引用本文
王妍, 欧国立. CGE模型在低碳交通政策领域的应用及前景[J]. 清华大学学报(自然科学版), 2023, 63(11): 1693-1706.
WANG Yan,
OU Guoli.
Application and prospects of the computable general equilibrium model in low-carbon transportation policies[J]. Journal of Tsinghua University (Science and Technology), 2023, 63(11): 1693-1706.
CGE模型在低碳交通政策领域的应用及前景
1. 北京交通大学 经济管理学院, 北京 100044;
2. 中国铁路经济规划研究院有限公司 经济与管理研究所, 北京 100038
2. 中国铁路经济规划研究院有限公司 经济与管理研究所, 北京 100038
摘要：气候变化问题十分复杂, 涉及环境、经济和社会等诸多层面因素, 交通排放是影响气候变化的重要因素。该文聚焦可计算一般均衡(computable general equilibrium, CGE)模型在交通政策研究中的应用, 共采集涵盖科学引文数据库、科学指南、中国知网等主要数据库的78篇实证研究文献, 其中超过50%的文献关注了低碳交通政策的影响。通过梳理文献发现：目前低碳交通政策评估领域的CGE模型实证研究尚处于起步阶段, 未来低碳交通CGE模型建模可从政策研究、模型构建和协同效益3个方面进行深入研究。该文提出了将“避免型/转换型/改善型-规划/制度/经济/信息/技术(avoid/shift/improve-planning/regulatory/economic/information/technological, ASI-PREIT)”结构的低碳交通政策矩阵纳入可达性因素的空间可计算一般均衡(spatial computable general equilibrium, SCGE)模型的建模思路, 并进行了协同环境—经济—社会效益的低碳交通广义协同效益研究, 为拓展低碳交通问题的研究提供参考思路。
关键词：可计算一般均衡模型 低碳交通 政策矩阵 可达性 协同效益
Application and prospects of the computable general equilibrium model in low-carbon transportation policies
1. School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China;
2. Economics and Management Office, China Railway Economic and Planning Research Institute Co., Ltd., Beijing 100038, China
2. Economics and Management Office, China Railway Economic and Planning Research Institute Co., Ltd., Beijing 100038, China
Abstract: [Significance] The issue of climate change is extremely complex and encompasses multiple factors such as the environment, economy, society, and related aspects. With the ongoing maturation of complex system modeling technology, low-carbon transportation research using the computable general equilibrium (CGE) model presents a new approach to policy evaluation. The CGE model has three primary advantages for analyzing the economic challenges of transitioning to low-carbon transportation. First, the approach has a solid microeconomic foundation that can directly reflect the mechanism and influence of economic subjects' behavior under the assumption of a rational economic player. Second, CGE models are capable of fully simulating the connections of different economic sectors, which can uncover the transmission effect of transportation policy impact among various sectors, as well as the response of various sectors to the policy impact. Third, the model has two major types, static and dynamic CGE models, which can analyze the short- and long-term impact of different policies, respectively. As an essential prediction tool for policy impact and trend analysis, CGE models can comprehensively reveal the interaction characteristics between the transportation industry and the whole national economy, enabling the prediction of the economic and social impact of low-carbon transportation policies. [Progress] This study investigates contemporary research on transportation policies based on the CGE model. A total of 78 relevant empirical studies are collected from the Web of Science, Science Direct, and China National Knowledge Infrastructure, of which more than 50% focus on predicting the impact of low-carbon transportation policies, indicating that the investigation of traffic-related carbon emissions has gradually become a popular topic of empirical analysis using CGE models. The research topics include: (1) The influence of low-carbon transportation economic incentives, such as carbon tax, emission trading scheme, and transportation subsidies. (2) The application effect of low-carbon technologies, such as electric vehicles and carbon capture and storage. (3) The effect of low-carbon transportation urban planning, including land use, vehicle speed limits, walking-oriented urban design, and bicycle-oriented urban space development. (4) Predicting the economic and social impact of the implementation of nationally determined contributions and fuel economy standards. Previous research establishes a solid foundation for prediction and policy analysis in low-carbon transportation research; however, in the context of China's 2030 carbon peak and 2060 carbon neutrality goals, some issues remain that require further exploration and investigation. [Conclusions and Prospects] First, regarding emissions reduction policies, differing transportation needs, transportation structure, energy structure, technical level, and macropolicies will affect transportation carbon emissions. The carbon emissions reduction potential of various policies requires further study, and it is essential to propose structured solutions referencing the prediction and design of composite system transportation emissions reduction policies. Based on China's 1+N policy system for advancing the dual carbon goals, this study constructs a low-carbon transportation policy matrix based on the "avoid/shift/improve-planning/regulatory/economic/information/technological (ASI-PREIT)" structure, producing a proposed "policy basket" for low-carbon transportation CGE modeling. This policy matrix will comprehensively reveal the correlation between policy tools for low-carbon transportation CGE modeling and help put forward structured low-carbon solutions. Second, in terms of model construction, accessibility is the most intuitive factor for transportation. As with other sectors, treating the transportation sector simply as a product production sector risks neglecting network and external benefits; therefore, this study proposes the inclusion of transportation accessibility factors in low-carbon transportation CGE models as spatial computable general equilibrium model to identify regional economic correlations and regional product flow. Third, in terms of synergies, carbon emissions reduction in transportation is crucial to achieving China's dual carbon goals and can advance innovation and economic growth, leveraging a wide range of synergies, including sustainable development, improving public health, and enhancing the overall quality of life. Currently, increasingly severe ecological and environmental challenges are forcing global economies to reassess the GDP-centered development model, seeking balanced and sustainable development strategies that include environment, economy, and society. This study proposes the development of a comprehensive low-carbon transportation CGE model to compare and analyze the optimal solutions for balancing the co-benefits of environment-economy-society from a global perspective and design low-carbon transportation policy combinations to advance sustainable development. In summary, this study endeavors to systematically review the empirical research applying CGE models in the field of low-carbon transportation, provide a reference for expanding the research on low-carbon transportation, and help policymakers and the transportation sector achieve China's dual carbon goals.
Key words:
computable general equilibrium model low-carbon transportation policy matrix accessibility co-benefits
交通运输行业是目前中国温室气体排放贡献率排名第三的行业，同时也是中国温室气体排放增速最快的行业。中国已将碳达峰、碳中和目标写入“十四五”规划，“1+N”政策体系正在加速制定；制定在规划设计合理的交通碳减排战略对策的同时，又能促进经济社会的高质量发展的措施，成为政策制定者和学术界共同关注的话题[2]。
可计算一般均衡(computable general equilibrium，CGE)模型作为目前国际上流行的公共政策量化分析工具，能反映不同行业之间的互动关联关系，可用于评估政策变化和经济活动所带来的直接和间接影响。这些特点使CGE模型能更好地分析交通行业与其他产业、部门之间的互动关系，更好地探究低碳交通政策带来的系统性影响[3-4]。随着复杂系统建模技术逐渐成熟，应用CGE模型进行低碳交通研究为政策评估带来了新的思路，对相关文献的梳理将有助于更好地理解这一问题，为该领域未来的研究工作拓宽思路，以助力低碳交通与环境—经济—社会协同联动系统的协调、可持续发展。
1 CGE模型在交通领域研究中的应用价值 1.1 模型简介CGE模型基本原理如图 1所示。
|
| 图 1 CGE模型概念图 |
首先，在企业生产过程中，根据企业的利润最大化原则，可得所有I部门的商品供给函数和k(k=1, 2，…, m)要素的需求函数分别为：
| $ q_i^s=q_i^s\left(p_1, p_2, \cdots, p_n, w_1, w_2, \cdots, w_m\right), $ | (1) |
| $ x_k^{\mathrm{d}}=x_k^{\mathrm{d}}\left(p_1, p_2, \cdots, p_n, w_1, w_2, \cdots, w_m\right) . $ | (2) |
其中pi为I部门生产的商品i(i=1, 2, …, n)的价格。
其次，在居民消费过程中，根据消费者效用最大化原则，可得所有I部门的商品需求函数为
| $ q_i^{\mathrm{d}}=q_i^{\mathrm{d}}\left(p_1, p_2, \cdots, p_n, Y\right), $ | (3) |
| $ Y=\sum\limits_k^m w_k \times e_k^s . $ | (4) |
其中：Y为全体居民收入，由居民的供给侧要素禀赋eks和wk决定。
再次，假设所有要素禀赋被充分利用，则要素供应等于要素禀赋，即
| $ x_k^{\mathrm{s}}=e_k^{\mathrm{s}} \text {. } $ | (5) |
最后，商品市场和要素市场实现均衡，表示为：
| $ \begin{gathered} q_i^{\mathrm{s}}\left(p_1, p_2, \cdots, p_n, w_1, w_2, \cdots, w_m\right)= \\ q_i^{\mathrm{d}}\left(p_1, p_2, \cdots, p_n, Y\right), \end{gathered} $ | (6) |
| $ x_k^{\mathrm{d}}\left(p_1, p_2, \cdots, p_n, w_1, w_2, \cdots, w_m\right)=x_k^{\mathrm{s}} . $ | (7) |
当政策冲击上述模型中的某一变量时，将引起相关部门产品或要素的变动，进而影响整个市场。
CGE模型立足于Walras一般均衡理论，以微观经济主体的理性人假设为基础，以宏观与微观变量之间的链接关系为纽带，以经济系统整体为分析对象，考察经济系统内部所有要素及商品的价格和数量变动，描述多个市场及其行为主体间的相互作用，评估政策和冲击所带来的联动影响。
1.2 发展历程1960年，经济学家Johansen基于Leontief的投入产出模型首次创建了CGE模型，旨在通过数值模拟与量化分析，研究挪威的国民经济问题[5-6]。然而，受到当时计算机技术的限制，加之当时计量经济学模型的兴起，CGE模型在20世纪六七十年代一直未受到足够的重视，这一时期仅有几篇研究国民收入和模型求解问题的文献[7-9]。一直到20世纪70年代末，以数据为依据的计量经济学模型在经济大萧条和能源危机中逐渐失去解释功能，对商品和要素价格变化的影响分析再次受到重视[10-11]。Dixon[12]在Johanson最早提出的CGE模型基础上，通过最优化方法给出了模型求解思路，使CGE模型在20世纪80年代被用于研究国际油价等宏观问题。进入21世纪，随着计算机编程技术的发展，建立了GTAP(global trade analysis project)、TERM(the enormous regional model)等CGE模型，CGE模型得到了从静态到动态、从单一区域到多地区、从单一国家到全球等维度的扩展与应用。在实际政策分析中，研究者可以根据不同的研究目的选择某一行业作为政策冲击对象，分析行业政策的系统影响，其中交通运输通常是一个重要的产业部门[10-11](见图 2)。
|
| 图 2 CGE模型沿革 |
1.3 应用价值
交通运输业作为国民经济的基础性产业，相比于其他行业有其独特性。从供给侧看，交通基础设施投资大，对上下游关联产业具有重要的带动作用；从需求侧看，交通需求作为派生需求，为旅客和货主提供时空位移服务。交通运输对国民经济社会的整体运转具有关键作用，有的运输经济研究常采用计量经济和统计分析等局部建模方法进行事后评价，对于政策效果的事前预测尚处于起步阶段。在此背景下，利用CGE模型分析运输经济问题有以下优点：一是具备扎实的微观基础，可直观反映经济主体在理性经济人假设下采取的行为机制及影响；二是充分模拟部门联动，能反映交通政策冲击在各部门间的传导效应，模拟各个部门及经济主体对政策冲击的反应；三是能更好地体现时序价值，模型分为静态和动态两大类，能够分别分析不同政策的短期冲击及长期影响[13-16]。CGE模型作为重要的政策影响分析和趋势预测工具，随着数据可得性和模型可计算性的不断深化，能深入刻画和揭示交通行业与国民经济之间的互动关联特征，从而预测运输经济活动影响和交通政策效果。
2 CGE模型在低碳交通领域的研究本文以“交通”“运输”“铁路”“公路”“民航”“水运”“碳排放”“碳减排”“CGE”和“CGE模型”等为关键词，在科学引文数据库、科学指南、中国知网等数据库中进行检索，共检索到交通领域CGE实证研究文献78篇，这些文献的年份跨度为2007—2022年，其中近5年(2018—2022年)的文献38篇(约49%)。从发表时间可以看出，CGE模型在交通研究领域的实证应用仅开展了约15年，且近年来受到的关注越来越多。
78篇文献中，静态模型46篇(约59%)，动态模型32篇(约41%)；单区域模型58篇(约74%)，多区域模型20篇(约26%)；其中33篇(约42%)研究了中国及中国部分区域的运输经济问题；41篇(约53%)关注了交通碳排放问题。受关注的运输经济问题大致可分为4大类，即绿色交通政策影响(A)[13, 17-46]；交通基础设施投资影响(B)[47-67]；新能源技术推广影响(C)[68-79]；其他运输经济问题(D)[80-93](见表 1)。
表 1 绿色交通政策影响研究
| 文献 | 研究问题 | 文献数量及其占比 | 研究子项 | ||
| 动态CGE模型 | 多区域CGE模型 | 低碳交通 | |||
| [13, 17-46] | 绿色交通政策影响 | 31篇，40% | [21，23-25，27，31，35-36，38-43，46] | [21，28，30，36，39-41] | [17-27，30-38，40-41，44-45] |
| [47-67] | 交通基础设施投资影响 | 21篇，27% | [54，56，62-63，65，67] | [48-50，52，55-58，61，63-64，66] | [52，54，60，63] |
| [68-79] | 新能源技术推广影响 | 12篇，15% | [69-75, 78] | — | [69-79] |
| [80-93] | 其他运输经济问题 | 14篇，18% | [86, 91-92] | [84] | [83-84] |
| 注：第4列展示了采用“动态CGE模型”的文献，其余文献采用“静态CGE模型”。第5列展示了构建“多区域CGE模型”的文献，其余文献聚焦单个区域构建了“单区域CGE模型”。 | |||||
对绿色交通政策效果的研究，学者们重点关注了碳交易、碳税、燃油经济性及交通污染物控制排放等制度型或经济型政策所带来的经济、社会和环境影响[13, 17-46]，评估如何缓解相关政策对经济的负面影响。例如，文[19]立足中国2030年国家自主贡献(nationally determined contributions，NDC)目标，构建中国碳交易制度(emission trading scheme，ETS)典型试点上海市的静态CGE模型，研究表明：碳交易机制有利于降低中国实现NDC目标的经济成本，可再生能源发展、碳价、碳排放配额和碳交易量是影响碳交易机制的重要因素，并预计高排放的航空运输业将成为碳配额购买的主力。文[22]研究了中国交通碳税政策，结果显示：以对宏观经济和运输部门的负面影响最小为目标，不同运输部门和不同能源类别之间的适宜碳税存在差异，且通过补贴和减免所得税等方式对碳税收入进行循环利用有利于在减弱税收扭曲效应的同时促进交通节能减排。文[46]构建动态CGE模型分析燃油税对巴西的经济影响，研究表明：通过征收燃油税并补贴公共交通服务，有利于使中低收入家庭获益，在一定程度上减少收入不平等问题。
在交通基础设施投资效果的研究方面，学者们尝试用CGE模型替代费用效益分析(cost-benefit analysis，CBA)模型进行建设项目经济可行性评估，以更好地反映大型交通基建项目的直接影响和间接影响[47-67]，旨在为规划提供更充分的决策支撑。例如，文[56]通过构建中国多区域动态CGE模型评估中国高铁的区域经济影响，研究表明：高速铁路投资主要从提高铁路部门生产率和产生投资乘数效益2方面促进经济增长，并且这种促进效应在西南欠发达地区尤为显著。文[51]和[63]均研究了高铁和公路2种准公共物品投资的乘数效应，前者采用中国单区域静态CGE模型，后者采用中国多区域动态CGE模型，研究表明：高铁和公路投资均对经济体有显著的正向作用，但在高铁和公路乘数效应的相对大小方面，由于模型和情景设定的差异，文[51]和[63]的研究结果存在较大差异。
对新能源技术推广的研究是近些年的热点话题，学者们预测分析了电动汽车普及、交通电气化、交通能源效率提升等技术革新措施对经济社会的直接和间接影响[68-79]。文[73-76]以电动汽车(electric vehicles，EVs)普及为主题，从技术进步、经济增长、污染物排放、碳减排及汽车行业发展等不同视角研究预测电动汽车发展带来的经济社会和环境影响。文[70]在分析电动汽车推广的同时，进一步考虑碳捕获与封存(carbon capture and storage，CCS)技术的综合影响，研究表明：EVs和CCS技术的推广将对减少能源转型过程中的宏观经济损失和显著降低碳排放发挥重要作用。文[71]使用动态CGE模型评估到2050年中国大规模发展可再生能源的影响，研究表明：大规模开发可再生能源不仅不会产生重大宏观经济成本，而且有利于带动相关产业链发展，重塑能源结构，实现经济和环境的双重效益。
此外，一些学者关注交通税费和补贴、运输效率及新冠肺炎疫情影响等特殊事件的短期冲击，以及一些政策的长期影响[80-93]。典型的如文[80-82]，均分析了新冠肺炎疫情对交通运输行业尤其是对客运行业的冲击，以及由此带来的宏观经济影响。
聚焦41篇研究低碳交通的文献，其中37篇(约90%)文章出自2013—2022年，19篇(约46%)文章出自2018—2022年。低碳交通研究逐渐成为CGE实证分析的研究热点[94]，包括：从碳税、碳交易和运输补贴等角度研究低碳交通经济激励手段的影响[17-23, 83]；从EVs和CCS等新技术的角度分析低碳技术的应用效果[69-76]；从土地利用、小汽车限速、步行导向型城市设计和自行车导向型城市空间发展等角度研究低碳交通城市规划的效果[24-25]；从NDC、燃油经济性标准等制度设计角度研究减排政策的经济社会影响[26-27]。上述研究为交通碳排放问题的预测分析奠定了良好基础，然而在碳达峰、碳中和新的目标背景下，仍然存在一些问题需要深入探索和研究。一是在减排政策方面，不同运输需求、运输结构、能源结构、技术水平和宏观政策都会影响交通碳排放水平，各种政策的碳减排潜力亟待深入发掘，有必要通过设计预测复合系统型交通减排方案提出结构化的解决方案。二是在模型构建方面，可达性是最能直观反映交通运输作用的指标，目前利用多区域研究模型研究交通可达性对经济社会作用的文献仍然很少，如果单纯将交通运输行业视作运输产品生产部门，就容易忽略运输的网络效益和外部效益，而刻画交通可达性将有助于优化模型的理论基础，更好地解释现实问题。三是协同效益方面，交通碳减排的效益不仅体现在气候变化本身，而且反映在碳减排的协同效益方面，例如，使用新能源技术的同时可带来其他污染物排放的降低，乃至带来由新技术产业发展而促进经济社会的发展，目前，这方面的研究尚处于起步阶段。
3 低碳交通CGE模型应用前景CGE模型能反映低碳交通政策在行业部门间产生的联动效应，具备分析可供短期冲击和长期影响的能力，虽然在关键参数校准、技术细节表达和非均衡市场条件建模等方面存在进一步优化完善的空间，但基于对文[13, 17-93]的调研，本研究认为：在碳达峰、碳中和目标深入经济社会高质量发展各个层面的新时代，充分发掘CGE模型在低碳交通领域的应用价值，可更好地把握发展与减排、局部与整体、短期和中长期之间的关系，因此研究系统性、全局性和可持续性的解决方案正当其时。本文将从以下3个方面探讨CGE模型在低碳交通领域的应用前景。
3.1 政策矩阵：低碳交通的系统性解决方案世界各国的实践证明：政策制度的创新和综合运用是推动碳减排的重要动力。目前，中国正在加速形成碳达峰、碳中和“1+N”政策体系。构建低碳交通CGE模型、聚焦重点领域、研究关键政策措施组合的减排效果及经济社会影响有助于提供多维系统的低碳交通解决方案。
鉴于世界银行、世界交通大会、欧洲环境署等诸多机构在政策分析中提出利用环境政策矩阵进行分层分类的思路[95-98]，本文系统梳理了以《中共中央国务院关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见》 《2030年前碳达峰行动方案》等为代表的10余项碳达峰、碳中和政策体系纲领文件，给出目前中国交通碳减排可以采取的主要政策，即基于“避免型/转换型/改善型-规划/制度/经济/信息/技术(avoid/shift/improve-planning/regulatory/ economic/information/technological，ASI- PREIT)”结构的低碳交通政策矩阵，如表 2所示，按照避免型(A)、转换型(S)和改善型(IMP)3种策略类型，以及规划(P)、制度(R)、经济(E)、信息(INF)和技术(T)5类工具、两大维度进行展示。通过构建中国低碳交通政策矩阵，为低碳交通CGE模型建模提供一个“政策篮子”，研究者可根据需要重点关注其中某一个政策的影响效果，也可以通过建模分析不同政策效果的差异。通过多维系统型交通减排方案的设计预测，有助于充分认识政策工具之间的关联关系，预测研究基于经济社会整体的成本效益，分析影响交通碳减排的关键核心政策措施，提出结构化的解决方案，助力交通行业尽早实现“碳中和”目标。
表 2 中国低碳交通政策矩阵
| 工具 | 策略 | ||
| A | S | IMP | |
| P | ①引导客运企业规模化、集约化经营； ②加快发展绿色物流，建设低碳集约高效的配送模式； ③加快构建便利高效、适度超前的充换电网络体系; ④加快建设大容量公共交通基础设施; ⑤加强自行车专用道和行人步道等城市慢行系统建设 |
①加快建设综合立体交通网，大力发展多式联运； ②加快构建便利高效、适度超前的充换电网络体系; ③加快建设大容量公共交通基础设施; ④加强自行车专用道和行人步道等城市慢行系统建设 |
①持续降低运输能耗和二氧化碳排放强度； ②加快构建便利高效、适度超前的充换电网络体系; ③加快建设大容量公共交通基础设施; ④加强自行车专用道和行人步道等城市慢行系统建设 |
| R | ①提高燃油车船能效标准，健全交通运输装备能效标识制度; ②加快淘汰高耗能高排放老旧车船 |
①积极发展绿色物流，包括给予新能源城市配送物流车辆更多通行便利 | ①实行新能源和清洁能源车辆规模化应用和公共交通优先发展策略 |
| E | ①加快建设完善全国碳排放权交易市场，逐步扩大市场覆盖范围，丰富交易品种和交易方式，完善配额分配管理 | ①新能源汽车免税及补贴政策，通过逐年增加新能源汽车积分比例、对平均燃料消耗量减少的企业给予奖励等 | ①加大对节能环保、新能源、低碳交通运输装备和组织方式及碳捕集利用与封存等项目的支持力度 |
| INF | ①加快建设完善全国碳排放权交易市场，逐步扩大市场覆盖范围，丰富交易品种和交易方式，完善配额分配管理 | ①组织绿色出行和公交出行等主题宣传活动 | ①建立城市交通管理、公交和出租汽车等相关系统，实现出行服务信息共享 |
| T | ①发展智能交通，推动不同运输方式合理分工、有效衔接，降低空载率和不合理客货运周转量; ②引导航空企业加强智慧运行，实现系统化节能降碳 |
①发展智能交通，推动不同运输方式合理分工、有效衔接，降低空载率和不合理客货运周转量; ②加快发展新能源和清洁能源车船，推进铁路电气化改造，促进船舶常态化靠港使用岸电; ③积极扩大电力、氢能、天然气和先进生物液体燃料等新能源和清洁能源在交通运输领域的应用 |
①加快发展新能源和清洁能源车船，推进铁路电气化改造，促进船舶常态化靠港使用岸电; ②积极扩大电力、氢能、天然气和先进生物液体燃料等新能源和清洁能源在交通运输领域的应用 |
表 2中A政策是指有助于减少不必要交通需求或交通排放的政策；S政策是指有助于将碳排放高的交通工具转换为低碳交通工具的政策；IMP政策是指有助于改善交通系统碳排放强度的策略；P政策是对基础设施的规划政策，包括公共交通、土地使用和交通布局等；R政策是政府管理机构所实施的监管工具，包括燃油标准、排放标准和通行规则等；E政策有助于将外部成本内部化，可使用的方式有控制排放权、税收和补贴等；INF政策是采用各类宣传和信息化手段促进低碳交通发展的信息策略；T政策致力于利用低碳燃料、新能源技术、智能交通和智慧运行等先进技术手段促进低碳交通的发展。例如，“加快建设综合立体交通网，大力发展多式联运”就是通过规划建设基础设施网络，采用“公转铁”“公转水”等联运方式，引导公路运输向公铁、水铁联运发展，在满足门对门运输需求的同时提升运输效率，降低全流程能耗和碳排放，属于“转换型规划政策”。
3.2 模型构建：低碳交通的空间可计算一般均衡模型鉴于交通运输业本身的特殊性，从需求端看，运输成本和运输可达性的差异会间接影响其他产品的价格和需求；从供给端看，交通基础设施投资所产生的乘数效应，以及运输网络完善带来的规模报酬递增效应会影响市场结构的变动。因此，如果单纯地在CGE建模中将交通运输视为普通的产品生产行业，就很容易忽略上述问题的复杂性[11, 99]。空间可计算一般均衡(spatial computable general equilibrium，SCGE)模型将区域经济学和空间经济学的基本逻辑引入CGE模型，能反映同一产品在不同区域的价格差异，解释由规模报酬和集聚效应产生的不同区域运输成本之间的差别。
事实上，随着运输经济学的发展以及CGE模型求解方法的不断进步，SCGE理论建模和应用已经取得了一定进展。如图 3所示，从最初的CGE模型建模到SCGE模型的建立发展大致经历了4个阶段：1) 20世纪80年代末至90年代初，拓展CGE贸易模型的空间维度，得出了运输成本影响货物在区域间流动的结论[100-102]。2) 20世纪90年代中期，借鉴空间价格均衡(spatial price equilibrium，SPE)理论预测货运网络的建模方法，融合建立具备最优运输路径的空间CGE模型[103-104]。3) 21世纪前后，受新经济地理学(new economic geography，NEG)的影响，Dixit-Stiglitz垄断竞争、规模报酬可变和集聚效应被纳入SCGE模型研究中[105-109]。4) 2010年至今，随着城市群和都市圈的不断发展，从全球、全国维度的研究逐渐深入区域维度，在交通—土地利用、交通—环境政策、交通—人口变化等问题上得到了实证问题的深入拓展：Van Truong等[10]根据12种运输可达性影响因素总结了交通CGE模型相关的实证研究; Robson等[11]和沈体雁[99]等系统梳理了SCGE建模的理论和应用研究成果，指出了目前存在的问题及难点。
|
| 图 3 SCGE模型研究进展 |
随着运输经济理论的深入发展以及上述SCGE模型的不断完善，可考虑将运输可达性因素纳入低碳交通CGE模型进行分析，在既有的单区域CGE模型基础上增加区域经济关联关系，以及商品和要素的区际流动刻画。优点为：1) 充分考虑运输可达性对区域贸易的影响，更深入地认识交通运输与经济社会之间的耦合关系，进而在一定程度上解决一般均衡理论中常常被诟病的“与现实不符”的问题；2) 细致分析同一交通碳减排政策对于不同区域的影响差异，以及不同区域交通碳减排政策的差异，从而制定有助于区域协调发展的一体化政策措施。
3.3 协同效益：低碳交通的全局最优解交通运输行业的脱碳，不仅对于实现碳达峰、碳中和目标至关重要，而且可以推动创新、促进经济增长，并带来诸如可持续发展、改善公共健康和提高生活质量等广泛的协同效益。如图 4所示，将研究视角从既有交通行业的局部分析扩展至宏观经济社会的全局影响分析。对于经济系统，交通运输业的投资有助于拉动经济增长、优化产业结构、提高就业水平，对商贸、物流、旅游和创新等均具有促进作用[10, 27, 56, 110]；同时，交通基础设施的建设运营意味着碳排放的增加，选择何种交通运输方式实现经济和交通的耦合发展问题，一直备受关注[63]。对于社会系统，伴随着城市化和产业转型发展，交通规划已经成为城市规划中必不可少的一环，交通的发展与城市土地利用和产业空间布局等具有深刻的互动关系，深刻影响着社会资源的空间分配[24-25, 110]。对于生态环境系统，对交通部门实施低碳减排措施，不仅可减少直接或间接碳排放，而且可带来节约资源、改善空气质量等环境协同效益[111-112]。进一步地，对于人居系统，交通运输碳减排在缓解气候变化的同时，有助于提高空气质量和居民的健康水平，甚至减少收入差距[113-114]。对于交通碳排放系统，不同的运输结构、运输需求、能源结构和技术水平都会影响交通能源需求及碳排放水平[115-119]，而上述因素则受到交通与环境—经济—社会—人居系统之间的耦合互动影响。因此，在研究低碳交通问题时，需要关注交通碳减排的协同效益，从而分析得到低碳交通政策的全局最优解。
|
| 图 4 经济—社会—人居—环境—交通系统 |
从狭义视角看，由于气候变化和大气污染在很大程度上具有同源性，即均主要由化石能源的使用引起，在研究交通碳减排问题的同时，有必要关注碳减排所带来的其他污染物排放的减少，如可吸入颗粒物、氮氧化物、硫氧化物和臭氧等。从广义视角看，2015年联合国可持续发展峰会提出17项可持续发展目标，涵盖经济、社会和环境诸多因素的协同发展。生态环境问题的日益严峻，正在倒逼世界各国摆脱以GDP为中心的发展模式，寻求环境—经济—社会的均衡可持续发展策略。因此，在交通碳排放CGE模型建模过程中，可以考虑分析政策组合在经济增长、环境保护、资源节约、公众健康和社会公平等方面的影响，从全局视角比较分析环境—经济—社会的协同效益，提出有利于环境—经济—社会均衡可持续发展的交通碳减排政策组合最优解，为助力交通行业实现“双碳”目标提供理论依据和科学建议。
4 结论与展望本文聚焦交通领域CGE模型实证研究成果，基于中国碳达峰和碳中和战略目标背景，重点研究和分析了交通领域CGE模型方面的文献，研究表明：应当坚持系统、全局和协调可持续发展的理念，从政策研究、模型构建和协同效益3方面出发研究低碳交通问题。在此基础上，本文提出基于ASI-PREIT结构的中国低碳交通政策矩阵、更加适于分析运输经济学前沿领域的SCGE模型建模方向及环境—经济—社会全局最优的协同效益研究方向，通过分析预测、情景对比和方案比选等手段，探索交通碳减排的结构化解决方案，助力交通行业尽早实现碳中和目标。
需要特别指出的是，针对气候变化的政策研究是一个复杂的问题，其假设条件和效用函数的设定、科学数据的处理、遏制气候变暖政策的成本收益，以及未来技术进步的不断加快均会影响决策结果。本文希望通过系统梳理低碳交通领域的CGE模型实证研究成果，提供一种可实现良好低碳交通效果的系统性评估方案，为政策制定提供参考依据。同时，通过比较不同政策组合的设定、假设条件的设定、模型等，强化预测效果的稳健性和可靠性，有助于优化对政策效果预测的科学性与准确性。
参考文献
| [1] |
Word Bank Group. Country climate and development report (CCDR) for China[R]. Washington, DC: The World Bank Group, 2022.
|
| [2] |
陈迎. 碳中和概念再辨析[J]. 中国人口·资源与环境, 2022, 32(4): 1-12. CHEN Y. A re-analysis of the concept of carbon neutrality[J]. China Population, Resources and Environment, 2022, 32(4): 1-12. (in Chinese) |
| [3] |
张欣. 可计算一般均衡模型的基本原理与编程[M]. 2版.. 上海: 格致出版社, 2018. ZHANG X. Principles of computable general equilibrium (CGE) modeling and programming[M]. 2nd ed.. Shanghai: Truth and Wisdom Press, 2018. (in Chinese) |
| [4] |
米志付. 气候变化与经济发展: 综合评估建模方法及其应用[M]. 上海: 上海三联书店, 2021. MI Z F. Climate change and economic development integrated assessment modeling method and its application[M]. Shanghai: Shanghai Joint Publishing Company, 2021. (in Chinese) |
| [5] |
LEONTIEF W W. The structure of the American economy 1919-1929[M]. Cambridge: Harvard University Press, 1941.
|
| [6] |
JOHANSEN L. A multi-sectoral study of economic growth[M]. Amsterdam: North-Holland, 1960.
|
| [7] |
SCARF H. The approximation of fixed points of a continuous mapping[J]. SIAM Journal on Applied Mathematics, 1967, 15(5): 1328-1343. DOI:10.1137/0115116 |
| [8] |
ADELMAN I, ROBINSON S. Income distribution policy in developing countries: A case study of Korea[M]. Stanford: Stanford University Press, 1978.
|
| [9] |
LYSY F J, TAYLOR L. The general equilibrium income distribution model. In World Bank, models of growth and distribution for Brazil[J]. London: Oxford University Press, 1980. |
| [10] |
VAN TRUONG N, SHIMIZU T. The effect of transportation on tourism promotion: Literature review on application of the computable general equilibrium (CGE) model[J]. Transportation Research Procedia, 2017, 25: 3096-3115. DOI:10.1016/j.trpro.2017.05.336 |
| [11] |
ROBSON E N, WIJAYARATNA K P, DIXIT V V. A review of computable general equilibrium models for transport and their applications in appraisal[J]. Transportation Research Part A: Policy and Practice, 2018, 116: 31-53. DOI:10.1016/j.tra.2018.06.003 |
| [12] |
DIXON P B, PARMENTER B R, SUTTON J M, et al. ORANI: A multisectoral model of the Australian economy[M]. Amsterdam: North-Holland, 1982.
|
| [13] |
陈建华, 刘学勇, 秦芬芬. CGE模型在交通运输行业的引入研究[J]. 北京交通大学学报(社会科学版), 2013, 12(3): 31-36. CHEN J H, LIU X Y, QIN F F. A study on application of CGE model in transportation industry[J]. Journal of Beijing Jiaotong University (Social Sciences Edition), 2013, 12(3): 31-36. DOI:10.3969/j.issn.1672-8106.2013.03.006 (in Chinese) |
| [14] |
齐天宇, 张希良, 何建坤. 全球能源经济可计算一般均衡模型研究综述[J]. 中国人口·资源与环境, 2016, 26(8): 42-48. QI T Y, ZHANG X L, HE J K. Review of research on global energy-economic computable general equilibrium models[J]. China Population, Resources and Environment, 2016, 26(8): 42-48. DOI:10.3969/j.issn.1002-2104.2016.08.007 (in Chinese) |
| [15] |
王灿, 蔡闻佳. 气候变化经济学[M]. 北京: 清华大学出版社, 2020. WANG C, CAI W J. Climate change economics[M]. Beijing: Tsinghua University Press, 2020. (in Chinese) |
| [16] |
FOURE J, AGUIAR A, BIBAS R, et al. Macroeconomic drivers of baseline scenarios in dynamic CGE models: Review and guidelines proposal[J]. Journal of Global Economic Analysis, 2020, 5(1): 28-62. DOI:10.21642/JGEA.050102AF |
| [17] |
BERG C. Household transport demand in a CGE-framework[J]. Environmental and Resource Economics, 2007, 37(3): 573-597. DOI:10.1007/s10640-006-9050-y |
| [18] |
ABRELL J. Regulating CO2 emissions of transportation in Europe: A CGE-analysis using market-based instruments[J]. Transportation Research Part D: Transport and Environment, 2010, 15(4): 235-239. DOI:10.1016/j.trd.2010.02.002 |
| [19] |
WU R, DAI H C, GENG Y, et al. Achieving China's INDC through carbon cap-and-trade: Insights from Shanghai[J]. Applied Energy, 2016, 184: 1114-1122. DOI:10.1016/j.apenergy.2016.06.011 |
| [20] |
CHOI Y, LIU Y, LEE H. The economy impacts of Korean ETS with an emphasis on sectoral coverage based on a CGE approach[J]. Energy Policy, 2017, 109: 835-844. DOI:10.1016/j.enpol.2017.06.039 |
| [21] |
TIAN X, DAI H C, GENG Y, et al. The effects of carbon reduction on sectoral competitiveness in China: A case of Shanghai[J]. Applied Energy, 2017, 197: 270-278. DOI:10.1016/j.apenergy.2017.04.026 |
| [22] |
ZHOU Y X, FANG W S, LI M J, et al. Exploring the impacts of a low-carbon policy instrument: A case of carbon tax on transportation in China[J]. Resources, Conservation and Recycling, 2018, 139: 307-314. DOI:10.1016/j.resconrec.2018.08.015 |
| [23] |
李思寰, 唐连生. 湖南交通运输业CGE碳税动态仿真模型研究[J]. 中国集体经济, 2020(10): 75-78. LI S H, TANG L S. Research on dynamic simulation model of CGE carbon tax in Hunan transportation industry[J]. China Collective Economy, 2020(10): 75-78. (in Chinese) |
| [24] |
ZHANG R S, MATSUSHIMA K, KOBAYASHI K. Land use, transport, and carbon emissions: A computable urban economic model for Changzhou, China[J]. Review of Urban & Regional Development Studies, 2016, 28(3): 162-181. |
| [25] |
ZHANG R S, LONG Y, WU W C, et al. How do transport policies contribute to a low carbon city?An integrated assessment using an urban computable general equilibrium model[J]. Energy Procedia, 2018, 152: 606-611. DOI:10.1016/j.egypro.2018.09.218 |
| [26] |
KARPLUS V J, PALTSEV S, BABIKER M, et al. Should a vehicle fuel economy standard be combined with an economy-wide greenhouse gas emissions constraint?Implications for energy and climate policy in the United States[J]. Energy Economics, 2013, 36: 322-333. DOI:10.1016/j.eneco.2012.09.001 |
| [27] |
BOONPANYA T, MASUI T. Assessing the economic and environmental impact of freight transport sectors in Thailand using computable general equilibrium model[J]. Journal of Cleaner Production, 2021, 280: 124271. DOI:10.1016/j.jclepro.2020.124271 |
| [28] |
饶呈祥. 燃油税经济效应的省际差异探析[J]. 财经科学, 2009(2): 73-80. RAO C X. The regional economic effects of fuel taxiation on different provinces[J]. Finance & Economics, 2009(2): 73-80. (in Chinese) |
| [29] |
袁长伟, 王建伟, 焦萍. 燃油税对国民经济与运输冲击的CGE模拟[J]. 交通运输工程学报, 2011, 11(6): 100-105. YUAN C W, WANG J W, JIAO P. CGE simulation of fuel oil tax impact on national economy and transportation[J]. Journal of Traffic and Transportation Engineering, 2011, 11(6): 100-105. DOI:10.19818/j.cnki.1671-1637.2011.06.016 (in Chinese) |
| [30] |
KISHIMOTO P N, ZHANG D, ZHANG X L, et al. Modeling regional transportation demand in China and the impacts of a national carbon policy[J]. Transportation Research Record: Journal of the Transportation Research Board, 2014, 2454(1): 1-11. DOI:10.3141/2454-01 |
| [31] |
KARPLUS V J, KISHIMOTO P N, PALTSEV S. The global energy, CO2 emissions, and economic impact of vehicle fuel economy standards[J]. Journal of Transport Economics and Policy, 2015, 49(4): 517-538. |
| [32] |
SOLAYMANI S, KARI F. Impacts of energy subsidy reform on the Malaysian economy and transportation sector[J]. Energy Policy, 2014, 70: 115-125. DOI:10.1016/j.enpol.2014.03.035 |
| [33] |
SOLAYMANI S, KARDOONI R, YUSOFF S B, et al. The impacts of climate change policies on the transportation sector[J]. Energy, 2015, 81: 719-728. DOI:10.1016/j.energy.2015.01.017 |
| [34] |
CAETANO T, MERVEN B, HARTLEY F, et al. Decarbonisation and the transport sector: A socio-economic analysis of transport sector futures in South Africa[J]. Journal of Energy in Southern Africa, 2017, 28(4): 9-18. |
| [35] |
KARKATSOULIS P, SISKOS P, PAROUSSOS L, et al. Simulating deep CO2 emission reduction in transport in a general equilibrium framework: The GEM-E3T model[J]. Transportation Research Part D: Transport and Environment, 2017, 55: 343-358. DOI:10.1016/j.trd.2016.11.026 |
| [36] |
MITTAL S, DAI H C, FUJIMORI S, et al. Key factors influencing the global passenger transport dynamics using the AIM/transport model[J]. Transportation Research Part D: Transport and Environment, 2017, 55: 373-388. DOI:10.1016/j.trd.2016.10.006 |
| [37] |
AVETISYAN M. Impacts of global carbon pricing on international trade, modal choice and emissions from international transport[J]. Energy Economics, 2018, 76: 532-548. DOI:10.1016/j.eneco.2018.10.020 |
| [38] |
PALTSEV S, HENRY CHEN Y H, KARPLUS V, et al. Reducing CO2 from cars in the European Union[J]. Transportation, 2018, 45(2): 573-595. DOI:10.1007/s11116-016-9741-3 |
| [39] |
TIAN X, DAI H C, GENG Y, et al. Economic impacts from PM2.5 pollution-related health effects in China's road transport sector: A provincial-level analysis[J]. Environment International, 2018, 115: 220-229. DOI:10.1016/j.envint.2018.03.030 |
| [40] |
ZHANG R S, FUJIMORI S, DAI H C, et al. Contribution of the transport sector to climate change mitigation: Insights from a global passenger transport model coupled with a computable general equilibrium model[J]. Applied Energy, 2018, 211: 76-88. DOI:10.1016/j.apenergy.2017.10.103 |
| [41] |
TIAN X, DAI H C, GENG Y, et al. Toward the 2-degree target: Evaluating co-benefits of road transportation in China[J]. Journal of Transport & Health, 2019, 15: 100674. |
| [42] |
BETARELLI JR A A, DOMINGUES E P, HEWINGS G J D. Transport policy, rail freight sectors, and market structure: The economic effects in Brazil[J]. Transportation Research Part A: Policy and Practice, 2020, 135: 1-23. DOI:10.1016/j.tra.2020.02.018 |
| [43] |
REN S Y, WANG P, DAI H C, et al. Health and economic impact assessment of transport and industry PM2.5 control policy in Guangdong Province[J]. Sustainability, 2021, 13(23): 13049. DOI:10.3390/su132313049 |
| [44] |
SOLAYMANI S, SHARAFI S. A comparative study between government support and energy efficiency in Malaysian transport[J]. Sustainability, 2021, 13(11): 6196. DOI:10.3390/su13116196 |
| [45] |
MUTO S, TOYAMA H, TAKAI A. Evaluation of transport and location policies to realize the carbon-free urban society[J]. Sustainability, 2021, 14(1): 1-21. DOI:10.3390/su14010001 |
| [46] |
PROQUE A L, JUNIOR A A B, PEROBELLI F S. Fuel tax, cross subsidy, and transport: Assessing the effects on income and consumption distribution in Brazil[J]. Research in Transportation Economics, 2022, 95: 101204. DOI:10.1016/j.retrec.2022.101204 |
| [47] |
HORRIDGE M, WITTWER G. SinoTERM, a multi-regional CGE model of China[J]. China Economic Review, 2008, 19(4): 628-634. DOI:10.1016/j.chieco.2008.05.002 |
| [48] |
KIM E, HEWINGS G J D. An application of an integrated transport network-multiregional CGE model to the calibration of synergy effects of highway investments[J]. Economic Systems Research, 2009, 21(4): 377-397. DOI:10.1080/09535310903444758 |
| [49] |
BRÖCKER J, KORZHENEVYCH A, SCHVRMANN C. Assessing spatial equity and efficiency impacts of transport infrastructure projects[J]. Transportation Research Part B: Methodological, 2010, 44(7): 795-811. DOI:10.1016/j.trb.2009.12.008 |
| [50] |
HADDAD E A, PEROBELLI F S, DOMINGUES E P, et al. Assessing the ex ante economic impacts of transportation infrastructure policies in Brazil[J]. Journal of Development Effectiveness, 2011, 3(1): 44-61. DOI:10.1080/19439342.2010.545891 |
| [51] |
庄序莹, 侯敬雯. 高速铁路、公路建设的财政投资效益研究: 基于可计算一般均衡(CGE)模型的分析[J]. 财贸经济, 2012(6): 43-49. ZHUANG X Y, HOU J W. Comparative study of government investment returns between high-speed railway and highway construction: A CGE analysis[J]. Finance & Trade Economics, 2012(6): 43-49. DOI:10.3969/j.issn.1005-913X.2012.06.022 (in Chinese) |
| [52] |
BROHI K M, UQAILI M A, MAHAR R B. Environmental policies for new road network of Pakistan to control air emissions[M]//UQAILI M, HARIJAN K. Energy, Environment and Sustainable Development. Vienna: Springer, 2012: 307-319.
|
| [53] |
KIM E, BAE Y. Economic contribution of the private sector on financing and operation of a highway: Financial computable general equilibrium[J]. Journal of Transport Economics and Policy, 2015, 49(3): 397-414. |
| [54] |
CHEN Z H, XUE J B, ROSE A Z, et al. The impact of high-speed rail investment on economic and environmental change in China: A dynamic CGE analysis[J]. Transportation Research Part A: Policy and Practice, 2016, 92: 232-245. DOI:10.1016/j.tra.2016.08.006 |
| [55] |
ELSHAHAWANY D N, HADDAD E A, LAHR M L. Accessibility, transportation cost, and regional growth: A case study for Egypt[J]. Middle East Development Journal, 2017, 9(2): 256-277. DOI:10.1080/17938120.2017.1366773 |
| [56] |
CHEN Z H. Measuring the regional economic impacts of high-speed rail using a dynamic SCGE model: The case of China[J]. European Planning Studies, 2019, 27(3): 483-512. DOI:10.1080/09654313.2018.1562655 |
| [57] |
朱博恩, 张伯伟, 马骆茹. 交通基础设施联通对"丝绸之路经济带"的经济影响研究: 基于CGE的模拟分析[J]. 国际商务(对外经济贸易大学学报), 2019(5): 41-55. ZHU B E, ZHANG B W, MA L R. Study on economic effects of transportation infrastructure connectivity along the"Silk Road Economic Belt ": Based on CGE model[J]. International Business (Joural of the University of International business and Economics), 2019(5): 41-55. (in Chinese) |
| [58] |
李娜, 石敏俊, 张卓颖, 等. 基于多区域CGE模型的长江经济带一体化政策效果分析[J]. 中国管理科学, 2020, 28(12): 67-76. LI N, SHI M J, ZHANG Z Y, et al. Analysis on policy effects of integration of Yangtze River Economic Belt based on a multi-regional CGE model[J]. Chinese Journal of Management Science, 2020, 28(12): 67-76. DOI:10.16381/j.cnki.issn1003-207x.2020.12.007 (in Chinese) |
| [59] |
PISKIN M, HEWINGS G J D, HANNUM, C M. Synergy effects of highway investments on the Turkish economy: An application of an integrated transport network with a multiregional CGE model[J]. Transport Policy, 2020, 95: 78-92. DOI:10.1016/j.tranpol.2020.05.011 |
| [60] |
CARDENETE M A, LÓPEZ-CABACO R. Economic and environmental impact of the new Mediterranean Rail Corridor in Andalusia: A dynamic CGE approach[J]. Transport Policy, 2021, 102: 25-34. DOI:10.1016/j.tranpol.2020.12.007 |
| [61] |
KIM E, MOON S W, YI Y. Analyzing spillover effects of development of Asian highway on regional growth of Northeast Asian countries[J]. Review of Development Economics, 2021, 25(3): 1243-1266. DOI:10.1111/rode.12786 |
| [62] |
ROKICKI B, HADDAD E A, HORRIDGE J M, et al. Accessibility in the regional CGE framework: The effects of major transport infrastructure investments in Poland[J]. Transportation, 2021, 48(2): 747-772. DOI:10.1007/s11116-019-10076-w |
| [63] |
YANG Q, HU X R, WANG Y Q, et al. Comparison of the impact of China's railway investment and road investment on the economy and air pollution emissions[J]. Journal of Cleaner Production, 2021, 293: 126100. DOI:10.1016/j.jclepro.2021.126100 |
| [64] |
ALI T, HUANG J K, XIE W. Bilateral economic impacts of China-Pakistan economic corridor[J]. Agriculture, 2022, 12(2): 143. DOI:10.3390/agriculture12020143 |
| [65] |
CETIN V R. When do public transport investments really matter?A CGE analysis for Türkiye[J/OL]. Economic Systems Research. (2022-09-26)[2022-12-10]. https://www.tandfonline.com/doi/full/10.1080/09535314.2022.2122407.
|
| [66] |
ISHIKURA T, YOKOYAMA F. Regional economic effects of the Ring Road project in the Greater Tokyo Area: A spatial CGE approach[J]. Papers in Regional Science, 2022, 101(4): 811-837. DOI:10.1111/pirs.12677 |
| [67] |
NJOYA E T, RAGAB A M. Economic impacts of public air transport investment: A case study of Egypt[J]. Sustainability, 2022, 14(5): 2651. DOI:10.3390/su14052651 |
| [68] |
ANSON S, TURNER K. Rebound and disinvestment effects in refined oil consumption and supply resulting from an increase in energy efficiency in the Scottish commercial transport sector[J]. Energy Policy, 2009, 37(9): 3608-3620. DOI:10.1016/j.enpol.2009.04.035 |
| [69] |
SILVA C M, FERREIRA A F, BENTO J P C. Impact of hydrogen in the road transport sector for Portugal 2010-2050[J]. Energy Procedia, 2014, 58: 207-214. DOI:10.1016/j.egypro.2014.10.430 |
| [70] |
LI W, JIA Z J, ZHANG H Z. The impact of electric vehicles and CCS in the context of emission trading scheme in China: A CGE-based analysis[J]. Energy, 2017, 119: 800-816. DOI:10.1016/j.energy.2016.11.059 |
| [71] |
DAI H C, XIE X X, XIE Y, et al. Green growth: The economic impacts of large-scale renewable energy development in China[J]. Applied Energy, 2016, 162: 435-449. DOI:10.1016/j.apenergy.2015.10.049 |
| [72] |
SIAGIAN U W R, YUWONO B B, FUJIMORI S, et al. Low-carbon energy development in Indonesia in alignment with intended nationally determined contribution (INDC) by 2030[J]. Energies, 2017, 10(1): 52. DOI:10.3390/en10010052 |
| [73] |
GUO Z W, LI T, PENG S T, et al. Environmental and economic consequences of the incentive policy on electric vehicle industry: A CGE based study in China[J]. Resources, Conservation, and Recycling, 2021, 169: 105542. DOI:10.1016/j.resconrec.2021.105542 |
| [74] |
LIN B Q, WU W. The impact of electric vehicle penetration: A recursive dynamic CGE analysis of China[J]. Energy Economics, 2021, 94: 105086. DOI:10.1016/j.eneco.2020.105086 |
| [75] |
GUO Z W, LI T, SHI B W, et al. Economic impacts and carbon emissions of electric vehicles roll-out towards 2025 goal of China: An integrated input-output and computable general equilibrium study[J]. Sustainable Production and Consumption, 2022, 31: 165-174. DOI:10.1016/j.spc.2022.02.009 |
| [76] |
JIANG H D, XUE M M, LIANG Q M, et al. How do demand-side policies contribute to the electrification and decarburization of private transportation in China?A CGE-based analysis[J]. Technological Forecasting and Social Change, 2022, 175: 121322. DOI:10.1016/j.techfore.2021.121322 |
| [77] |
DU H B, CHEN Z N, ZHANG Z K, et al. The rebound effect on energy efficiency improvements in China's transportation sector: A CGE analysis[J]. Journal of Management Science and Engineering, 2020, 5(4): 249-263. DOI:10.1016/j.jmse.2020.10.005 |
| [78] |
KHAMPHILAVANH B, MASUI T. Scenario-based analysis of electric vehicle penetration in road transportation in Laos[C]//Proceedings of 2020 International Conference and Utility Exhibition on Energy, Environment, and Climate Change (ICUE). Pattaya, Thailand: IEEE, 2020: 1-8.
|
| [79] |
TAMBA M, KRAUSE J, WEITZEL M, et al. Economy-wide impacts of road transport electrification in the EU[J]. Technological Forecasting and Social Change, 2022, 182: 121803. DOI:10.1016/j.techfore.2022.121803 |
| [80] |
CUI Q, HE L, LIU Y, et al. The impacts of COVID-19 pandemic on China's transport sectors based on the CGE model coupled with a decomposition analysis approach[J]. Transport Policy, 2021, 103: 103-115. DOI:10.1016/j.tranpol.2021.01.017 |
| [81] |
ZHANG Q, TONG Q. The economic impacts of traffic consumption during the COVID-19 pandemic in China: A CGE analysis[J]. Transport Policy, 2021, 114: 330-337. DOI:10.1016/j.tranpol.2021.10.018 |
| [82] |
JUNIOR A A B, FARIA W R, PROQUE A L, et al. COVID-19, public agglomerations, and economic effects: Assessing the recovery time of passenger transport services in Brazil[J]. Transport Policy, 2021, 110: 254-272. DOI:10.1016/j.tranpol.2021.06.004 |
| [83] |
TSCHARAKTSCHIEW S, HIRTE G. Should subsidies to urban passenger transport be increased?A spatial CGE analysis for a German metropolitan area[J]. Transportation Research Part A: Policy and Practice, 2012, 46(2): 285-309. DOI:10.1016/j.tra.2011.09.006 |
| [84] |
LEE T C, LAM J S L, LEE P T W. Asian economic integration and maritime CO2 emissions[J]. Transportation Research Part D: Transport and Environment, 2016, 43: 226-237. DOI:10.1016/j.trd.2015.12.015 |
| [85] |
HADDAD E A, HEWINGS G J D, PEROBELLI F S, et al. Regional effects of port infrastructure: A spatial CGE application to Brazil[J]. International Regional Science Review, 2010, 33(3): 239-263. DOI:10.1177/0160017610368690 |
| [86] |
SOLAYMANI S, KARI F. Environmental and economic effects of high petroleum prices on transport sector[J]. Energy, 2013, 60: 435-441. DOI:10.1016/j.energy.2013.08.037 |
| [87] |
STEININGER K W, FRIEDL B, GEBETSROITHER B. Sustainability impacts of car road pricing: A computable general equilibrium analysis for Austria[J]. Ecological Economics, 2007, 63(1): 59-69. DOI:10.1016/j.ecolecon.2006.09.021 |
| [88] |
NJOYA E T, NIKITAS A. The role of air transport in employment creation and inclusive growth in the Global South: The case of South Africa[J]. Journal of Transport Geography, 2020, 85: 102738. DOI:10.1016/j.jtrangeo.2020.102738 |
| [89] |
王碧茹, 曾小舟, 杨冰慧, 等. 基于CGE的民用客机产业效应变动分析[J]. 哈尔滨商业大学学报(自然科学版), 2022, 38(2): 243-250. WANG B R, ZENG X Z, YANG B H, et al. Analysis of industrial effects of Chinese civil aircraft based on CGE model[J]. Journal of Harbin University of Commerce (Natural Sciences Edition), 2022, 38(2): 243-250. (in Chinese) |
| [90] |
马星, 曾小舟, 曾修彬. 基于CGE模型的我国民用飞机政策影响分析[J]. 华东交通大学学报, 2015, 32(2): 59-66. MA X, ZENG X Z, ZENG X B. Policy analysis of Chinese civil aircraft based on CGE model[J]. Journal of East China Jiaotong University, 2015, 32(2): 59-66. DOI:10.3969/j.issn.1005-0523.2015.02.011 (in Chinese) |
| [91] |
胡宗义, 韩婉莹, 刘亦文. 大飞机项目对相关产业影响的CGE研究[J]. 湖南大学学报(自然科学版), 2009, 36(8): 88-92. HU Z Y, HAN W Y, LIU Y W. Impact of large aircraft project on Chinese macro-economy and various industries, a CGE approach[J]. Journal of Hunan University (Natural Sciences), 2009, 36(8): 88-92. (in Chinese) |
| [92] |
戚铭尧, 杨坤河, 缪立新. 基于可计算一般均衡的货运量预测模型[J]. 工业技术经济, 2013, 32(3): 3-10. QI M Y, YANG K H, MIAO L X. A freight forecasting model based on computable general equilibrium[J]. Journal of Industrial Technological Economics, 2013, 32(3): 3-10. DOI:10.3969/j.issn.1004-910X.2013.03.001 (in Chinese) |
| [93] |
饶呈祥, 范平. 交通非税收入对我国经济影响的CGE分析[J]. 软科学, 2008, 22(6): 32-37, 51. RAO C X, FAN P. The economic effects of traffic revenue from a CGE analysis[J]. Soft Science, 2008, 22(6): 32-37, 51. (in Chinese) |
| [94] |
HUANG L, KELLY S, Lü K J, et al. A systematic review of empirical methods for modelling sectoral carbon emissions in China[J]. Journal of Cleaner Production, 2019, 215: 1382-1401. |
| [95] |
世界银行《碧水蓝天》编写组. 碧水蓝天: 展望21世纪的中国环境[M]. 云萍, 祁忠, 译. 北京: 中国财政经济出版社, 1997. Word Bank the Writing Group of" Blue Water and Sky". Clear water, blue skies: China's environment in the new century[M]. YUN P, QI Z, trans. Beijing: China Financial & Economic Publishing House, 1997. (in Chinese) |
| [96] |
World Conference on Transport Research Society and Institute for Transport Policy Studies. Urban transport and the environment: An international perspective[M]. Boston: Elsevier, 2004.
|
| [97] |
EUROPEAN ENVIRONMENT AGENCY. Towards a resource-efficient transport system-TERM 2009: Indicators tracking transport and environment in the European Union[R]. Brussels: European Union, 2010.
|
| [98] |
NAKAMURA K, HAYASHI Y. Strategies and instruments for low-carbon urban transport: An international review on trends and effects[J]. Transport Policy, 2013, 29: 264-274. |
| [99] |
沈体雁, 温璐歌. 基于SCGE的国土空间规划模拟框架CTSPM及其在国土空间安全模拟仿真中的应用[J]. 自然资源学报, 2021, 36(9): 2320-2334. SHEN T Y, WEN L G. Simulation framework of China's territorial spatial planning model (CTSPM) based on spatial computable general equilibrium model (SCGE) and its application in land and space safety simulation[J]. Journal of Natural Resources, 2021, 36(9): 2320-2334. (in Chinese) |
| [100] |
JONES R, WHALLEY J. A Canadian regional general equilibrium model and some applications[J]. Journal of Urban Economics, 1989, 25(3): 368-404. |
| [101] |
BUCKLEY P H. A transportation-oriented interregional computable general equilibrium model of the United States[J]. The Annals of Regional Science, 1992, 26(4): 331-348. |
| [102] |
WIDE R M. Transportation costs in regionai, models of foreign trade: An application to Canada-U.S. trade[J]. Journal of Regional Science, 1992, 32(2): 185-207. |
| [103] |
ROSON R. Computable spatial economic equilibria and freight network modelling[J]. International Journal of Transport Economics, 1993, 20(1): 51-66. |
| [104] |
ROSON R. The macroeconomic impact of traffic congestion: A CGE analysis[M]//VAN DEN BERGH J C J M, NIJKAMP P, RIETVELD P. Recent Advances in Spatial Equilibrium Modelling. Berlin, Springer, 1996: 261-277.
|
| [105] |
BRÖCKER J. Operational spatial computable general equilibrium modeling[J]. The Annals of Regional Science, 1998, 32(3): 367-387. |
| [106] |
FAN W, TREYZ F, TREYZ G. An evolutionary new economic geography model[J]. Journal of Regional Science, 2000, 40(4): 671-695. |
| [107] |
MIYAGI T. Economic appraisal for multiregional impacts by a large-scale expressway project[R]. Amsterdam: Tinbergen Institute Discussion Papers, 2001.
|
| [108] |
HOVI I B, JEAN-HANSEN V. Establishing marginals for Norwegian freight flows in 2003[R]. Oslo: Institute of Transport Economics, 2006.
|
| [109] |
VOLD A, JEAN-HANSEN V. PINGO: A model for prediction of regional and interregional freight transport in Norway[R]. Oslo: Institute of Transport Economics, 2007.
|
| [110] |
CASCETTA E, CARTENÌ A, HENKE I, et al. Economic growth, transport accessibility and regional equity impacts of high-speed railways in Italy: Ten years expost evaluation and future perspectives[J]. Transportation Research Part A: Policy and Practice, 2020, 139: 412-428. |
| [111] |
SCHNELL J L, NAIK V, HOROWITZ L W, et al. Air quality impacts from the electrification of light-duty passenger vehicles in the United States[J]. Atmospheric Environment, 2019, 208: 95-102. |
| [112] |
陆潘涛, 韩亚龙, 戴瀚程. 1.5℃和2℃目标下中国交通部门2050年的节能减排协同效益[J]. 北京大学学报(自然科学版), 2021, 57(3): 517-528. LU P T, HAN Y L, DAI H C. Co-benefits of decarbonizing China's transport sector in energy saving and emission reduction under 1.5-and 2-degree targets in 2050[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2021, 57(3): 517-528. (in Chinese) |
| [113] |
YU F, XIAO D, CHANG M S. The impact of carbon emission trading schemes on urban-rural income inequality in China: A multi-period difference-in-differences method[J]. Energy Policy, 2021, 159: 112652. |
| [114] |
WANG M, FENG C. Tracking the inequalities of global per capita carbon emissions from perspectives of technological and economic gaps[J]. Journal of Environmental Management, 2022, 315: 115144. |
| [115] |
SCHÄFER A, JACOBY H D. Technology detail in a multisector CGE model: Transport under climate policy[J]. Energy Economics, 2005, 27(1): 1-24. |
| [116] |
WANG H L, OU X M, ZHANG X L. Mode, technology, energy consumption, and resulting CO2 emissions in China's transport sector up to 2050[J]. Energy Policy, 2017, 109: 719-733. |
| [117] |
PAN X Z, WANG H L, WANG L N, et al. Decarbonization of China's transportation sector: In light of national mitigation toward the Paris agreement goals[J]. Energy, 2018, 155: 853-864. |
| [118] |
牛玉静, 陈文颖, 吴宗鑫. 全球多区域CGE模型的构建及其3E扩展[J]. 清华大学学报(自然科学版), 2014, 54(8): 1073-1079. NIU Y J, CHEN W Y, WU Z X. Global multi-regional CGE model with its 3E extensions[J]. Journal of Tsinghua University (Science & Technology), 2014, 54(8): 1073-1079. (in Chinese) |
| [119] |
刘俊伶, 孙一赫, 王克, 等. 中国交通部门中长期低碳发展路径研究[J]. 气候变化研究进展, 2018, 14(5): 513-521. LIU J L, SUN Y H, WANG K, et al. Study on mid-and long-term low carbon development pathway for China's transport sector[J]. Climate Change Research, 2018, 14(5): 513-521. (in Chinese) |