蒋文韬
高速公路是现代交通运输的重要组成部分，也是城市化进程中不可或缺的基础设施。庞大的公路运输体量带来的是巨大的碳排放量，从而造成环境负担。然而，当前的多数研究是集中于城市交通运输碳排放或公路建设碳排放，较少聚焦于高速公路运输系统造成的碳排放。基于此，本文以研究高速公路交通运行的碳排放评估技术作为切入点，建立高速公路交通运行碳排放评价体系，帮助管理者识别高速公路交通运行碳排放影响因素的效应，为减少碳排放策略提供决策意见和实际评价应用方案。本文尝试建立高速公路交通运行碳排放评价指标体系，分为碳排放水平评价和碳减排潜力评价。碳排放水平评价方面，主要集中于碳排放量的测算，在现有的交通碳排放测算的方法基础上，运用本地化后的 MOVES 模型计算排放因子，对高速公路交通运行碳排放进行测算。碳减排潜力评价方面，本文尝试应用影响因素分解模型和 DEA 分析法，分析高速公路交通运行的碳排放的各个影响因素和碳排放效率。最后以G2 京沪高速山东段为例进行案例分析。
本文首先从交通碳排放关键指标、交通碳排放影响因素以及交通碳排放效率三个方面梳理了国内外研究现状。对高速公路交通运行的碳排放主体、交通碳排放评估方法，指标集构建方法，交通碳排放测算方法，交通排放模型以及影响因素分解模型等理论基础以及相关的研究方法进行了综述。
在明确了高速公路交通运行碳排放评估指标构建原则和指标筛选流程之后，根据驱动因素和响应目标，将指标分为碳排放水平评价以及碳减排潜力评价。在碳排放水平评估层面，针对车辆类型和行驶距离因素，利用 G2 京沪高速公路山东段的相关数据进行了相关性分析，分析结果表明它们与碳排放水平具有高度相关性，确认为碳排放水平评价的关键变量。在碳减排潜力评价层面，依次建立目标层、准则层和指标层，根据前述指标构建原则和筛选流程进行筛选，结合宏观经济、交通结构和能源消耗三个驱动因素，预选了 10 个变量，并进行相关性分析，为了使变量更好地反映准则层所提出的经济、交通发展、环境和能源消耗四个维度，对这些变量进行 KMO 独立性检验，并结合实际可操作性和数据获取能力，明确了各关键指标的意义以及量化方法。
在碳排放水平评估层面，基于之前对碳排放测算的几种方法的讨论，选择模型法确定排放因子的取值，并选择 MOVES 模型进行碳排放测算，在对 MOVES模型参数进行本地化和敏感性分析的基础上，选择采用基于速度的排放因子模拟方法。在碳排放潜力评价层面，通过影响因素进行分解和运用 LMDI 模型，并借鉴STIRPAT 模型拓展指标的方法优化了 LMDI 模型。通过将碳减排潜力的影响因素分解成六种效应，并对各效应进行运算后，分析各效应对碳排放的促进或抑制作用，根据各效应的作用，提出有效的减碳策略建议。运用 DEA 方法评估碳排放效率，帮助识别交通资源配置是否达到最优状态，以满足低碳交通转型的需求。
最后，利用前述研究成果，以 G2 京沪高速山东段为例进行验证。首先，将MOVES 模型的参数进行本地化修正，使其更接近 G2 京沪高速山东段的真实情况。根据模拟的排放因子结果计算了 G2 京沪高速公路山东段的碳排放量，并根据建立的评价指标体系，收集数据并进行相关分析。结果显示，碳强度效应、产业结构效应、交通结构效应、交通强度效应以及能耗效应对碳排放影响的贡献率分别为123.2%、34.1%、-43.0% 、88.8%以及-103.1%。结果表明：就碳减排潜力而言，调整交通结构，促进高速公路交通运力结构的调整，改善能源使用效率对碳排放有明显抑制作用。高速公路交通运行碳排放的效率取决于规模效益的大小。因此，减碳策略可以着重优化交通运输结构。增加对能源技术开发推广的投入，扩大其技术规模，从而促进新能源的使用，提升能源效率。
关键词：高速公路，交通运行，碳排放测算，碳排放影响因素，碳排放效率，减碳策略
Freeway is an important component of modern transportation and an indispensable infrastructure in the process of urbanization. The huge volume of road transportation brings about huge carbon emissions, thereby causing an environmental burden. However, most current research focuses on carbon emissions from urban transportation or freeway construction, with less emphasis on carbon emissions caused by freeway transportation systems. Based on this, this article takes the study of carbon emission assessment technology for freeway traffic operation as the starting point, establishes a carbon emission assessment system for freeway traffic operation, helps managers identify the effects of factors affecting carbon emissions in freeway traffic operation, and provides decision-making opinions and practical evaluation application solutions for reducing carbon emissions strategies. This article attempts to establish a carbon emission evaluation index system for freeway transportation operation, which is divided into carbon emission level evaluation and carbon emission reduction potential evaluation. In terms of carbon emission level evaluation, the main focus is on the calculation of carbon emissions. Based on the existing methods for calculating transportation carbon emissions, a localized MOVES model is used to calculate emission factors and measure the carbon emissions of freeway transportation operations. In terms of evaluating the potential for carbon reduction, this article attempts to apply the decomposition model of influencing factors and DEA analysis method to analyze the various influencing factors and carbon emission efficiency of carbon emissions in the operation of freeway transportation. Finally, take the Shandong section of the G2 Beijing Shanghai Expressway as an example for case analysis.
This article first reviews the current research status at home and abroad from three aspects: key indicators of transportation carbon emissions, influencing factors of transportation carbon emissions, and transportation carbon emission efficiency. A review was conducted on the theoretical basis and related research methods of carbon emission entities, transportation carbon emission assessment methods, indicator set construction methods, transportation carbon emission calculation methods, transportation emission models, and influencing factor decomposition models for the operation of freeway transportation.
After clarifying the construction principles and screening process of carbon emission assessment indicators for freeway transportation operation, the indicators are divided into carbon emission level evaluation and carbon emission reduction potential evaluation based on driving factors and response goals. At the level of carbon emission level assessment, correlation analysis was conducted using relevant data from the G2 Beijing Shanghai Freeway Shandong section, focusing on vehicle type and driving distance factors. The analysis results showed that they have a high correlation with carbon emission levels and are confirmed as key variables for carbon emission level assessment. At the level of carbon emission reduction potential evaluation, a target layer, a criterion layer, and an indicator layer are established in sequence. Based on the principles and screening process of constructing the aforementioned indicators, 10 variables are selected in combination with three driving factors of macroeconomics, transportation structure, and energy consumption. Correlation analysis is conducted to better reflect the four dimensions of economy, transportation development, environment, and energy consumption proposed by the criterion layer. KMO independence tests are conducted on these variables, and the significance and quantification methods of each key indicator are clarified in combination with practical operability and data acquisition ability.
At the level of carbon emission level assessment, based on the previous discussion of several methods for carbon emission calculation, the model method is chosen to determine the value of emission factors, and the MOVES model is selected for carbon emission calculation. Based on the localization and sensitivity analysis of the MOVES model parameters, the speed based emission factor simulation method is chosen. At the level of carbon emission potential evaluation, the LMDI model was optimized by decomposing influencing factors and using the STIRPAT model to expand indicators. By decomposing the influencing factors of carbon reduction potential into six effects and calculating each effect, analyze the promoting or inhibiting effects of each effect on carbon emissions, and propose effective carbon reduction strategy suggestions based on the effects of each effect. Using DEA method to evaluate carbon emission efficiency and help identify whether transportation resource allocation has reached the optimal state to meet the needs of low-carbon transportation transformation.
Finally, using the aforementioned research results, the G2 Beijing Shanghai Freeway Shandong section is taken as an example for verification. Firstly, localize the parameters of the MOVES model to make it closer to the real situation of the G2 Beijing Shanghai Freeway Shandong section. Based on the simulated emission factor results, the carbon emissions of the Shandong section of the G2 Beijing Shanghai Freeway were calculated, and data was collected and analyzed according to the established evaluation index system. The results show that the contribution rates of carbon intensity effect, industrial structure effect, transportation structure effect, transportation intensity effect, and energy consumption effect on carbon emissions are 123.2%, 34.1%, -43.0%, 88.8%, and -103.1% respectively. The results indicate that in terms of carbon emission reduction potential, adjusting the transportation structure, promoting the adjustment of freeway transportation capacity structure, and improving energy efficiency have a significant inhibitory effect on carbon emissions. The efficiency of carbon emissions from freeway transportation operation depends on the size of economies of scale. Therefore, carbon reduction strategies can focus on optimizing the transportation structure. Increase investment in the development and promotion of energy technology, expand its technological scale, thereby promoting the use of new energy and improving energy efficiency.
Keywords: Freeway, Transportation Operation, Carbon Emission Calculation, Carbon Emission Influencing Factors, Carbon Emission Efficiency, Carbon Reduction Strategies
