling coordination degree of UDTL and HQDRT.

Table 2 Coupling coordination degree division standards and classification.

Kernel density estimation

Kernel Density Estimation (KDE) is a non-parametric statistical method that reconstructs the probability density function of random variables through smoothing estimation techniques, providing an effective tool for analyzing the spatial and temporal differences in the complex collaborative evolution of different systems⁵⁶. In this study, the KDE is applied to panel data from 12 prefecture-level cities in Heilongjiang Province from 2011 to 2022, which overcomes the theoretical limitations of traditional parametric methods and directly reveals the intrinsic distribution characteristics of the system from the data itself. Compared to other econometric methods, KDE does not rely on predefined distributional assumptions and can more accurately reconstruct the probability density distribution of the coupling coordination degree. This flexibility allows for a systematic characterization of the collaborative development trends and differentiated features between UDTL and HQDRT. Furthermore, by integrating KDE with quantitative analysis through econometric regression models, this study can comprehensively reveal the interrelationship between UDTL and HQDRT. Specifically, the econometric regression model provides indicators of the direct impact of UDTL on HQDRT, while KDE systematically characterizes the spatial and temporal convergence trends and differential features of the collaborative development between UDTL and HQDRT at a finer granularity.

$$F(x)=\frac{1}{{ng}}\sum\limits_{{i=1}}^{n} {K\left[ {({X_i} - x)/g} \right]}$$

(10)

In the formula (10), n represents the number of provinces, g denotes the bandwidth, K is the kernel function, X_i refers to the independently and uniformly distributed sample data representing the coupling coordination degree between UDTL and HQDRT in various prefecture-level cities in Heilongjiang Province, and x represents the mean value of the coupling coordination degree.

Geographical detector

The Geographical Detector, as a spatial analysis method used to explore the spatial variability of geographical factors and the strength of their interrelationships⁵⁷ can reveal the driving mechanisms of spatial differentiation and quantify the explanatory power of influencing factors on regional disparities. The Geographical Detector consists of two core modules: the factor detector and the interaction detector. The factor detector computes the explanatory power of various driving factors on spatiotemporal differences in co-evolution, while the interaction detector reveals nonlinear coupling effects between factors, overcoming the limitations of traditional linear regression methods in analyzing complex system mechanisms⁵⁸. Considering that the co-evolution of UDTL and HQDRT exhibits complex spatiotemporal self-organizing relationships, traditional regression analysis struggles to fully capture the dynamic interaction of driving factors. Therefore, the Geographical Detector is an ideal method for analyzing such complex systems. Based on the five control variables from the original econometric model, this study further introduces five key variables that may influence the spatiotemporal differences in the co-evolution of UDTL and HQDRT: the level of urbanization, scientific and technological support, the degree of market openness, fixed asset investment intensity, and environmental regulation. Through a systematic analysis of multidimensional factors, this study comprehensively reveals the intrinsic driving mechanisms of regional collaborative development. Furthermore, it attempts to precisely quantify the explanatory power of each factor in the co-evolution process and examines the complex interactions among these factors. The Geographical Detector is defined as shown in the formula (11).

$$q=1 - \frac{1}{{N{\sigma ^2}}}\sum\limits_{{h=1}}^{L} {{N_h}\sigma _{h}^{2}}$$

(11)

In the formula, q represents the contribution of spatial variation in factor X to the co-evolution of UDTL and HQDRT, ranging from 0 to 1. A higher q value indicates stronger explanatory power. When q equals 1, the influencing factor can fully explain the spatial distribution of UDTL and HQDRT co-evolution. Conversely, when q equals 0, it indicates no relationship between the influencing factor and the co-evolution of UDTL and HQDRT. h and N represent the number of units in layer h and the entire region, respectively, where h = (1, 2, …, L) indicates the classification of coupling coordination or factor X. Additionally, σh² and σ² denote the variances of coupling coordination within layer h and the entire region, respectively.

Data source

This study covers panel data from 12 prefecture-level cities in Heilongjiang Province for the period 2011–2022. The Digital Inclusive Finance Index, used to measure the digital economy, was obtained from the Digital Finance Research Center at Peking University. The attention to digital economy policies was measured by analyzing the frequency of digital economy-related terms in municipal government work reports from various cities in Heilongjiang Province, reflecting the level of government support for the digital economy. Data on digital economy-related patents and green technology patents were primarily sourced from the China Research Data Services Platform (CNRDS). Other indicators used to measure the level of digital technology and the high-quality development of highway transportation infrastructure were obtained from the National Bureau of Statistics of China, the China City Statistical Yearbook (2011–2023), and the EPS database. Additionally, the geographical data for this study were sourced from the Resource and Environment Science Data Platform of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. To ensure data completeness and scientific rigor, linear interpolation was used to estimate missing values for certain indicators.

Results

UDTL and HQDRT measurement results

This study utilizes the entropy method to measure UDTL and HQDRT in various cities of Heilongjiang Province from 2011 to 2022. The results indicate a robust overall growth in UDTL (increasing from 0.075 to 0.286) and HQDRT (rising from 0.091 to 0.141), although significant regional disparities exist. In Harbin, the application of digital technology on high-speed highways such as the Beijing-Harbin and Suiman routes resulted in a spike in the UDTL index from 0.200 to 0.793, demonstrating significant advancements in digital infrastructure construction and technological innovation. This has facilitated the digital transformation of the local economy, strongly supporting the high-quality development of road transportation infrastructure. In contrast, Qiqihar experienced a slower growth in UDTL (rising from 0.058 to 0.354). Although this growth reflects the diffusion of digital technology, the lagging absolute level limits its digital transformation and the improvement of infrastructure quality. This regional disparity exacerbates the “Matthew effect” through a technology-infrastructure feedback mechanism, where core cities continue to benefit while peripheral cities fall into path dependence, posing challenges for coordinated development. Regarding HQDRT, Harbin increased from 0.425 to 0.624, highlighting the role of digital technology in enhancing service quality and operational efficiency, particularly in traffic flow management and scheduling. Conversely, Qiqihar’s HQDRT only rose from 0.134 to 0.194, constrained by the lag in digital technological foundations and infrastructure development, which hampers sustainable economic growth.

Furthermore, the spatial distribution trends of UDTL and HQDRT for the years 2011, 2015, 2018, and 2022 were visualized using ArcMap 10.8.1 (in fig. 2, UDTL is represented in orange, with deeper colors indicating higher levels of UDTL; HQDRT is represented by green circles, with the size of the circles reflecting the levels of HQDRT). Figure 2 shows that Harbin experienced rapid increases in both indicators during the study period, while cities such as Qiqihar, Jixi, and Hegang lagged behind with fluctuating values. With the deepening of digital technology in transportation networks such as the Suiman Expressway, HQDRT growth rates in the densely populated southern regions surpassed those in the northern marginal areas; however, the regional disparity has not significantly narrowed, indicating spatial imbalances in the application of digital technology. Therefore, there is an urgent need for econometric models to quantitatively analyze the mechanisms by which UDTL affects HQDRT, revealing its pathways of influence and regional heterogeneity characteristics.

Fig. 2

Visualization maps of UDTL and HQDRT measurement results for 2011, 2015, 2018, and 2022. Note: The data are sourced from the Resources and Environmental Science Data Platform (https://www.resdc.cn), specifically the “ Xinliang Xun. Multi-year Administrative Boundary Data of Prefecture-level Cities in China” (2022) (DOI:https://doi.org/10.12078/2023010102). The present map is based on these data, with only the administrative boundaries of prefecture-level cities in Heilongjiang Province retained.

Baseline regression results

Before conducting regression analysis, this study first performs a correlation analysis among the variables, which can reveal the linear relationships and potential interactions between them, ensuring the reliability and validity of the subsequent regression analysis. Figure 3 demonstrates a significant positive correlation between UDTL and HQDRT, indicating that improvements in digital technology can effectively promote the quality enhancement of transportation infrastructure. Additionally, there are relatively significant correlations between HQDRT and variables such as LED, DGI, and PD, while DOFT shows a negative correlation with no significant impact on HQDRT. These findings indicate that digital technology, along with economic and policy factors, plays a critical role in infrastructure development. Therefore, further regression analysis is needed to explore the specific impact mechanisms of UDTL on HQDRT and its performance under the influence of other factors. Furthermore, the VIF for all variables in this study are below 5, indicating that there are no serious multicollinearity issues among the independent variables, which further enhances the reliability of the model analysis.

Fig. 3

Correlation analysis among variables.

Table 3 presents the results of the regression analysis on the impact of UDTL on HQDRT using a two-way fixed effects model. All regression models control for fixed effects of year and province to ensure the robustness and timeliness of the results. From models (1) to (6), the coefficients of UDTL remain significantly positive, ranging from 0.392 to 0.405, and are significant at the 1% statistical level (p < 0.01), indicating that improvements in digital technology levels have a persistent and significant positive impact on HQDRT. Specifically, for each one-unit increase in UDTL, HQDRT improves by approximately 0.4 units on average, highlighting the critical role of digital technology in promoting the high-quality development of road transportation infrastructure. This influence mechanism may stem from the application of digital technology in optimizing traffic flow management, enhancing transportation efficiency, and strengthening safety monitoring, thereby directly improving the quality of road transportation.

Furthermore, R² for the models range from 0.978 to 0.979, indicating a high level of explanatory power for the models concerning HQDRT. However, in contrast to the significant impact of UDTL, other control variables showed no significant effects across all models. For instance, the coefficient of LED (economic development level) is insignificant in the majority of the models, with a maximum value of 0.017 (p > 0.05). This suggests that the current model structure may not effectively capture the more complex interactions between variables, and a linear framework may not fully reflect the impact of these indicators on HQDRT. One possible reason is that digital technology directly promotes the quality enhancement of road transportation by optimizing management processes and improving operational efficiency. For example, the application of intelligent traffic systems enables real-time data monitoring and scheduling, effectively reducing traffic congestion and transportation costs, thereby enhancing efficiency and service quality. On the other hand, this may reflect a nonlinear association between the economic indicators of these control variables and the quality of road transportation. Especially during the early stages of economic development, the introduction of technological innovation may have a short-term impact on improving transportation efficiency that exceeds the influence of other traditional economic factors. Additionally, the control variables may have nonlinear relationships that require further dynamic analysis to reveal how these variables influence HQDRT at different stages.

Table 3 Regression results.

Results of synergistic development levels

Figure 4 clearly illustrates the changes in the coupling coordination degree of UDTL and HQDRT across 12 prefecture-level cities in Heilongjiang Province from 2011 to 2022. During this period, the overall coordination degree increased significantly from 0.185 in 2011 to 0.296 in 2022, reflecting positive advancements in digital technology in Heilongjiang Province. However, this improvement remains in a state of imbalance, indicating that despite progress, overall development still faces challenges of structural disequilibrium. For example, in Harbin, the coordination degree increased from 0.382 in 2011 to 0.593 in 2022, highlighting the leading role of the provincial capital in the application of digital technology and integration of transportation infrastructure. Harbin’s digital transformation, particularly the digital upgrades of major transportation corridors such as the Suiman Expressway and the Beijing-Harbin Expressway, not only improved traffic efficiency but also reinforced the city’s central role in regional transportation. These changes provide valuable experiences for other prefecture-level cities and foster synergistic effects across the province.

However, significant disparities between regions still persist. For instance, Qiqihar and Daqing exhibit higher coupling coordination degrees, relying on relatively well-developed road networks and digital technology capabilities, whereas Shuangyashan and Jixi, despite some improvements in provincial development, are still in a state of “barely coordinated,” indicating deficiencies in technology application and infrastructure development in these peripheral cities. This marked regional heterogeneity reflects the differences among cities in resource allocation, policy support, and technology adoption capabilities, highlighting the uneven integration effects of digital technology on transportation development. Further analysis of the heatmap trends indicates that after 2019, although the overall level of digital technology development has improved, the growth in coupling coordination degree has significantly slowed down for most cities. This trend emphasizes that relying solely on traditional urban development models and infrastructure construction is insufficient to meet the demands of high-quality development, highlighting the need for a deep integration of digital technology with the transportation system. The digital transformation of road transportation is a crucial path to addressing developmental bottlenecks and should focus on intelligent upgrades to enhance vehicle-road collaboration and logistics management capabilities. Future research could employ kernel density estimation methods to analyze the spatiotemporal evolution characteristics of each prefecture-level city and explore potential driving mechanisms to better guide coordinated development strategies among different cities in Heilongjiang Province.

Fig. 4

Heat map of coupling degree C value and coordination degree D for UDTL and HQDRT.

Spatio-temporal variation results

To further reveal the spatiotemporal differences in the coordinated evolution of UDTL and HQDRT, this study utilized Matlab 2022b and StataMP 17 to generate relevant three-dimensional and two-dimensional kernel density plots, comprehensively analyzing their dynamic evolution characteristics, with a focus on aspects such as distribution location, movement trends, distribution persistence, and peak morphology⁵⁹. Figure 5 shows that from 2011 to 2022, the overall distribution center of UDTL and HQDRT’s coordinated development among the 12 prefecture-level cities in Heilongjiang Province has shifted significantly to the right, reflecting a continuous improvement in coupling coordination degree. This trend indicates that the coordination between digital technology levels and regional high-quality development is continually deepening, facilitating improvements in overall development quality. However, despite the distribution center shifting to a higher range of D values (mainly concentrated between 0.2 and 0.3), the significant increase in peak height and the narrowing of peak width suggest a worsening polarization of regional development, with some areas making significant progress in the coordinated promotion of digital technology and high-quality development, while others lag behind. This polarization phenomenon is closely related to the level of digitalization of transportation infrastructure. For instance, cities along the province’s major highway corridors have significantly enhanced their levels of coordinated development due to early advantages in digital technology application and infrastructure construction. In contrast, cities with weak coverage of road networks lag in the application of digital technology and infrastructure construction, exacerbating the trend of uneven regional development.

Further analysis indicates that the kernel density distribution shape of coupling coordination degree evolved from a “bimodal” to a “trimodal” distribution between 2011 and 2022, highlighting a multi-level differentiation between UDTL and HQDRT. This shift is closely related to the digital economy and infrastructure construction policies implemented in Heilongjiang Province in recent years, where core cities such as Harbin and Daqing have led the digital transformation, while some peripheral cities have failed to keep pace with technological advances due to weak economic foundations and delayed policies, resulting in slow improvements in coordinated development. The elongation of the right tail of the kernel density curve further indicates that the gaps in coordinated development between cities are widening, reflecting the unevenness of regional economic development and the significant achievements and disparities in digitalization efforts.

Fig. 5

3D kernel density map of the synergistic evolution of UDTL and HQDRT. Note: The vertical axis represents the kernel density values of the synergistic development of UDTL and HQDRT, while the horizontal axis denotes the coupling coordination degree of UDTL and HQDRT. The vertical axis also indicates the year range from 2010 to 2022.

Figure 6 further validates the above analysis. The overall trend shows that since 2011, the density curve in the two-dimensional kernel density plot has shifted to the right each year, particularly evident in 2015, 2019, and 2022. This change is consistent with the results of the three-dimensional kernel density analysis, further confirming the gradual enhancement of the coordinated development between UDTL and HQDRT. Furthermore, the changes in distribution width of the two-dimensional kernel density plot further substantiate the phenomenon of regional development polarization. In 2011, the relatively wide density distribution suggests that the coordination status among the cities was relatively balanced. However, over time, the density curve gradually converged; particularly in 2022, the concentration on the right side significantly increased, forming a clear unimodal trend. This indicates that the coordination levels of technology and infrastructure in some cities are improving, while the relative lag of other cities has become increasingly evident, further reinforcing the polarization phenomenon observed in the three-dimensional kernel density plot. The continuation of the right tail phenomenon is also reflected in the two-dimensional kernel density plot, indicating that some cities still lag in the coordinated evolution of UDTL and HQDRT. This finding again validates the conclusions drawn from the three-dimensional analysis, highlighting the ongoing issue of uneven regional economic development and the widening spatial gaps.

Fig. 6

2D kernel density maps for the co-evolution of UDTL and HQDRT in 2011, 2015, 2019 and 2022. Note: The vertical axis represents the kernel density values of the synergistic development of UDTL and HQDRT, while the horizontal axis indicates the coupling coordination degree of UDTL and HQDRT.

Driver results

Identifying the driving factors behind the coordinated evolution and spatiotemporal differences of UDTL and HQDRT is crucial for a deeper understanding of their interrelationships. This study employs a quantile method to discretize ten potential driving factors and analyzes them using a geographic detector. The results (Fig. 7) indicate that during the study period, five factors—economic development level (LED), government intervention level (DGI), population density (PD), market openness (DMO), and environmental regulation (ER)—significantly influence the coordinated evolution and spatiotemporal differences of UDTL and HQDRT. Preliminary factor detector analysis suggests that independent driving factors alone cannot sufficiently explain the complex relationship between UDTL and HQDRT; hence, further interaction detector analysis is particularly necessary to explore the interactions and dependencies among different factors.

Specifically, the nonlinear enhancement effect of openness (DOFT) on industrial structure (IS) is particularly apparent. Openness promotes industrial structure upgrading, attracts foreign investment into high-tech fields, and brings advanced production technologies and management models. This technology spillover effect significantly enhances local enterprises’ production efficiency and innovation capacity, enabling them to better adapt to the demands of the digital economy. An open market environment allows enterprises to more easily access international market information and resources, thereby optimizing industrial structure and promoting high-quality development. Additionally, openness facilitates international technology transfer and knowledge exchange, providing more technical support for local enterprises and effectively improving transportation efficiency, such as through the introduction of intelligent logistics systems and automated traffic management. Regarding the nonlinear enhancement effect between industrial structure (IS) and urbanization level, an optimized industrial structure can provide more high-value-added job opportunities, attracting population agglomeration in urban areas and further accelerating the urbanization process. The scale and agglomeration effects brought about by urbanization lay a solid foundation for infrastructure investment and digital technology applications, enhancing labor market efficiency and promoting knowledge dissemination, thereby driving regional economic development. Urbanization also promotes the development of intelligent transportation systems and smart city technologies, enhancing transportation efficiency and resource utilization. For instance, the increase in urban population leads to higher demand for public transportation, stimulating the construction of intelligent transportation systems. Moreover, urbanization also drives the construction of digital infrastructure, such as fiber optic networks and 5G base stations, ensuring the widespread application of digital technologies.

Regarding the diminishing effect between government intervention level (DGI) and economic development level (LED), excessive government intervention may lead to a disconnection between policies and market demands, limiting the effectiveness of coordinated evolution in the context of a gradually maturing market and optimizing economic structure. Excessive administrative intervention may stifle market vitality, hinder enterprise innovation, and adversely affect the quality of economic development. In the future, the government should shift its focus towards providing public services and creating a fair competitive environment rather than directly intervening in market operations to avoid resource misallocation and the emergence of inefficient projects, ensuring that resources flow to high-tech industries and promoting the healthy development of the overall economy.

Fig. 7

Significance plot of the 10 drivers. Note: The vertical axis represents the p-values, while the horizontal axis indicates the time range from 2011 to 2022. Different colors denote the p-values of various variables, with the black line representing p = 0.1. Values below the black line are considered significant at the 10% level, while the red line indicates p = 0.05, with values below the red line regarded as significant at the 5% level.

Discussion

This study systematically analyzes the impact effects, spatiotemporal differences in coordinated evolution, and driving factors between UDTL and HQDRT using a multidimensional approach that includes econometric models, coupling coordination models, kernel density analysis, and geographic detectors. The research reveals the dynamic interplay of coordinated development between the two, particularly within the context of Northeast China’s old industrial base. The coupling coordination results indicate that the coordinated evolution between UDTL and HQDRT exhibits a “core-periphery” binary structure, reflecting profound differences in urban innovation capacity, institutional environment, and resource allocation. For example, core cities such as Harbin promote the effective coupling of digital technology and road transportation through technological accumulation and institutional innovation, creating regional innovation spillover effects. However, peripheral cities face developmental bottlenecks due to a lack of innovative resources and institutional inertia. The lag in digital technology not only restricts the high-quality development of their road transportation but also exacerbates regional imbalances. The slowdown in the growth of coordinated development after 2019 indicates that the current development model may be constrained by institutional factors, revealing the tension between technological innovation and institutional change, which limits the breadth of coordinated evolution and the reconstruction of regional innovation ecosystems. Kernel density estimation indicates that the coordinated evolution of UDTL and HQDRT has transitioned from a “bimodal” to a “trimodal” distribution, with some cities like Harbin forming a “high-quality development pole,” while others, due to technological lag, find themselves in a state of “near disharmony.” The right-tail phenomenon suggests that some cities lag in digital transformation and infrastructure development, impacting the coordinated development of regional economies. Therefore, local governments should increase support for underdeveloped areas to avoid further fragmentation. Moreover, factor analysis shows that economic development level, government intervention, population density, and market openness are crucial for the coordinated evolution of UDTL and HQDRT. However, preliminary analysis indicates that these factors alone cannot adequately explain the complex relationship between the two. Their nonlinear enhancement effects suggest that regional development has entered a stage of multidimensional integrated development, reflecting the deep dynamics of institutional innovation, technology diffusion, and social transformation.

Based on the above discussion, this study proposes the following recommendations:

Promotion strategies for digital technology in core cities (e.g., Harbin): Firstly, as a “leader” in regional innovation, Harbin should take proactive measures to integrate digital technology with transportation infrastructure. It is recommended that a provincial digital transportation innovation center be established, focusing on the research and promotion of intelligent transportation management technologies and systems. This center should provide funding support and technical services to facilitate the construction of real-time traffic monitoring and smart logistics platforms. Additionally, leveraging big data and artificial intelligence, a comprehensive urban traffic management system should be developed to enhance traffic flow regulation and safety management. This strategy requires close collaboration among government entities and industry stakeholders to ensure the rapid application of technology and effective implementation of policies.

Support measures for peripheral cities (e.g., Qiqihar): For relatively underdeveloped cities like Qiqihar and other peripheral areas, it is advisable to establish differentiated strategies for promoting digital technology. There should be a priority on developing foundational digital infrastructure, including fiber optic networks and mobile communication facilities, to improve the basic conditions for digital technology application. Simultaneously, it is recommended to establish special funds to encourage enterprises to innovate in the fields of intelligent transportation and digital infrastructure, thereby narrowing the gap with core cities. In implementing these measures, it is essential to consider local industrial characteristics and encourage collaboration between regional enterprises and higher education institutions to facilitate technology transfer and talent training, thereby elevating local technical application levels and innovation capabilities.

(3) Regional collaboration and policy environment development: Furthermore, to promote balanced regional development, it is recommended to establish cross-regional collaboration mechanisms to optimize resource allocation through a “strong supports weak” strategy. Local governments can collaboratively construct a provincial digital transportation collaborative innovation platform to consolidate resources and promote the cross-regional flow of technological elements. In terms of the policy environment, it is advisable to review and optimize existing institutional arrangements to adapt to the rapidly changing technological landscape and reduce friction between government intervention and the market. Specifically, measures such as tax reductions and streamlining approval processes can be employed to incentivize corporate research and development investments and promote technological innovation.

Conclusion

This study uses panel data from Heilongjiang Province for the period 2011–2022 as a sample, analyzing the interaction mechanism between UDTL and HQDRT from a dual-effect perspective, and further revealing the driving factors behind their spatiotemporal differences. The research reveals a significant trend of synergistic growth between UDTL and HQDRT, but there is notable heterogeneity in regional development. Core cities foster regional innovation “spillover effects” through technological accumulation and institutional innovation, while peripheral cities face challenges of lagging collaborative evolution due to their relatively weak innovative capabilities. The coupling coordination analysis presents a typical “core-periphery” binary structure, reflecting deep-seated differences in urban innovation capacities and resource allocation. Kernel density estimation shows that the coordinated evolution has shifted from a “bimodal” to a “trimodal” distribution, validating the intensification of regional differentiation trends. The slowdown in coordinated development growth after 2019 suggests that the existing development paradigm encounters institutional constraints. Lastly, the analysis of driving factors reveals nonlinear interaction effects among multidimensional elements such as economic development, government intervention level, and market openness, indicating that cities have entered a stage of multilevel integrated development in the synergistic growth of UDTL and HQDRT, where traditional single-factor driven models are inadequate to address real-world development challenges. This study constructs a multidimensional analytical framework for the synergistic evolution of urban digital technology levels and high-quality road transportation development. Through coupling coordination, kernel density estimation, and other multivariate quantitative methods, the study systematically reveals the dynamic mechanisms of UDTL and HQDRT’s coordinated evolution and their spatiotemporal heterogeneity, thereby extending traditional linear research paradigms and enriching theoretical implications. This research holds significant practical implications for advancing the strategy of a strong transportation nation, achieving high-quality development, and revitalizing Northeast China.

The innovations of this study include: Firstly, this study focuses on Northeast China’s old industrial base, revealing the challenges faced in the transformation of UDTL and HQDRT, including issues related to inefficient resource allocation, thus enriching the study of relevant mechanisms in this region. Secondly, it is the first time that systems synergy theory has been applied to the analysis of UDTL and HQDRT, revealing the dynamic interaction mechanisms between the two and providing a theoretical basis for policy formulation. Furthermore, by utilizing various methods, including econometric models, coupling coordination models, kernel density estimation, and geographic detectors, the study thoroughly explores the influence effects and spatiotemporal differences between the two, thereby making the research more comprehensive both theoretically and empirically. Finally, specific policy recommendations are made for different cities. For instance, it is suggested that Harbin enhance the construction of innovation technology platforms, while for Qiqihar, the emphasis is placed on improving digital infrastructure, providing practical guidance for local governments in advancing the integration of the digital economy and transportation infrastructure.

Despite offering important insights, this study also has several limitations. Firstly, the panel data used in this study focuses primarily on various prefecture-level cities in Heilongjiang Province, reflecting the province’s unique economic and social characteristics. Future research could incorporate data from other provinces nationwide to conduct a deeper comparative analysis, thereby verifying the general applicability of the findings. Secondly, this study primarily explores the coupling coordination relationships between cities at a macro level, without addressing factors at the micro level. Future research could consider perspectives from enterprises, industries, and social individuals to explore more detailed driving mechanisms and influence pathways. Additionally, although the coupling coordination model has demonstrated good applicability in analyzing urban development, its general applicability across all types of cities still requires further verification. For example, structural and functional differences among cities at different stages of development may impact the model’s effectiveness. Therefore, future research should assess the applicability of the model across different types of cities to ensure the reliability and validity of the results. Lastly, the time span of the panel data may limit the capture of long-term trends. Future research could extend the time frame and incorporate spatial correlation networks or spatial econometric models to deepen the understanding of the dynamics of coupling coordination evolution.