FEN WANG
Associate Professor Unit: School of International Economics and Trade,
Ningbo University of Finance and Economics
Unit province, city, and postal code:
Ningbo City, Zhejiang Province 315175315175,
Ningbo Zhejiang China
Email: worphan77@163.com
Abstract—In order to explore the role of intelligent shipping technology in boosting the carbon neutrality target of waterway transportation, taking the current situation of carbon emission in waterway transportation as the entry point, analyzing the development trend of intelligent shipping technology and its relevance in carbon emission reduction, combining the experimental data of the typical ships of Yangtze River Shipping and Coastal Shipping, and constructing the comprehensive analysis model based on the multi-dimensional evaluation index system, assessing the energy saving, emission reduction, and economic benefits of intelligent shipping technology in the Specific performance. The results show that intelligent shipping technology significantly reduces the carbon emission intensity per unit of transportation turnover, improves energy utilization efficiency, and the contribution rate of emission reduction reaches more than 20%, while showing the potential in automated navigation, energy efficiency management and carbon emission monitoring. Promoting the widespread application of intelligent shipping technologies in the field of waterway transportation will provide important support for achieving the goal of carbon neutrality.
Keywords-smart shipping; waterborne transportation; carbon neutrality; carbon emissions I. Introduction
As an important part of the global logistics system, the energy efficiency level and carbon emission characteristics of waterway transportation have far-reaching impacts on the realization of global climate change and environmental governance goals. The carbon intensity reduction target by 2030 proposed by the International Maritime Organization highlights the urgency of the industry’s green transformation. However, the traditional shipping model is difficult to meet the demand for decarbonization due to technological bottlenecks and management model limitations. The rise of smart shipping technology provides a new solution path for the shipping industry. Core technologies such as automated navigation, energy efficiency optimization and real-time carbon emission monitoring can significantly improve operational efficiency and reduce fuel consumption and carbon intensity. Promoting the in-depth integration of smart shipping technology and carbon neutral target is not only an important direction for the green development of shipping industry, but also one of the key initiatives to deal with the global climate crisis, which has important academic value and practical significance.
II. Analysis of the Current Situation of Carbon Emission in Waterway Transportation and the Development of Intelligent Shipping
A. Current status of carbon emissions from waterborne transport
The current status of carbon emissions from waterway transportation, as an important component of global cargo transportation, has attracted widespread attention. According to the latest industry statistics, the annual carbon dioxide emissions from global maritime transportation account for about 12% of the total carbon emissions from global transportation, with specific sources including fuel oil combustion, increased navigational resistance and energy consumption during port calls. Among them, fuel combustion accounts for more than 80% of the emissions, mainly concentrated in large cargo ships and tankers, especially in ocean transportation [1]. According to IMO (International Maritime Organization), the global shipping industry needs to achieve a 40% reduction in carbon intensity by 2030, which makes accurate measurement and reduction of carbon emissions particularly urgent. In recent years, advanced carbon emission monitoring technologies have been applied, such as the use of real-time monitoring systems to analyze emission data from different voyages and ship types. Taking ocean-going cargo ships as an example, the monitoring data of tonne-kilometer carbon emissions show that the intensity of carbon emissions under full load is generally higher than that of some offshore vessels. This phenomenon is not only related to sailing distance, but also affected by ship design, oil quality and operation mode. In order to further optimize the emission reduction strategy, it becomes necessary to establish shipping carbon emission models based on actual operation data. In these models, the commonly used formula is [2]:
Where E is the total emission, F is the fuel consumption factor, S is the average ship speed, and T is the total running time. Through the dynamic parameter adjustment of the above formula, the carbon emission level of the shipping industry can be predicted and optimized more scientifically. To further explore the effects of regional differences and ship type differences on emissions, Table 1 below, the fuel consumption and carbon emission intensity of ocean routes are generally higher, while the low-speed optimization strategy has more significant energy saving and emission reduction effects on offshore routes. It provides directional guidance for the subsequent application of intelligent shipping technology and lays the foundation for achieving the carbon neutral goal.
- Comparison of carbon emission intensity of different types of ships
|
Vessel type |
Route type |
Average speed (knots) |
Fuel consumption factor (g/ton-km) |
Carbon intensity (g CO₂/ton-km) |
|
container ship |
the open ocean (far from the coast) |
20 |
15 |
300 |
|
bulk carrier |
offshore |
12 |
10 |
120 |
|
tanker (ship) |
the open ocean (far from the coast) |
15 |
18 |
270 |
B. Status of Intelligent Shipping Technology Development
In recent years, the rapid development of intelligent shipping technology has provided an important opportunity for the green transformation and efficient operation of the traditional shipping industry. Intelligent shipping technology covers a wide range of fields such as automated navigation systems, intelligent energy efficiency optimization platforms, shipping big data analysis and decision support systems. Among them, the automated navigation system has gradually realized the leap from auxiliary operation to fully autonomous driving, and significantly reduced sailing time and fuel consumption by combining real-time meteorological information and marine environment data for route optimization. On the other hand, the intelligent energy efficiency optimization platform further optimizes fuel efficiency by integrating ship sensor data and fuel consumption models, analyzing sailing status in real time and dynamically adjusting engine power and load distribution. In addition, the big data analysis and decision support system shows unique advantages in port logistics optimization and ship management, and is able to predict port congestion through machine learning algorithms and suggest optimal berthing strategies to shorten berthing time and reduce energy consumption and carbon emissions.
The maturity of intelligent shipping technology is also characterized by stage-by-stage development. Taking the global patent application data in recent years as an example, the number of patent applications involving automated navigation has increased by nearly 200% from 2015 to 2023, reflecting the industry’s high attention to this field [3]. Although the technology is developing rapidly, it also faces many challenges, such as high technology integration costs, insufficient standardization, and data security risks. These bottlenecks limit the full-scale promotion of smart shipping technologies globally. However, with the acceleration of the trend of decarbonization and digitization, the future smart shipping will pay more attention to the synergy between technologies and achieve a larger range of application landing through policy incentives and technological innovation, which will inject sustainable power for the green development of the waterway transportation industry.
C. Analysis of the correlation between smart shipping and carbon emission reduction
There is a close correlation between smart shipping technology and carbon emission reduction, mainly in the three dimensions of shipping efficiency improvement, fuel consumption optimization and carbon emission monitoring. Intelligent shipping optimizes route planning through automated navigation technology and uses real-time weather and marine environment data to reduce resistance and fuel consumption during navigation. According to the trial data of global shipping companies, the average voyage fuel efficiency of ships using route optimization systems has increased by 12%-18%, which directly reduces carbon emissions per unit mileage [4]. The intelligent energy efficiency management platform collects data such as engine load and fuel consumption rate in real time through the ship’s sensor network, and utilizes artificial intelligence algorithms to dynamically adjust the sailing speed and engine power matching, which further reduces energy waste.
The key to smart shipping technology in carbon emission reduction also lies in its ability to accurately monitor and predict carbon emissions. Combined with big data analysis and Internet of Things technology, the intelligent shipping system is able to analyze the whole chain of sources, pathways and total amount of carbon emissions and generate optimization schemes. This systematic emission reduction measure realizes synergistic emission reduction of ships, ports and logistics, and effectively reduces potential carbon emission hazards in the transportation process. In order to visually demonstrate the multi-dimensional promotion effect of intelligent shipping on carbon emission reduction, Figure 1 below reveals the relationship between carbon emission reduction and intelligent technology in terms of fuel optimization, sailing efficiency, port management, etc., by means of a multi-factor comparison model.
- Figure 1 Correlation between smart shipping and carbon reduction
The figure demonstrates the synergistic effect of improved shipping efficiency and fuel consumption optimization on carbon emission reduction, while highlighting the key contribution of smart technologies to the achievement of energy saving and environmental protection goals. In the future, by accelerating the promotion and application of smart shipping technologies, the goal of carbon neutrality in waterway transportation will be more achievable.
III. Research methodology and design
A. Research framework
The construction of the research framework is the key foundation for analyzing the role of smart shipping in contributing to the carbon neutrality target of waterway transport. A three-dimensional coupled model of “technology-policy-market” is adopted to explore the multi-level impacts of the application of smart shipping technologies on carbon emissions through a systematic approach. In the framework, the technology level includes core technologies such as automated navigation, energy efficiency optimization platforms and carbon emission monitoring, and focuses on analyzing the effects of their application in different shipping scenarios and their contribution to carbon emission reduction; the policy level focuses on the impacts of international and regional regulations, subsidy policies, and the formulation of technical standards on the promotion of smart shipping; and the market level examines the acceptance of smart technologies by shipping enterprises and the economic costs and benefits trade-offs of the implementation of smart technologies. The policy level focuses on the impact of international and regional regulations, subsidies and standards on the promotion of smart shipping. The dynamic coupling relationship among the three is revealed through an analytical strategy using both quantitative and qualitative methods, and validated by an indicator system based on the hierarchical analysis method (AHP) [5].
In order to visualize the logical relationship of the research framework, the following Figure 2 describes in detail the roles and interaction paths of the three-dimensional model of technology, policy, and market in this study, demonstrating the main path of technological innovation to drive the benefits of carbon emission reduction, the policy support to provide guarantee, and the market demand to promote the popularization and deepening application of intelligent shipping technology. Through this framework, the comprehensive role of intelligent shipping in promoting waterway transportation to achieve carbon neutrality can be systematically revealed, providing a scientific basis for policy formulation and technology development.
- Schematic diagram of the research framework
B. Subjects of study
In this study, representative ships of two typical shipping regions, namely, Yangtze River Shipping and Coastal Shipping, are selected as research objects, specifically including 5 each of 3000 TEU-class container ships, 50,000-ton bulk carriers and 80,000-ton tankers, totaling 15 ships. These research samples cover different route types, different ship sizes and different cargo types, with strong representativeness. The study period is from January to December 2023, covering shipping data from different seasons throughout the year. The selected ships are equipped with the core modules of intelligent shipping systems, including ship energy efficiency management system (SEMS), navigation situational awareness system (NSAS) and intelligent ship management platform (ISMP). Among them, the SEMS system monitors key parameters such as main engine energy consumption, auxiliary engine energy consumption, speed and load in real time, the NSAS system integrates meteorological navigation, route optimization and collision avoidance warning, and the ISMP platform is responsible for the comprehensive functions of ship equipment management, maintenance warning and energy efficiency analysis [6]. These systems interconnect data with the shore-based management center through the ship’s LAN to form a complete intelligent shipping technology application system. In order to ensure the reliability of the research data, all the research objects are monitored for carbon emissions in accordance with the ISO 14064 greenhouse gas verification standard, and the data is verified by a third-party organization.
C. Construction of Research Indicator System
Based on the research framework and the characteristics of the research object, a multi-level and multi-dimensional evaluation index system is constructed. The indicator system adopts the hierarchical analysis method (AHP), starting from three first-level indicators of technology application, energy saving and emission reduction, and economic benefits, which are refined into 12 second-level indicators and 36 third-level indicators. The weights of the indicators at each level were determined by the Delphi method, and 20 experts from shipping enterprises, scientific research institutions and management departments were invited to conduct three rounds of evaluation. The technology application indicators (weight 0.4) include intelligent system coverage rate, data collection accuracy rate, system response time, etc.; energy saving and emission reduction indicators (weight 0.35) include carbon emission per unit of transportation turnover, energy utilization efficiency, emission reduction contribution rate, etc.; and the economic benefit indicators (weight 0.25) include system investment payback period, operation cost saving rate, maintenance cost, etc. [7]. The specific indicator system is shown in Table 2, which is quantitatively analyzed by the fuzzy comprehensive evaluation method, and the evaluation standard adopts five levels of quantitative criteria (excellent, good, general, poor, and poor) and establishes the corresponding affiliation function. The data of each index are automatically collected by the intelligent shipping system or obtained by manual statistics, and standardized processing is adopted to eliminate the influence of the quantitative outline. The evaluation results are weighted and synthesized to arrive at the final evaluation score, which provides a quantitative basis for the effect of intelligent shipping technology on the goal of carbon neutrality.
- Smart shipping carbon emission reduction evaluation index system and weight allocation
|
Level 1 indicators |
Secondary indicators |
Tertiary indicators |
|
Technology applications (0.40) |
System coverage (0.15) |
ITS coverage (0.06) |
|
Data collection accuracy (0.05) |
||
|
System response time (0.04) |
||
|
Operational efficiency (0.13) |
Route optimization rate (0.05) |
|
|
Equipment utilization rate (0.04) |
||
|
Fault warning accuracy (0.04) |
||
|
Safety performance (0.12) |
System stability (0.04) |
|
|
Data security (0.04) |
||
|
Emergency response capacity (0.04) |
||
|
Energy saving and emission reduction (0.35) |
Energy efficiency (0.12) |
Energy use efficiency (0.04) |
|
Energy consumption per unit (0.04) |
||
|
Rate of optimization of energy mix (0.04) |
||
|
Emission control (0.12) |
Carbon intensity (0.04) |
|
|
Emission reduction contribution (0.04) |
||
|
Emission monitoring accuracy (0.04) |
||
|
Economic benefits (0.25) |
Cost-effectiveness (0.13) |
Payback period (0.05) |
|
Operating cost savings (0.04) |
||
|
Maintenance costs (0.04) |
IV. Experimental design and implementation
A. Smart Shipping Technology Application Program
It adopts a phased and multi-dimensional intelligent shipping technology application program, focusing on the implementation of three core systems, namely the ship energy efficiency management system (SEMS), the navigation situation awareness system (NSAS) and the intelligent ship management platform (ISMP). In the SEMS system, a deep learning-based energy efficiency prediction model is deployed, which analyzes historical voyage data and establishes a nonlinear relationship model between ship energy consumption and factors such as speed, load, and meteorological conditions. The core algorithm of the model adopts the LSTM (Long Short-Term Memory) network architecture, and its prediction accuracy reaches more than 95% [8].The NSAS system integrates multi-source data such as BeiDou navigation, AIS communication, and meteorological radar to construct a real-time navigation situational awareness network, which realizes millisecond-level data processing and decision-making response through edge computing technology.ISMP The ISMP platform adopts a distributed architecture and establishes an integrated management system that includes ten functional modules such as equipment management, maintenance warning, and energy efficiency analysis. The platform realizes real-time data interoperability with the shore-based center through a 5G private network and sets up a triple data security protection mechanism. The system integration adopts modularized design, and the subsystems are seamlessly connected with each other through a unified data interface, which ensures the stability and scalability of the overall system. The specific implementation process of the technical program is shown in Table 3:
- Flow chart for the implementation of smart shipping technology
|
point |
time cycle |
Main tasks |
intended target |
|
preparatory phase |
1 month |
System installation and commissioning |
Completion of hardware deployment and system tuning |
|
adaptation period |
2 months |
Personnel training and system optimization |
Stabilization of the system |
|
implementation period |
6 months |
Data Acquisition and Analysis |
Formation of a complete experimental dataset |
|
assessment period |
3 months |
Effectiveness evaluation and optimization |
Completion of system performance evaluation report |
B. Experimental grouping
To ensure the scientific and comparable results of the experiment, a controlled experimental design method was used to group the 15 sample ships according to ship type and route characteristics. The experimental group included nine ships fully equipped with intelligent shipping systems, and the control group included six ships with only basic navigation equipment. The experimental group was further subdivided into three subgroups: the oceanic group (3 ships), the offshore group (3 ships), and the inland waterway group (3 ships), in order to study the effect of the application of the intelligent system under different navigational environments [9]. Meanwhile, considering the influence of seasonal factors on shipping carbon emissions, the experimental cycle was divided into four quarters, and data collection and comparative analysis were conducted separately in each quarter. In order to reduce the experimental error, all the ships participating in the experiment are selected with similar construction year (2018-2020) and similar technical specifications, and to ensure the consistency of the crew’s operation level. Detailed information of the experimental grouping is shown in Table 4:
- Grouping of experimental vessels
|
groups |
Number of ships |
Vessel Configuration |
Average load capacity |
Intelligent System Configuration |
|
Experimental group A |
3 ships |
container ship |
3000 TEU |
Complete Intelligent Systems |
|
Experimental group B |
3 ships |
bulk carrier |
50,000 tons |
Complete Intelligent Systems |
|
Experimental group C |
3 ships |
tanker (ship) |
80,000 tons |
Complete Intelligent Systems |
|
Control group A |
2 ships |
container ship |
3000 TEU |
Basic navigation equipment |
|
Control group B |
2 ships |
bulk carrier |
50,000 tons |
Basic navigation equipment |
|
Control group C |
2 ships |
tanker (ship) |
80,000 tons |
Basic navigation equipment |
C. Data collection methods
The research adopts the acquisition method of multi-source data fusion, and establishes a comprehensive data acquisition system covering ship operation status, energy consumption and environmental parameters. Ship operation data are collected in real time through a network of intelligent sensors distributed on key equipment, with a sampling frequency of once per second, and the main parameters include main engine rotational speed, torque, power output, fuel flow rate, etc. [10]. The energy consumption data adopt a dual validation mechanism, i.e., direct measurement through the fuel flow meter combined with indirect calculation based on the calorific value method, to ensure the accuracy of the data. Environmental parameters, on the other hand, are acquired through the shipboard weather station and external meteorological data interface, including wind speed, wind direction, air temperature, humidity, sea state and other information. After pre-processing, all collected data are transmitted to the ship’s comprehensive data center through standardized interfaces, and distributed storage architecture is adopted to ensure data security. Data quality control adopts a three-level filtering mechanism: sensor level, system level and manual audit level to ensure the authenticity and reliability of data. The key indicators of the data acquisition process are shown in Table 5:
- Table of key indicators for data collection
|
data type |
sampling frequency |
data level |
Precision Requirements |
Storage Methods |
|
operational state |
1 time/second |
TB level/month |
±0.1% |
distributed storage |
|
energy consumption |
1x/minute |
GB level/month |
± 0.5% |
distributed storage |
|
Environmental parameters |
1 time/hour |
MB/month |
±1.0% |
Centralized storage |
V. Experimental results and analysis
A. Analysis of changes in carbon emissions
In the practical application of smart shipping technology, the trend of carbon emissions becomes the core element in assessing its contribution to the goal of carbon neutrality. By comparing the data of the experimental group and the control group, intelligent shipping technology significantly reduces the carbon emission intensity per unit of transportation turnover. The average carbon emission intensity of ships in the experimental group decreased from the original 220 g CO₂/ton-km to 175 g CO₂/ton-km, a decrease of 20.5%, whereas the control group only showed weak fluctuations, with a change of less than 5% [11]. The most significant reduction was observed in ocean routes, especially in 3000 TEU class container ships, where the carbon intensity was reduced from 300 g CO₂/ton-km to 240 g CO₂/ton-km, while the reduction in bulk carriers in offshore routes was concentrated in the low-speed optimization stage, where the carbon intensity of their carbon emissions was reduced from 120 g CO₂/ton-km to 95 g CO₂/ton-km.
In order to deeply analyze the emission reduction effect, the carbon emissions under different ship types and routes were modeled [12]. By optimizing the speed and sailing path, the fuel consumption factor of the experimental group was reduced by 15%-18%. As shown in Table 6, the path optimization function of the automated navigation system reduced the fuel consumption factor of the ocean-going container ship from 15 g/ton-km to 12.5 g/ton-km, which significantly reduced the emission level. In addition, the real-time situational awareness function of the NSAS system further reduces the ineffective fuel consumption during navigation by adjusting the speed in combination with meteorological conditions. Figure 3 shows the dynamic trend of carbon emissions with the optimization of sailing speed and the improvement of fuel efficiency, in which ocean and offshore routes show obvious differences, especially after route optimization, the improvement of fuel efficiency directly drives the reduction of carbon emission reduction intensity. From a comprehensive point of view, the application of intelligent shipping technology not only shows significant emission reduction effect in quantitative indexes, but also proves the far-reaching impact of optimized sailing mode and energy efficiency management on carbon emission of waterway transport in technical details, which lays a solid foundation for realizing the goal of carbon neutrality.
- Ship Type and Fuel Optimization Data Sheet
|
Vessel type |
Route type |
Average speed (knots) |
Fuel consumption factor (g/ton-km) |
Carbon intensity (g CO₂/ton-km) |
||
|
pre-optimization |
post-optimization |
pre-optimization |
post-optimization |
|||
|
container ship |
the open ocean (far from the coast) |
20 |
15 |
12.5 |
300 |
240 |
|
bulk carrier |
offshore |
12 |
10 |
8.5 |
120 |
95 |
|
tanker (ship) |
the open ocean (far from the coast) |
15 |
18 |
15 |
270 |
225 |
- Effect of speed optimization on fuel efficiency and carbon emission
B. Assessment of energy efficiency improvements
The application of intelligent shipping technology significantly improves the energy efficiency of ship operation. Through systematic analysis of the experimental data, it is found that the comprehensive energy efficiency index (EEOI) of the ships in the experimental group has been improved by an average of 23.8% compared with that of the control group. The energy efficiency improvement is mainly reflected in three aspects: main engine energy efficiency optimization, auxiliary engine energy consumption control and navigation resistance reduction. In terms of main engine energy efficiency, through the intelligent regulation of the SEMS system, the dynamic matching of optimal speed and torque is realized, which improves the fuel economy of the main engine by 18.5%. The experimental data show that at the same speed, the fuel consumption rate of the main engine of the experimental group is reduced from 195 g/kWh to 159 g/kWh. As for the control of auxiliary engine energy consumption, through intelligent load distribution and equipment management, the average load ratio of auxiliary engine is increased from 62% to 78%, and the energy consumption per unit time is reduced by 22.3%. In terms of navigation resistance reduction, through real-time weather navigation and route optimization, the impact of wind and waves on the ship is effectively reduced, and the average navigation resistance coefficient is reduced by 15.6%.
To quantitatively assess the effect of energy efficiency improvement, an energy efficiency assessment model based on multi-factor coupling is established:
Where η is the comprehensive energy efficiency index, Pm is the main engine output power, Fc is the fuel consumption rate, Pa is the auxiliary engine power, La is the load coefficient, Rv is the actual speed, Rs is the standard speed, and α, β, and γ are the weight coefficients. The calculation results through this model are shown in Table 7:
- Energy Efficiency Improvement Assessment Data Sheet
|
Ship Classification |
Mainframe energy efficiency improvement rate (%) |
Auxiliary energy efficiency improvement rate (%) |
Navigation efficiency improvement rate (%) |
Comprehensive energy efficiency improvement rate (%) |
|
container ship |
18.5 |
22.3 |
15.6 |
20.1 |
|
bulk carrier |
17.2 |
20.8 |
14.2 |
18.5 |
|
tanker (ship) |
19.1 |
23.5 |
16.8 |
21.2 |
C. Analysis of factors influencing the application of technology
Through multi-dimensional correlation analysis, the key factors affecting the application effect of intelligent shipping technology are identified to include four dimensions: technology maturity, crew adaptability, external environmental conditions and economic feasibility. In terms of technology maturity, system stability and data accuracy are the core indicators that determine the application effect. It is found that the intelligent shipping system can play the best performance when the system stability reaches more than 99.9% and the data collection accuracy is better than ±0.1%. In terms of crew adaptability, through the establishment of a standardized training system and assessment mechanism, the crew’s operational proficiency has increased from 65% at the initial stage to more than 95%, which significantly improves the application effect of the system. The influence of external environmental conditions is mainly reflected in weather conditions and navigation areas, in which the optimization effect of the system is reduced by about 25% in bad weather, and the regulatory requirements and infrastructure conditions of different navigation areas also significantly affect the performance of the system. The economic feasibility analysis shows that the payback period of the intelligent shipping system is significantly negatively correlated with the operational efficiency of the ship, with an average payback period of 2.8 years.
In order to quantify the intensity of the role of each influencing factor, principal component analysis was used to establish an influencing factor assessment model, and the results are shown in Table 8. The results show that technology maturity and crew adaptability are the dominant factors influencing the application effect of smart shipping technology, and they together explain 63% of the variance contribution rate. This provides an important decision-making basis for subsequent technology optimization and promotion of application. Through the in-depth analysis of the influencing factors, technology improvement strategies and application promotion programs can be formulated in a more targeted manner to further enhance the boosting role of intelligent shipping technology in waterway transportation carbon and in the process of goal achievement.
- Assessment of factors influencing the application of technology
|
factor |
weighting factor |
correlation coefficient |
significance level |
Degree of impact |
|
Technology maturity |
0.35 |
0.82 |
P < 0.01 |
unyielding |
|
Crew adaptability |
0.28 |
0.75 |
P < 0.01 |
comparatively strong |
|
External environmental conditions |
0.22 |
0.68 |
P < 0.05 |
moderate |
|
economic viability |
0.15 |
0.61 |
P < 0.05 |
weaker |
VI. Countermeasure Suggestions for Smart Shipping to Promote Carbon Neutrality
A. Technological Innovation Pathway
The technological innovation path of intelligent shipping to promote the goal of carbon neutrality needs to break through the bottleneck of traditional technology from multiple dimensions and promote the green transformation of the industry with systematic upgrading of the whole chain. First, efforts should be made to develop an intelligent navigation system based on the fusion of artificial intelligence and the Internet of Things (IoT). Through large-scale sensor networks and edge computing architectures, real-time ship operation data, meteorological information and channel status should be captured to build a dynamic navigation optimization model. Secondly, the energy efficiency optimization technology needs to develop towards precision and personalization, for example, analyzing the historical ship operation data through deep learning models, dynamically adjusting the speed and power output, and comprehensively improving the fuel efficiency [13]. At the same time, carbon emission monitoring technology must move towards a real-time, high-precision full-coverage system, relying on cloud computing platforms to integrate heterogeneous data from multiple sources, to realize the prediction of carbon emissions and source tracking, and to generate accurate emission reduction programs. In addition, the synergistic development of technology is also key, i.e., optimizing berthing and loading/unloading processes and reducing berthing energy consumption through the interconnection of information between ships and ports.
B. Recommendations for policy support
To promote smart shipping to help realize the goal of carbon neutrality, policy support should focus on promoting the deep integration of technology and the market, and creating a favorable development environment rather than mere constraints and controls. Policies should provide a stable guarantee for the R&D and application of smart shipping technologies, for example, through the establishment of a special carbon emission reduction fund to provide long-term support for technological innovation projects and enterprise transformation. At the same time, priority should be given to promoting the standardization of smart shipping-related technologies, including data interfaces, carbon emission monitoring and energy efficiency optimization, in order to reduce technical barriers to industry collaboration. Through the establishment of the standardization system, the industry can achieve the scale promotion of key technologies more quickly and lay the technical foundation for the green transformation of the waterway transportation industry [14].
There is also a need to focus on promoting the integration of technology with commercial applications and encouraging shipping enterprises to integrate smart technologies into their daily operations. For example, the application of smart shipping technologies in different types of ships and routes could be supported through pilot projects, and the promotion strategy could be optimized based on actual results. At the same time, policies could explore market-driven emission reduction mechanisms, such as the expansion of the carbon trading market, to provide additional benefits to enterprises adopting smart technologies and enhance their incentives for transformation.
In the context of globalization, policy support should also take into account regional characteristics and international collaboration. On the one hand, differentiated policies should be formulated for inland waterway shipping, offshore transport and ocean routes in accordance with local conditions, so as to adapt to the actual needs of different regions; on the other hand, international data-sharing and technological cooperation should be encouraged, so as to reduce the cost of research and development, and to promote the globalization of the application of smart shipping technologies. Such policy support will not only help shipping enterprises to improve operational efficiency, but also provide a long-term boost to the goal of carbon neutrality.
C. Implementation pathway planning
Path planning for the implementation of smart shipping to promote the goal of carbon neutrality needs to combine technology promotion, industry synergy and data-driven comprehensive strategies, with systematic and phased promotion to ensure the landing and effectiveness of technology application. In terms of technology promotion, priority should be given to the establishment of pilot areas for intelligent shipping, and representative ships and ports should be selected on major coastal and inland waterway transportation routes to conduct tests on the application of full-process technology, so as to accumulate practical experience for subsequent large-scale promotion by monitoring carbon emission data and fuel efficiency optimization effects. At the same time, the dynamic energy efficiency management system based on AI algorithms can be used to optimize sailing patterns, integrate real-time weather and sea condition information into sailing path planning, and form precise energy-saving solutions. At the level of industry synergy, it is necessary to establish a seamless connection between ships, ports and logistics platforms, realize real-time information sharing through unified data exchange protocols, optimize the timeliness of berthing, loading and unloading and departure of ships, and reduce berthing energy consumption. In addition, inter-industry technology sharing is encouraged, especially in the field of intelligent navigation and carbon emission monitoring technology, to reduce the technical threshold and application cost through joint research and development [15].
Data-driven is an important support for the implementation path, and it is necessary to build an industry-wide data collection and analysis platform, centralize the storage and modeling of ship operation data, fuel consumption and carbon emission intensity, and optimize the operation parameters in real time with the help of machine learning technology to generate carbon emission reduction strategies. This process requires cooperation with third-party organizations to enhance transparency and credibility through regular audits and public release of carbon emission reduction data. The implementation path should also be aligned with existing international policy frameworks to promote regional coordination and international cooperation in smart shipping, in order to maximize the effect of carbon emission reduction and provide strong strategic support for the long-term development of the industry.
D. Safeguards Study
In order to ensure that intelligent shipping contributes to the realization of the goal of carbon neutrality, it is necessary to build a comprehensive guarantee system from four dimensions: technological stability, financial support, talent cultivation and legal regulation. At the technical level, a reliability testing and assessment mechanism for smart shipping technology should be established to verify the stability and adaptability of the technology in different environments through a combination of simulation experiments and actual sailing data, and an emergency response system should be set up to ensure the safety and controllability of the smart system under abnormal conditions. In terms of funding, it is necessary for the government and financial institutions to jointly build a multi-level financing platform to provide special funds to support intelligent shipping projects, while introducing green financial tools such as carbon bonds to encourage enterprises to invest in energy-saving and emission reduction technologies, and to reduce the burden of technological upgrading on small and medium-sized enterprises (SMEs) through a cost-sharing mechanism.
In terms of talent guarantee, compound technical talents should be cultivated through the collaborative mode of industry-university-research, and courses and training programs related to intelligent shipping should be offered in cooperation with universities, scientific research institutes and leading enterprises in the industry, so as to build up a high-level talent team with both technological research and development capabilities and practical experience. In the legal and regulatory dimension, it is necessary to improve the regulations and standard system related to smart shipping, and define the responsibilities in the use of technology, especially in the field of automated navigation and carbon emission monitoring, to ensure the legal compliance of technology application. At the regulatory level, it is recommended to adopt blockchain technology to realize the tamper-proof storage of carbon emission data, and to establish a transparent industry credit system based on it, so as to enhance the industry’s executive power through dynamic auditing and performance ranking. This multi-dimensional safeguard will provide long-term strategic support for the widespread promotion of smart shipping and the achievement of carbon neutral goals.
VII. Conclusion
Intelligent shipping technology has demonstrated important technical advantages and application potential in promoting waterway transport to achieve the goal of carbon neutrality, effectively reducing energy consumption and carbon emission intensity in the transport process by improving fuel efficiency, optimizing route planning and real-time carbon emission monitoring. However, the current application of the technology still faces limitations such as complex system integration, high cost and insufficient standardization, and the stability and adaptability of some key technologies in large-scale application need to be further verified. In addition, environmental conditions and regulatory differences in different navigation regions pose challenges to technology promotion, and inter-industry synergy mechanisms and data sharing platforms have yet to be perfected. In the future, it is necessary to strengthen the research on energy efficiency optimization technology driven by multi-source data, improve the carbon emission monitoring and governance system of the whole chain, and explore the path of low-cost and high-efficiency technology promotion. Through continuous deepening of technological innovation and international cooperation, more theoretical support and practical reference will be provided for realizing the green transformation and sustainable development of waterway transportation.
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