A method and device for assessing port ship emission disengagement, and a medium

Abstract

The application discloses a port ship emission decoupling evaluation method and device and medium, relates to the technical field of port environmental management and traffic emission analysis, and is used for solving the problems that emission factor decomposition and decoupling evaluation are split in the prior art, non-economic factor decoupling contribution is difficult to quantitatively identify, and different emission difference driving mechanisms cannot be revealed. Including: obtaining pre-processed ship port operation basic data; adopting a five-factor decomposition model based on LMDI additive decomposition, performing a decomposition operation based on the ship port operation basic data, obtaining detailed results of ship emission driving effects covering multi-dimensional effects; and performing Tapio decoupling calculation operation on the results to obtain target decoupling state judgment results; based on the judgment results, embedding LMDI decomposition factors in reverse to decouple the effort, obtaining the total decoupling effort index after eliminating the scale effect and the contribution proportion of each sub-factor; finally, multi-dimensional coupling analysis is performed to obtain the different emission difference driving mechanisms and the collaborative emission reduction strategy.

Description

Technical Field

[0001] This invention relates to the field of port environmental management and traffic emission analysis technology, and in particular to a method, apparatus and medium for assessing the decoupling of port vessel emissions. Background Technology

[0002] Maritime transport accounts for approximately 80% of global cargo volume. Ports, as areas with highly concentrated ocean-going vessel activities and emissions, have seen their ship emissions of greenhouse gases and air pollutants become key factors influencing global climate change and port air quality. Driven by both "dual carbon" goals and the construction of green ports, accurately identifying the mechanisms and driving factors of port vessel emissions changes has become a core technological requirement for the shipping industry's low-carbon transformation.

[0003] However, existing technologies have significant limitations. On the one hand, while methods based on extended STIRPAT models or system dynamics can identify the impact of multiple factors on carbon emissions, they are essentially statistical regressions or scenario simulations, unable to achieve precise, residual-free decomposition of the contributions of each factor, and struggling to quantify the exact emission reduction contribution of specific factors (such as energy efficiency improvements or policy interventions) over a specific time period. On the other hand, while the single LMDI (Log-Mean Dijkstra Index) factor decomposition method can achieve residual-free decomposition, it can only answer the question of "how much emission change each factor has driven," but cannot directly reveal the dynamic response relationship between emission changes and port economic activities (such as throughput), lacking a decoupling analysis perspective, and therefore unable to determine whether emission reduction achievements come at the expense of economic activity. Furthermore, while the single Tapio decoupling model can determine the decoupling status of emissions and economic growth, it cannot identify the internal driving mechanisms behind the decoupling, i.e., it cannot answer the crucial question of "whether energy efficiency improvements, energy structure optimization, or emission factor reductions have driven the decoupling." Existing studies that combine LMDI with decoupling models are merely simple parallel approaches, failing to establish a quantitative mapping between factor decomposition results and decoupling efforts. Furthermore, they cannot isolate the economic scale factor (i.e., the natural effect of throughput growth) to assess the "active" emission reduction contribution of non-economic scale factors. Simultaneously, all existing technologies fail to reveal the fundamental differences in the driving mechanisms of different emissions, such as greenhouse gases (GHG), nitrogen oxides (NOx), fine particulate matter (PM2.5), and sulfur oxides (SOx), making it difficult to formulate precise and coordinated emission reduction strategies in practice.

[0004] Therefore, the present invention provides a more reliable assessment scheme for port vessel emissions decoupling. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, and medium for assessing port vessel emissions decoupling, which addresses the problems in the prior art where emission factor decomposition and decoupling assessment are disconnected, it is difficult to quantitatively identify the contribution of non-economic factors to decoupling, and it is impossible to reveal the differentiated driving mechanisms of different emissions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for assessing the decoupling of port vessels, the method comprising: Obtain preprocessed basic data on ship and port operations; the basic data on ship and port operations includes at least ship emission data, ship activity data, and port cargo throughput data; The five-factor decomposition model based on LMDI additive decomposition was constructed and decomposed based on the ship port operation data to obtain detailed results of ship emission driving effects covering multiple dimensions. Based on the detailed results of the ship emission driving effect, the Tapio decoupling calculation operation is performed to obtain the target decoupling status determination result; Based on the target decoupling state determination result, the LMDI decomposition factor is back-embedded to quantify the decoupling effort, and the total decoupling effort index and the contribution ratio of each sub-factor are obtained after removing the scale effect. Based on the total decoupling effort index and the detailed results of the ship emission driving effect, a multi-dimensional coupling analysis was conducted to obtain the differentiated driving mechanisms and synergistic emission reduction strategies for different emissions.

[0007] Optionally, the multi-dimensional effects include at least the throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect, and emission factor effect; When the ship port operation basic data is ship emission data, the constructed five-factor decomposition model based on LMDI additive decomposition is used to perform decomposition operations on the ship port operation basic data to obtain detailed results of ship emission driving effects covering multi-dimensional effects, specifically including: The five-factor decomposition model based on LMDI additive decomposition was constructed to decompose the total change in port ocean-going vessel emissions between adjacent years into throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect and emission factor effect. Port ocean-going vessel emissions data are decomposed into a product of five factors, expressed as follows: ; Where E represents the total emissions from ocean-going vessels at the port, Q represents the port's cargo throughput, A represents the number of vessel arrivals, and W represents the total energy consumption of the vessel's engines. For the first Energy consumption of power-type equipment For the first Emissions from power equipment =1, 2, 3 represent the main engine, auxiliary engine, and boiler, respectively; T represents the ship activity level per unit throughput; and U represents the energy consumption level per unit of ship trips. Indicates the first The proportion of energy consumption of power equipment in total energy consumption, Indicates the first Emission intensity corresponding to unit energy consumption of power equipment.

[0008] Optionally, the additive decomposition of the total change in emissions between adjacent years can be expressed as follows: ; in, This represents the total change in emissions from ocean-going vessels at the port from year t-1 to year t. and These represent the port emissions from ocean-going vessels in years t and t-1, respectively. , , , and These are the throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect, and emission factor effect from year t-1 to year t.

[0009] Optionally, the step of performing Tapio decoupling calculation based on the detailed results of the ship emission driving effects to obtain the target decoupling status determination result specifically includes: Construct a Tapio decoupling model and calculate the decoupling elasticity coefficient; The decoupling status is determined based on the decoupling elasticity coefficient, the sign of the emission change, and the sign of the throughput change; the decoupling elasticity coefficient is the ratio of the emission change rate to the throughput change rate between adjacent years. The decoupling elasticity coefficient is expressed as: ; in, express Year Decoupling elasticity coefficient at the annual stage; and These are the port throughputs in year t and year t-1, respectively; This represents the change in throughput between adjacent years; Based on the sign of the change in emissions and the sign of the change in throughput, as well as the range of values ​​for the decoupling elasticity coefficient, the decoupling status is divided into three categories: decoupling, negative decoupling, and connection; each category includes at least eight states.

[0010] Optionally, based on the target decoupling state determination result, the LMDI decomposition factor is back-embedded to quantify the decoupling effort, obtaining the total decoupling effort index after removing the scale effect and the contribution ratio of each sub-factor, specifically including: A decoupling effort model is constructed. Based on the detailed results of the ship emission driving effects, the total decoupling effort is obtained by removing the throughput effect from the total emission change. For the two cases where the throughput effect is positive and negative, the total decoupling effort index and the sub-item decoupling effort index of each non-economic scale factor are determined respectively. The total decoupling effort index is expressed as: ; in, The total decoupling effort index is represented by the total decoupling effort index, which is then decomposed into... , , and The sum of , , and These are decoupling effort indices for transport intensity, ship energy efficiency, energy structure, and emission factors, respectively.

[0011] Optionally, before performing a multi-dimensional coupling analysis based on the total decoupling effort index and the detailed results of ship emission driving effects to obtain the differentiated driving mechanisms and synergistic emission reduction strategies for different emissions, the method further includes: A three-layer coupled analysis framework is constructed based on the detailed results of the ship emission driving effect, the results of the target decoupling status determination, the total decoupling effort index and the contribution ratio of each sub-factor. Based on the constructed three-layer coupling analysis framework, the differentiated driving mechanisms of different emissions are identified, and a coordinated emission reduction strategy is output. The three-layer coupling analysis framework includes an LMDI decomposition layer, a Tapio decoupling determination layer, and a decoupling effort quantification layer.

[0012] Optionally, the ship emission data shall include at least the emissions of four categories of pollutants: greenhouse gases, nitrogen oxides, particulate matter, and sulfur oxides. Based on a multi-dimensional coupling analysis of the total decoupling effort index and the detailed results of ship emission driving effects, the differentiated driving mechanisms and synergistic emission reduction strategies for different emissions are obtained, specifically including: For greenhouse gases and nitrogen oxides, when the emission reduction contribution mainly comes from the transport intensity effect and the ship energy efficiency effect, the output is an operation and management strategy mainly based on port transport organization optimization, ship deceleration plan implementation, shore power promotion and port turnover efficiency improvement. For fine particulate matter and sulfur oxides, when the emission reduction contribution mainly comes from the emission factor effect, the output should focus on technological upgrade strategies such as the implementation of low-sulfur fuel regulations, the deepening of emission control area policies, and the replacement of clean fuels.

[0013] Optionally, the throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect, and emission factor effect are all calculated using a logarithmic average weighting function, and are expressed as follows: ; ; ; ; ; in, This represents the emissions of Class i power equipment from ocean-going vessels in port in year t. This represents the emissions of Class i power equipment from ocean-going vessels in port in year t-1. The weighting function is a logarithmic average. When the decomposition result of any factor is positive, the corresponding factor promotes emission growth; when it is negative, the corresponding factor inhibits emission growth. This represents the level of ship activity corresponding to a unit throughput in year t. This represents the level of ship activity corresponding to a unit throughput in year t-1. This represents the energy consumption level per unit of ship trips in year t. This represents the energy consumption level per unit of ship trips in year t-1. Indicates the tth year, the first The proportion of energy consumption of power equipment in total energy consumption, Indicates the year t-1, the first digit of the t-1th year. The proportion of energy consumption of power equipment in total energy consumption, Indicates the tth year, the first Emission intensity per unit energy consumption of power equipment Indicates the year t-1, the first digit of the t-1th year. Emission intensity corresponding to unit energy consumption of power equipment.

[0014] Compared with existing technologies, this invention provides a port vessel emission decoupling assessment method. It acquires preprocessed basic data on vessel port operations; employs a constructed five-factor decomposition model based on LMDI additive decomposition, performing decomposition operations on the basic data to obtain detailed results of vessel emission driving effects covering multiple dimensions; performs Tapio decoupling calculations on the results to obtain the target decoupling status determination result; quantifies decoupling efforts by back-embedding LMDI decomposition factors based on the determination result, obtaining the total decoupling effort index after eliminating scale effects and the contribution ratio of each sub-factor; finally, performs multi-dimensional coupling analysis to obtain the differentiated driving mechanisms and collaborative emission reduction strategies for different emissions. More specifically, this invention, through the above technical solution, forms an end-to-end, interpretable decision support method from "data acquisition → factor decomposition → decoupling determination → contribution identification → strategy output," thus solving the problems existing in the prior art in a closed loop.

[0015] Furthermore, the use of LMDI additive decomposition technology allows emission changes to be decomposed into five effects without residuals, overcoming the limitations of traditional regression methods that cannot accurately quantify the contributions of each factor. The decomposition results strictly satisfy the factor identities, ensuring the scientific rigor and accuracy of the analysis. By introducing the Tapio decoupling model, dynamic monitoring of the relationship between emissions and economic activity response is achieved, enabling dynamic determination of eight decoupling states. This overcomes the deficiency of a single LMDI decomposition model in reflecting the relationship between emissions and economic activity response, and enables year-by-year dynamic tracking of the port's green development effectiveness. A decoupling effort model was established, quantitatively coupling the LMDI decomposition results with the decoupling analysis. For the first time, it achieves precise separation and quantitative assessment of the decoupling contributions of non-economic scale factors such as transport intensity and ship energy efficiency. For example, empirical analysis shows that when the supply chain crisis in 2021 led to port congestion, the decoupling effort index for GHG dropped to a negative value (no decoupling effort), while the decoupling effort index for PM2.5 remained positive (weak decoupling effort). This quantitative result directly reveals the differences in the robustness of different measures to external disturbances. By comparing and analyzing the driving mechanisms of GHG / NOx and PM2.5 / SOx, two emission reduction paths—"operation management driven" and "policy and technology driven"—can be clearly identified. This provides a scientific basis for port managers to formulate targeted and coordinated strategies of "operation optimization + technology upgrading," avoiding efficiency losses and resource waste caused by "one-size-fits-all" policies. The three-layer coupled analysis framework constructed in this invention—"factor decomposition → decoupling determination → contribution identification → strategy output"—forms an end-to-end closed-loop logic that can be directly embedded into the port emission management decision support system, enabling dynamic monitoring of emission status, accurate diagnosis of driving factors, and adaptive adjustment of governance policies.

[0016] Secondly, the present invention provides a port vessel emission decoupling assessment device. The device includes: The ship port operation basic data acquisition module is used to acquire preprocessed ship port operation basic data; the ship port operation basic data includes at least ship emission data, ship activity data, and port cargo throughput data. The LMDI additive decomposition module is used to perform decomposition operations based on the ship port operation basic data using the constructed five-factor decomposition model based on LMDI additive decomposition, to obtain detailed results of ship emission driving effects covering multiple dimensions. The target decoupling status determination module is used to perform Tapio decoupling calculation based on the detailed results of the ship emission driving effect to obtain the target decoupling status determination result. The decoupling effort quantification module is used to back-embed LMDI decomposition factors based on the target decoupling state determination result to quantify the decoupling effort, and obtain the total decoupling effort index and the contribution ratio of each sub-factor after removing the scale effect. The collaborative emission reduction strategy determination module is used to perform multi-dimensional coupling analysis based on the total decoupling effort index and the detailed results of the ship emission driving effect to obtain the differentiated driving mechanism and collaborative emission reduction strategy for different emissions.

[0017] Thirdly, the present invention provides a computer storage medium storing instructions, which, when executed by a processor, implement the aforementioned port vessel emission decoupling assessment method.

[0018] The technical effects achieved by the device-type solutions provided in the second aspect and the medium-type solutions provided in the third aspect are the same as those achieved by the method-type solutions provided in the first aspect, and will not be repeated here. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart of a port vessel emission decoupling assessment method provided by the present invention; Figure 2 This is a schematic diagram illustrating the cumulative decomposition of LMDI emissions from ocean-going vessels in a certain port year by year, as provided in the embodiments of the present invention. Figure 3 A schematic diagram illustrating the emission trends of ocean-going vessels in a certain port from 2006 to 2023 in an embodiment provided by the present invention; Figure 4 A schematic diagram showing the decoupling effort index and its decomposition results for four types of emissions from ocean-going vessels in a certain port from 2006 to 2023, provided in the embodiments of the present invention; Figure 5A schematic diagram of a port vessel emission decoupling assessment device provided by the present invention. Detailed Implementation

[0020] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0021] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or provision described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or provisions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0022] In this invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0023] The formation mechanisms of different emissions differ fundamentally: greenhouse gas (GHG) and nitrogen oxide (NOx) emissions are mainly related to combustion processes and total energy consumption, while fine particulate matter (PM2.5) and sulfur oxide (SOx) emissions are highly correlated with fuel quality (especially sulfur content). These differences necessitate differentiated control strategies for different emissions. More specifically, existing technologies suffer from shortcomings in the following three areas: First, while methods based on extended STIRPAT models or system dynamics can identify the degree of influence of multiple factors on carbon emissions, they are essentially statistical regressions or scenario simulations. They cannot achieve accurate, residual-free decomposition of the contribution of each factor, and it is difficult to quantify the exact emission reduction contribution of specific factors (such as energy efficiency improvements or policy interventions) in a specific time period.

[0024] Second, while the single LMDI (Log-mean Dijkstra index) factor decomposition method can achieve residual-free decomposition, it can only answer the question of "how much emission change each factor has driven", but cannot directly reveal the dynamic response relationship between emission changes and port economic activities (such as throughput). It lacks a decoupling analysis perspective and therefore cannot determine whether the emission reduction effect comes at the expense of economic activities.

[0025] Third, while the Tapio decoupling model alone can determine the decoupling status between emissions and economic growth, it cannot identify the internal driving mechanisms behind this decoupling, i.e., it cannot answer the crucial question of whether energy efficiency improvements, energy structure optimization, or emission factor reductions drove the decoupling. Existing studies that combine LMDI with decoupling models merely juxtapose the two methods; they fail to establish a quantitative mapping between factor decomposition results and decoupling efforts, and they cannot separate economic scale factors (i.e., the natural effect of throughput growth) to assess the "active" emission reduction contribution of non-economic scale factors. Furthermore, all existing technologies fail to reveal the fundamental differences in the driving mechanisms of different emissions (such as GHG and PM2.5), making it difficult to formulate precise and coordinated emission reduction strategies in practice.

[0026] Therefore, based on the aforementioned deficiencies, this invention needs to construct a closed-loop analysis framework integrating precise factor decomposition, dynamic decoupling determination, and quantitative identification of decoupling contributions, and on this basis, reveal the differentiated driving mechanisms of different emissions. Specifically, this invention organically integrates factor decomposition, decoupling status determination, and decoupling contribution identification of port ocean-going vessel emission changes by constructing a three-layer coupled analysis framework of LMDI-Tapio-decoupling efforts. Specifically, firstly, LMDI additive decomposition is used to decompose emission changes into five effects without residuals: throughput, transport intensity, ship energy efficiency, energy structure, and emission factors; secondly, the Tapio decoupling model is used to dynamically assess the decoupling status between emissions and port throughput; finally, the LMDI decomposition results are introduced into the decoupling analysis, and by constructing a decoupling effort model, after quantitatively separating the economic scale factor (throughput effect), the actual contribution of each non-economic scale factor to decoupling is accurately identified and quantified, thus forming an end-to-end, interpretable decision support scheme from "data collection → factor decomposition → decoupling determination → contribution identification → strategy output". Next, the scheme provided by the embodiments of this specification will be described with reference to the accompanying drawings: like Figure 1 As shown, the process may include the following steps: Step 110: Obtain preprocessed basic data on ship port operations.

[0027] Before this step, it is necessary to collect port emission data, vessel activity data, and port cargo throughput data, and then perform standardization processing to obtain pre-processed basic data on vessel and port operations. This basic data includes at least vessel emission data, vessel activity data, and port cargo throughput data.

[0028] More specifically, the collected data includes: annual emissions data of ocean-going vessels at the port, annual vessel arrival data, energy consumption data of main engines / auxiliary engines / boilers, and port cargo throughput data; among which, the emissions data includes emissions of at least four categories of pollutants: greenhouse gases (GHG), nitrogen oxides (NOx), fine particulate matter (PM2.5), and sulfur oxides (SOx). Data sources include annual port emissions inventory reports, Automatic Identification System (AIS) data, and port cargo throughput statistics.

[0029] Next, the collected data is standardized by unifying units and aligning definitions to ensure consistency in time scale and statistical scope across multiple data sources.

[0030] Step 120: Using the constructed five-factor decomposition model based on LMDI additive decomposition, decomposition operations are performed based on the ship port operation data to obtain detailed results of ship emission driving effects covering multiple dimensions.

[0031] This step belongs to the LMDI five-factor decomposition step. It constructs a five-factor decomposition model based on LMDI additive decomposition, which decomposes the total change in port ocean-going vessel emissions between adjacent years into throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect and emission factor effect.

[0032] Step 130: Perform Tapio decoupling calculation based on the detailed results of the ship emission driving effect to obtain the target decoupling status determination result.

[0033] In its implementation, this step requires first constructing a Tapio decoupling model to calculate the ratio of emission change rate to throughput change rate between adjacent years, thus obtaining the decoupling elasticity coefficient. Based on the positive / negative relationship between the decoupling elasticity coefficient, emission change, and throughput change, one of eight decoupling states is determined. Specifically, the decoupling states are divided into three main categories: decoupling, negative decoupling, and connection, totaling eight states: when <0 and A value greater than 0 indicates a strong unhooking. when >0、 >0 and 0≤ A value less than 0.8 indicates weak uncoupling; when <0、 <0 and A value greater than 1.2 indicates a recessionary decoupling. when >0 and <0 indicates a strong negative decoupling; when <0、 <0 and 0≤ A value less than 0.8 indicates a weak negative decoupling. when >0、 >0 and A value greater than 1.2 indicates expansionary negative decoupling; when >0、 >0 and 0.8≤ When <1.2, it is an extended connection; when <0、 <0 and 0.8≤ A value less than 1.2 indicates a fading connection.

[0034] Step 140: Based on the target decoupling state determination result, the LMDI decomposition factor is back-embedded to quantify the decoupling effort, and the total decoupling effort index and the contribution ratio of each sub-factor after removing the scale effect are obtained.

[0035] This step may include constructing a decoupling effort model, introducing the detailed results of the ship emission driving effects obtained from the 120LMDI decomposition in step 1 into the Tapio decoupling analysis framework in step 3, removing the throughput effect from the total emission change to obtain the total decoupling effort, and defining the total decoupling effort index and the decoupling effort index of each component for the two cases where the throughput effect is positive and negative, respectively.

[0036] Step 150: Perform a multi-dimensional coupling analysis based on the total decoupling effort index and the detailed results of the ship emission driving effect to obtain the differentiated driving mechanism and synergistic emission reduction strategy for different emissions.

[0037] Step 150 is the differentiated strategy output step. Based on the LMDI-Tapio-decoupling effort three-layer coupling analysis framework consisting of steps 120, 130 and 140, it identifies the differentiated driving mechanisms of different emissions (such as GHG / NOx and PM2.5 / SOx) and outputs corresponding synergistic emission reduction strategies.

[0038] Specifically, the working logic of the LMDI-Tapio-decoupling effort three-layer coupling analysis framework is as follows: (1) The first layer, the LMDI decomposition layer, is used to quantitatively identify the direction and extent of the contribution of each driving factor to emission changes; (2) The second layer, Tapio decoupling determination layer, is used to dynamically determine the decoupling status between emissions and port economic activities; (3) The third layer, the decoupling effort quantification layer, is used to map the LMDI decomposition results of the first layer to the decoupling analysis framework of the second layer, quantitatively stripping the contribution of economic scale factors (throughput effect) to identify the actual contribution of non-economic scale factors (transportation intensity, ship energy efficiency, energy structure, emission factors) to the formation of decoupling status, and realize the end-to-end closed-loop analysis of factor decomposition and decoupling assessment.

[0039] Figure 1 The method described herein involves acquiring preprocessed basic data on ship and port operations; employing a five-factor decomposition model based on LMDI additive decomposition, performing decomposition operations on the ship and port operation data to obtain detailed results of ship emission driving effects covering multiple dimensions; performing Tapio decoupling calculations on the results to obtain the target decoupling status determination results; quantifying decoupling efforts by back-embedding LMDI decomposition factors based on the determination results to obtain the total decoupling effort index after eliminating scale effects and the contribution ratio of each sub-factor; and finally, multi-dimensional coupling analysis to obtain the differentiated driving mechanisms and synergistic emission reduction strategies for different emissions. This invention can effectively identify the differentiated driving mechanisms between greenhouse gases / nitrogen oxides and fine particulate matter / sulfur oxides, providing accurate and interpretable decision support for ports to formulate synergistic emission reduction strategies of "operational optimization + technological upgrading".

[0040] based on Figure 1 In addition to the method described herein, this specification also provides some specific implementation methods of this method, which will be described below.

[0041] In step 120, the construction of the five-factor decomposition model includes: expressing the total emissions E of ocean-going vessels in the port as the product of five factors, and establishing factor identities, as shown in formula (1): (1); in, Transport intensity reflects the level of ship activity driven by a unit throughput. Ship energy efficiency reflects the average energy consumption of a single ship. The energy structure reflects the energy consumption proportion of various power equipment such as main engines, auxiliary engines, and boilers; The emission factor reflects the emission intensity per unit of energy consumption. A logarithmic average weighting function is used to achieve residual-free additive decomposition. The calculation formulas for each effect are shown in formulas (2)-(6): Throughput effect: (2); Transport intensity effect: (3); Ship energy efficiency effect: (4); Energy structure effects: (5); Emission factor effect: (6); in, This represents the emissions of Class i power equipment from ocean-going vessels in port in year t. This represents the emissions of Class i power equipment from ocean-going vessels in port in year t-1. The weighting function is a logarithmic average. When the decomposition result of any factor is positive, the corresponding factor promotes emission growth; when it is negative, the corresponding factor inhibits emission growth. This represents the level of ship activity corresponding to a unit throughput in year t. This represents the level of ship activity corresponding to a unit throughput in year t-1. This represents the energy consumption level per unit of ship trips in year t. This represents the energy consumption level per unit of ship trips in year t-1. Indicates the tth year, the first The proportion of energy consumption of power equipment in total energy consumption, Indicates the year t-1, the first digit of the t-1th year. The proportion of energy consumption of power equipment in total energy consumption, Indicates the tth year, the first Emission intensity per unit energy consumption of power equipment Indicates the year t-1, the first digit of the t-1th year. Emission intensity corresponding to unit energy consumption of power equipment.

[0042] Step 130 specifically includes: Construct a Tapio decoupling model and calculate the decoupling elasticity coefficient; The decoupling status is determined based on the decoupling elasticity coefficient, the sign of the emission change, and the sign of the throughput change; the decoupling elasticity coefficient is the ratio of the emission change rate to the throughput change rate between adjacent years.

[0043] Specifically, the decoupling elasticity coefficient is defined. Its calculation formula is as shown in formula (7): (7); according to , Positive and negative and The range of values ​​for the value is used to establish eight criteria for determining decoupling states, as shown in Table 1. This model can dynamically capture the response elasticity of emissions to changes in port economic activities and effectively distinguish between different development models such as "strong decoupling" (ideal emission reduction) and "expansionary negative decoupling" (environmental degradation).

[0044] Table 1. Criteria for Decoupling Port Ocean-going Vessel Emissions and Port Throughput from Tapio Furthermore, in step 140, the construction of the decoupling effort model specifically involves the throughput effect. It is considered an economic scale driver (whose changes reflect passive emissions changes resulting from economic expansion or contraction), while the transport intensity effect is considered... Ship energy efficiency Energy structure effect and emission factor effects Considered a non-economic scale factor, this represents proactive emissions reduction efforts. First, define the total decoupling effort. The change in emissions after removing the effects of economies of scale: .

[0045] Secondly, in order to accurately assess the active emission reduction contribution of non-economic scale factors, this invention defines a decoupling effort index for both positive and negative cases of throughput effect. : Scenario 1: When throughput increases, i.e. When >0, economic factors drive the increase in emissions. At this point, the decoupling effort index is expressed as formula (8): (8); This index measures the extent to which proactive emissions reduction efforts offset the emissions pressures from economic activity.

[0046] Scenario 2: When throughput decreases, i.e. When the threshold is <0, the contraction of economic activity itself has already led to passive emission reductions. In this case, to accurately assess "active" emission reduction efforts, this passive effect must be subtracted from the total decoupling efforts. The decoupling effort index is then expressed as formula (9): (9); When proactive emissions reduction efforts continue to be effective It can still be a positive value.

[0047] Finally, based on the judgment criteria shown in Table 2, the contribution strength of each active emission reduction factor is as follows: when When the value is ≥1, it is judged as "strong decoupling effort", indicating that the emission reduction contribution of non-economic scale factors is sufficient to achieve an absolute reduction in emissions; When 0 < When the value is less than 1, it is judged as "weak decoupling efforts," indicating that non-economic scale factors have played a positive but insufficient role in inhibiting emission reduction. when When the value is ≤0, it is judged as "no decoupling effort", indicating that non-economic scale factors have failed to play a positive role in emission reduction, and may even have exacerbated emission growth.

[0048] Table 2. Criteria for Decoupling Effort Index Furthermore, in step 150, the identification of differentiated driving mechanisms and the output of strategies include: based on the empirical analysis of data from a certain port from 2006 to 2023 using the aforementioned three-layer coupling framework, this invention can automatically identify and output two typical driving modes: (1) For GHG and NOx, emission reduction is mainly driven by the transport intensity effect and the ship energy efficiency effect, that is, the emission reduction effect is highly dependent on the port operation efficiency (such as ship turnaround time and deceleration plan execution rate). When encountering external shocks (such as port congestion caused by the supply chain crisis in 2021), the decoupling effort index will drop sharply (or even turn negative).

[0049] (2) For PM2.5 and SOx, their emission reduction is mainly dominated by the emission factor effect, that is, the emission reduction effect is mainly attributed to mandatory technical standards such as low sulfur fuel regulations, and is less affected by short-term fluctuations in port operating efficiency. Their decoupling effort index is relatively stable.

[0050] Therefore, this invention can intelligently output differentiated strategies: for the former, it is recommended to adopt an operational management strategy; for the latter, it is recommended to adopt a technology upgrade and policy implementation strategy, thereby avoiding inefficient "one-size-fits-all" management.

[0051] Next, to further explain the solution of the present invention in detail, specific application embodiments will be used for illustration: Taking the emissions data of ocean-going vessels at a certain port from 2006 to 2023 as an example, this embodiment collects ocean-going vessels at the port from 2006 to 2023 as the analysis object.

[0052] (1) Data collection: The collected data includes: (a) Annual emissions data for ocean-going vessels: greenhouse gases (GHG), nitrogen oxides (NOx) x ), fine particulate matter (PM) 2.5 ) and sulfur oxides (SO x Annual emissions; (b) Ship activity and energy consumption data: number of ships arriving in port annually, main engine energy consumption, auxiliary engine energy consumption and boiler energy consumption; (c) Port economic activity data: Port cargo throughput (covering ocean-going import and export and coastal transport cargo). The above emissions and energy consumption data are from the annual emissions inventory reports published by a certain port over the years. To ensure data consistency and comparability, the data for each year have undergone standardization processing, such as unit unification (e.g., converting all emissions to tons) and caliber alignment.

[0053] (2) LMDI five-factor decomposition calculation: The constructed five-factor decomposition model based on LMDI additive decomposition expresses total emissions as the product of throughput, transport intensity, ship energy efficiency, energy structure, and emission factors. Subsequently, a logarithmic average weighting function is used to calculate the annual effects of each factor; such as... Figure 2 As shown, Figure 2 Showcasing GHG, NO x PM 2.5 and SO x The cumulative decomposition results of LMDI over the years, among which, Figure 2 In the table, (a) represents the cumulative decomposition results of GHG, (b) represents the cumulative decomposition results of NOx, (c) represents the cumulative decomposition results of PM2.5, and (d) represents the cumulative decomposition results of SOx.

[0054] by Figure 3 Summary of PM 2.5 For example, Figure 3 In the diagram, (a) represents the trend of GHG emissions, (b) represents the trend of NOx emissions, (c) represents the trend of PM2.5 emissions, and (d) represents the trend of SOx emissions. From 2006 to 2023, the cumulative emission reduction was 522.4 tons. The decomposition results show that the emission factor effect contributed 313.6 tons, accounting for 60.0% of the total emission reduction, while the other four effects combined contributed the remaining 40.0%. This result clearly reveals the PM2.5 emission reduction trend. 2.5 The main driver of emission reduction is the improvement of emission factors.

[0055] (3) Tapio unhooking status determination: Calculate the decoupling elasticity coefficient year by year The decoupling status for each year was determined according to the criteria in Table 1. Table 3 shows the GHG and NO levels from 2006 to 2023. x PM 2.5 and SO x The evolution of the decoupling status of the four types of emissions year by year. It is evident that the decoupling status exhibited significant phased characteristics during the study period: 2006-2009: Affected by the global financial crisis, throughput declined, and the four types of emissions mostly showed "decoupling from recession", that is, the reduction in emissions was greater than the reduction in throughput; 2015–2018: With the full implementation of North American Emission Control Area policies, four categories of emissions frequently experienced a “strong decoupling” state, meaning that while throughput increased, emissions decreased in absolute terms. 2020-2021: Affected by the post-pandemic supply chain crisis and port congestion, all emissions showed "expansionary negative decoupling" (δ>>1.2), meaning that the growth rate of emissions far exceeded the growth rate of throughput, and environmental pressure deteriorated sharply.

[0056] Table 3. Decoupling Elasticity Coefficient and Decoupling Status of Ocean-going Vessel Emissions and Port Throughput in a Certain Port, 2006-2023 (4) Calculation of the decoupling effort index: Calculate total decoupling effort And calculate the total decoupling effort index according to different situations. And four sub-indices. For example... Figure 4 As shown, Figure 4 The decomposition results of the decoupling effort index are presented. Figure 4 In the table, (a) represents the decomposition result of the GHG decoupling effort index, (b) represents the decomposition result of the NOx decoupling effort index, (c) represents the decomposition result of the PM2.5 decoupling effort index, and (d) represents the decomposition result of the SOx decoupling effort index. The analysis shows that: For PM2.5 and SOx, the total decoupling effort index was positive in most years during the study period, and frequently reached a state of "strong decoupling effort" after 2015. ≥1); where, emission factor decoupling effort index The fact that it made the largest contribution indicates that the low-sulfur fuel policy is the core driving force behind its decoupling.

[0057] For GHG and NOx, the total decoupling effort index fluctuated dramatically; especially in 2020-2021, due to port congestion leading to longer ship anchoring waiting times, energy consumption per vessel increased (ship energy efficiency effect). The total decoupling effort index plummeted to a negative value after turning positive. <0), indicating a state of "no decoupling efforts"; this suggests that despite other emission reduction measures, the decline in operational efficiency has completely offset their positive effects.

[0058] (5) Results analysis and strategy output: Based on the above analysis, this invention automatically identifies two differentiated driving mechanisms based on the coupling framework, as shown in Table 4.

[0059] Table 4. Decoupling Elasticity Coefficient and Decoupling Status of Ocean-going Vessel Emissions and Port Throughput in a Certain Port, 2006-2023 Based on the above identification results, this invention outputs a collaborative emission reduction strategy recommendation: Given the vulnerability of GHG / NOx emission reduction, while continuing to promote operational management measures such as ship deceleration and shore power use, it is essential to include improving the overall port operational efficiency (such as optimizing berth allocation and reducing anchorage waiting time) in the core agenda to enhance the robustness of GHG / NOx emission reduction measures against external shocks.

[0060] Regarding PM2.5 and SOx, fuel quality regulation should be strengthened, and a long-term transition to zero-carbon fuels (such as methanol and ammonia) should be planned. Empirical evidence shows that in the 2021 port congestion incident, if a port had used the assessment method of this invention beforehand, it could have foreseen the significant impact of declining operational efficiency on GHG / NOx decoupling efforts and taken more targeted congestion mitigation and emission compensation measures.

[0061] Based on the foregoing implementation scheme, the technical effects that the technical solution provided by the present invention can achieve include at least: (1) Accurate decomposition capability: The LMDI additive decomposition method is used to decompose the emission changes into five effects without residuals, which overcomes the limitation of traditional regression methods that cannot accurately quantify the contribution of each factor. The decomposition results strictly satisfy the factor identity, ensuring the scientificity and accuracy of the analysis.

[0062] (2) Dynamic decoupling assessment capability: The Tapio decoupling model is introduced, and eight decoupling states are dynamically determined by the decoupling elasticity coefficient. This overcomes the deficiency that the single LMDI decomposition cannot reflect the relationship between emissions and economic activity response, and realizes the year-by-year dynamic tracking of the port's green development achievements.

[0063] (3) First quantitative identification of the decoupling contribution of non-economic factors: A decoupling effort model was established, and the LMDI decomposition results were quantitatively coupled with the decoupling analysis. For the first time, the decoupling contribution of non-economic scale factors such as transport intensity and ship energy efficiency was accurately separated and quantitatively evaluated. For example, empirical analysis shows that when the supply chain crisis in 2021 caused port congestion, the decoupling effort index of GHG dropped to a negative value (no decoupling effort), while the decoupling effort index of PM2.5 remained positive (weak decoupling effort). This quantitative result directly reveals the difference in the robustness of different measures to external disturbances.

[0064] (4) Supporting differentiated and coordinated emission reduction strategies: By comparing and analyzing the driving mechanisms of GHG / NOx and PM2.5 / SOx, this invention can clearly identify two emission reduction paths: "operation management driven" and "policy and technology driven". This provides a scientific basis for port managers to formulate targeted coordinated strategies of "operation optimization + technology upgrading", avoiding efficiency losses and resource waste caused by "one-size-fits-all" policies.

[0065] (5) Forming a closed-loop decision support: The three-layer coupled analysis framework of “factor decomposition → decoupling judgment → contribution identification → strategy output” constructed by this invention forms an end-to-end closed-loop logic, which can be directly embedded into the port emission management decision support system to realize dynamic monitoring of emission status, accurate diagnosis of driving factors and adaptive adjustment of governance policies.

[0066] Based on the same idea, the present invention also provides a port vessel emission decoupling assessment device, such as... Figure 5 As shown, the device may include: The ship port operation basic data acquisition module 510 is used to acquire preprocessed ship port operation basic data; the ship port operation basic data includes at least ship emission data, ship activity data and port cargo throughput data. The LMDI additive decomposition module 520 is used to perform decomposition operations based on the ship port operation basic data using the constructed five-factor decomposition model based on LMDI additive decomposition, to obtain detailed results of ship emission driving effects covering multi-dimensional effects. The target decoupling status determination module 530 is used to perform Tapio decoupling calculation operation based on the detailed results of the ship emission driving effect to obtain the target decoupling status determination result. The decoupling effort quantification module 540 is used to back-embed LMDI decomposition factors based on the target decoupling state determination result to quantify decoupling effort, and obtain the total decoupling effort index and the contribution ratio of each sub-factor after removing the scale effect. The coordinated emission reduction strategy determination module 550 is used to perform multi-dimensional coupling analysis based on the total decoupling effort index and the detailed results of the ship emission driving effect to obtain the differentiated driving mechanism and coordinated emission reduction strategy for different emissions.

[0067] Figure 5 In specific implementation, the implementation scheme of the device is as described in the aforementioned method embodiments, and will not be repeated here.

[0068] The present invention also discloses a computer storage medium, characterized in that the computer storage medium stores instructions, which, when executed by a processor, implement the port vessel emission decoupling assessment method executed by the processor in the above embodiments.

[0069] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0070] The embodiments of the present invention can divide functional modules according to the above method examples. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the embodiments of the present invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0071] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely exemplary descriptions of the invention as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for assessing the decoupling of port vessels, characterized in that, The methods include: Obtain preprocessed basic data on ship and port operations; the basic data on ship and port operations includes at least ship emission data, ship activity data, and port cargo throughput data; The five-factor decomposition model based on LMDI additive decomposition was constructed and decomposed based on the ship port operation data to obtain detailed results of ship emission driving effects covering multiple dimensions. Based on the detailed results of the ship emission driving effect, the Tapio decoupling calculation operation is performed to obtain the target decoupling status determination result; Based on the target decoupling state determination result, the LMDI decomposition factor is back-embedded to quantify the decoupling effort, and the total decoupling effort index and the contribution ratio of each sub-factor are obtained after removing the scale effect. Based on the total decoupling effort index and the detailed results of the ship emission driving effect, a multi-dimensional coupling analysis was conducted to obtain the differentiated driving mechanisms and synergistic emission reduction strategies for different emissions.

2. The port vessel discharge decoupling assessment method according to claim 1, characterized in that, The multidimensional effects include at least the throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect, and emission factor effect; When the ship port operation basic data is ship emission data, the constructed five-factor decomposition model based on LMDI additive decomposition is used to perform decomposition operations on the ship port operation basic data to obtain detailed results of ship emission driving effects covering multi-dimensional effects, specifically including: The five-factor decomposition model based on LMDI additive decomposition was constructed to decompose the total change in port ocean-going vessel emissions between adjacent years into throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect and emission factor effect. Port ocean-going vessel emissions data are decomposed into a product of five factors, expressed as follows: ； Where E represents the total emissions from ocean-going vessels at the port, Q represents the port's cargo throughput, A represents the number of vessel arrivals, and W represents the total energy consumption of the vessel's engines. For the first Energy consumption of power-type equipment For the first Emissions from power equipment =1, 2, 3 represent the main engine, auxiliary engine, and boiler, respectively; T represents the ship activity level per unit throughput; and U represents the energy consumption level per unit of ship trips. Indicates the first The proportion of energy consumption of power equipment in total energy consumption, Indicates the first Emission intensity corresponding to unit energy consumption of power equipment.

3. The port vessel discharge decoupling assessment method according to claim 2, characterized in that, The additive decomposition of the total change in emissions between adjacent years is expressed as follows: ； in, This represents the total change in emissions from ocean-going vessels at the port from year t-1 to year t. and These represent the port emissions from ocean-going vessels in years t and t-1, respectively. , , , and These are the throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect, and emission factor effect from year t-1 to year t.

4. The port vessel discharge decoupling assessment method according to claim 3, characterized in that, The Tapio decoupling calculation operation, performed based on the detailed results of the ship emission-driven effects, yields the target decoupling status determination result, specifically including: Construct a Tapio decoupling model and calculate the decoupling elasticity coefficient; The decoupling status is determined based on the decoupling elasticity coefficient, the sign of the emission change, and the sign of the throughput change; the decoupling elasticity coefficient is the ratio of the emission change rate to the throughput change rate between adjacent years. The decoupling elasticity coefficient is expressed as: ； in, express Year Decoupling elasticity coefficient at the annual stage; and These are the port throughputs in year t and year t-1, respectively; This represents the change in throughput between adjacent years; Based on the sign of the change in emissions and the sign of the change in throughput, as well as the range of values ​​for the decoupling elasticity coefficient, the decoupling status is divided into three categories: decoupling, negative decoupling, and connection; each category includes at least eight states.

5. The port vessel discharge decoupling assessment method according to claim 3, characterized in that, Based on the target decoupling state determination result, the LMDI decomposition factor is back-embedded to quantify the decoupling effort, resulting in the total decoupling effort index after removing the scale effect and the contribution ratio of each sub-factor, specifically including: A decoupling effort model is constructed. Based on the detailed results of the ship emission driving effects, the total decoupling effort is obtained by removing the throughput effect from the total emission change. For the two cases where the throughput effect is positive and negative, the total decoupling effort index and the sub-item decoupling effort index of each non-economic scale factor are determined respectively. The total decoupling effort index is expressed as: ； in, The total decoupling effort index is represented by [the index], which is then decomposed into [the following components]. , , and the sum of , , and These are decoupling effort indices for transport intensity, ship energy efficiency, energy structure, and emission factors, respectively.

6. The port vessel discharge decoupling assessment method according to claim 1, characterized in that, Before conducting a multi-dimensional coupling analysis based on the total decoupling effort index and the detailed results of ship emission driving effects to obtain the differentiated driving mechanisms and synergistic emission reduction strategies for different emissions, the analysis also includes: A three-layer coupled analysis framework is constructed based on the detailed results of the ship emission driving effect, the results of the target decoupling status determination, the total decoupling effort index and the contribution ratio of each sub-factor. Based on the constructed three-layer coupling analysis framework, the differentiated driving mechanisms of different emissions are identified, and a coordinated emission reduction strategy is output. The three-layer coupling analysis framework includes an LMDI decomposition layer, a Tapio decoupling determination layer, and a decoupling effort quantification layer.

7. The port vessel discharge decoupling assessment method according to claim 1, characterized in that, The ship emission data shall include at least the emissions of four categories of pollutants: greenhouse gases, nitrogen oxides, particulate matter, and sulfur oxides. Based on a multi-dimensional coupling analysis of the total decoupling effort index and the detailed results of ship emission driving effects, the differentiated driving mechanisms and synergistic emission reduction strategies for different emissions are obtained, specifically including: For greenhouse gases and nitrogen oxides, when the emission reduction contribution mainly comes from the transport intensity effect and the ship energy efficiency effect, the output is an operation and management strategy mainly based on port transport organization optimization, ship deceleration plan implementation, shore power promotion and port turnover efficiency improvement. For fine particulate matter and sulfur oxides, when the emission reduction contribution mainly comes from the emission factor effect, the output should focus on technological upgrade strategies such as the implementation of low-sulfur fuel regulations, the deepening of emission control area policies, and the replacement of clean fuels.

8. The port vessel discharge decoupling assessment method according to claim 4, characterized in that, The throughput effect, transport intensity effect, ship energy efficiency effect, energy structure effect, and emission factor effect are all calculated using a logarithmic average weighting function, and are expressed as follows: ； ； ； ； ； in, This represents the emissions of Class i power equipment from ocean-going vessels in port in year t. This represents the emissions of Class i power equipment from ocean-going vessels in port in year t-1. The weighting function is a logarithmic average. When the decomposition result of any factor is positive, the corresponding factor promotes emission growth; when it is negative, the corresponding factor inhibits emission growth. This represents the level of ship activity corresponding to a unit throughput in year t. This represents the level of ship activity corresponding to a unit throughput in year t-1. This represents the energy consumption level per unit of ship trips in year t. This represents the energy consumption level per unit of ship trips in year t-1. Indicates the tth year, the first The proportion of energy consumption of power equipment in total energy consumption, Indicates the (t-1)th year The proportion of energy consumption of power equipment in total energy consumption, Indicates the tth year, the first Emission intensity per unit energy consumption of power equipment Indicates the (t-1)th year Emission intensity corresponding to unit energy consumption of power equipment.

9. A port vessel discharge decoupling assessment device, characterized in that, The device includes: The ship port operation basic data acquisition module is used to acquire preprocessed ship port operation basic data; The basic data on ship port operations includes at least ship emission data, ship activity data, and port cargo throughput data. The LMDI additive decomposition module is used to perform decomposition operations based on the ship port operation basic data using the constructed five-factor decomposition model based on LMDI additive decomposition, to obtain detailed results of ship emission driving effects covering multiple dimensions. The target decoupling status determination module is used to perform Tapio decoupling calculation based on the detailed results of the ship emission driving effect to obtain the target decoupling status determination result. The decoupling effort quantification module is used to back-embed LMDI decomposition factors based on the target decoupling state determination result to quantify the decoupling effort, and obtain the total decoupling effort index and the contribution ratio of each sub-factor after removing the scale effect. The coordinated emission reduction strategy determination module is used to perform multi-dimensional coupling analysis based on the total decoupling effort index and the detailed results of the ship emission driving effect to obtain the differentiated driving mechanism and coordinated emission reduction strategy for different emissions.

10. A computer storage medium, characterized in that, The computer storage medium stores instructions that, when executed by the processor, implement the port vessel emission decoupling assessment method according to any one of claims 1 to 8.

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