Study on urban passenger transport carbon reduction pathways based on system dynamics – Nature

Executive Summary: A System Dynamics Analysis of Urban Passenger Transport Decarbonization Pathways

Controlling carbon emissions from passenger transport is a critical component in achieving national dual-carbon goals and advancing the Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). This report details the development and application of a system dynamics model designed to simulate carbon reduction pathways in urban passenger transport. The model integrates five key subsystems: population and economy, energy consumption, traffic operation, urban road infrastructure, and carbon emission control. Based on a case study of Beijing from 2010 to 2024, scenario simulations were conducted to evaluate the effectiveness of various policy interventions.

Analysis of single-policy scenarios revealed that optimizing public transit and upgrading energy technology, which directly support SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure), demonstrated greater effectiveness in emission reduction than travel demand management alone. However, dual-policy combinations yielded significantly improved outcomes, highlighting the necessity of integrated strategies. The effectiveness of these combinations was ranked as follows:

1. Public Transport and Energy Technology Policies (11.32% reduction)
2. Public Transport and Travel Demand Policies (10.58% reduction)
3. Energy Technology and Travel Demand Policies (8.94% reduction)

The findings conclude that single-policy measures are insufficient to meet ambitious emission reduction targets. A multidimensional, synergistic policy approach—integrating technological upgrades, structural optimization of transport systems, and behavioral guidance—is essential. This provides a systematic pathway for developing sustainable, low-carbon urban transport systems in alignment with global sustainability objectives.

1.0 Introduction: Aligning Urban Transport with Sustainable Development Goals

The global commitment to the 2030 Agenda for Sustainable Development necessitates transformative action in high-emission sectors. Urban transportation, a cornerstone of modern economies and societies, is a major contributor to global carbon emissions, posing a significant challenge to achieving SDG 13 (Climate Action). In China, the “Dual Carbon Goals”—peaking emissions by 2030 and achieving carbon neutrality by 2060—amplify the urgency of decarbonizing this sector. Rapid urbanization has led to increased travel demand, but traditional transport models suffer from structural imbalances and low energy efficiency, undermining progress toward SDG 11 (Sustainable Cities and Communities) by contributing to congestion and pollution.

Addressing this challenge requires a multi-faceted approach. Mainstream research has focused on three core dimensions for creating sustainable transport systems:

• Technological Innovation: Promoting new energy technologies to enhance emission reduction efficiency, directly contributing to SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure).
• Structural Optimization: Increasing the share of public transportation and optimizing rail transit networks to build resilient and sustainable infrastructure (SDG 9).
• Behavioral Regulation: Encouraging green travel behaviors like cycling, walking, and public transit use to foster sustainable consumption patterns (SDG 12).

Recognizing the complexity and interconnectedness of urban transport systems, this study utilizes a System Dynamics (SD) model. This approach allows for the analysis of complex feedback loops and the simulation of various policy scenarios, providing a robust quantitative foundation for designing effective, integrated policies that advance multiple SDGs simultaneously.

2.0 Methodology: A System Dynamics Model for Sustainable Transport

2.1 Model Framework and System Boundaries

A comprehensive SD model was constructed to analyze the carbon reduction pathways of urban passenger transport. The system boundary was defined to include five interrelated subsystems, reflecting a holistic approach to sustainable urban planning:

1. Population and Economy Subsystem: Models the drivers of transport demand, linking economic growth (SDG 8) with population dynamics.
2. Energy Consumption Subsystem: Tracks energy use by different transport modes, focusing on the transition to cleaner energy sources (SDG 7).
3. Urban Road Infrastructure Subsystem: Represents the development of physical infrastructure, a key component of SDG 9.
4. Traffic Operation Subsystem: Simulates the allocation of passenger volume across various transport modes, including public and private options.
5. Carbon Emission Control Subsystem: Calculates total carbon emissions and models the impact of mitigation and governance efforts (SDG 13).

The model focuses on gasoline, diesel, and electricity as the primary end-use energy sources, based on the energy structure of the passenger transport sector in Beijing.

2.2 Model Validation and Sensitivity Analysis

Prior to simulation, the model underwent rigorous validation to ensure its structural integrity and reliability. The validation process included:

• Structure Consistency Testing: The model’s internal logic, equations, and unit dimensions were verified using Vensim PLE software, confirming its applicability.
• Time Step Sensitivity Analysis: The model demonstrated high stability and consistency when tested with different time steps (0.25, 0.5, and 1 year), confirming its robustness.
• Historical Data Fitting: The model’s outputs for key variables, such as GDP and urban rail transit passenger volume, were compared against historical data from 2010 to 2024. The deviation remained within 5%, confirming the model’s validity.

Sensitivity analysis was also conducted to assess how variations in key parameters, such as energy cost ratios, influence model outputs, thereby enhancing the model’s predictive capabilities for policy simulation.

3.0 Results: Policy Scenario Simulation and SDG Impact Analysis

Simulations were conducted for the period 2010–2040 to analyze the effects of different policy interventions on carbon emissions. A baseline scenario was established, followed by single-policy and dual-policy combination scenarios.

3.1 Baseline Scenario and Single-Policy Interventions

The baseline simulation showed declining emission trends for public buses and taxis, largely due to policy-driven improvements in energy efficiency and the adoption of new energy vehicles. In contrast, emissions from rail transit showed a gradual increase, reflecting network expansion to meet growing demand. Three single-policy scenarios were then simulated:

• Public Transport Policy: Increasing rail transit mileage by 5% and 10% resulted in carbon emission reductions of 7.23% and 9.18%, respectively. This highlights the role of sustainable infrastructure (SDG 9) in achieving climate goals.
• Energy Technology Policy: Reducing the unit energy consumption of new energy vehicles by 5% and 10% led to emission reductions of 7.97% and 9.53%. This underscores the importance of investing in clean energy technology (SDG 7).
• Travel Behavior Policy: Reducing private vehicle passenger volume by 5% and 10% yielded emission reductions of 8.13% and 9.88%, demonstrating the impact of promoting sustainable consumption and production patterns (SDG 12).

3.2 Dual-Policy Synergy Analysis

To reflect real-world governance, dual-policy combinations were simulated, revealing significant synergistic effects that produced superior outcomes compared to single interventions. This finding aligns with the integrated nature of the 2030 Agenda, where progress on one goal can reinforce progress on others.

The emission reduction effectiveness of the dual-policy combinations was ranked as follows:

1. Public Transport + Energy Technology: 11.32% reduction. This combination effectively pairs investment in sustainable infrastructure (SDG 9) with advancements in clean energy (SDG 7).
2. Public Transport + Travel Behavior: 10.58% reduction. This strategy combines infrastructure development with public engagement to shift mobility patterns.
3. Energy Technology + Travel Behavior: 8.94% reduction. This pairing focuses on technological efficiency and demand-side management.

The results clearly indicate that integrated policy packages, particularly those that combine structural and technological interventions, are most effective for decarbonizing urban transport.

4.0 Conclusion and Policy Recommendations

This study confirms that the carbon reduction pathway for urban passenger transport is a complex system influenced by multiple, interwoven factors. Relying on a single policy strategy is insufficient to achieve the transformative change needed to meet climate targets and build sustainable cities. The superior performance of dual-policy combinations underscores the necessity of a synergistic, integrated approach.

To accelerate progress towards SDG 11 and SDG 13, policymakers should prioritize a multi-dimensional strategy that coordinates technological, structural, and behavioral interventions. The most effective pathway involves simultaneously expanding high-quality public transport infrastructure and promoting advanced clean energy technologies. Such an approach not only reduces carbon emissions but also enhances urban livability, economic productivity, and public health, creating co-benefits across multiple SDGs.

5.0 Future Research Directions for Advancing the 2030 Agenda

To further support the development of sustainable urban transport systems, future research should focus on the following areas:

• Multi-System Coupling Studies: Explore the dynamic links between transport systems and other urban systems like energy grids (SDG 7), land use planning, and regional economies to identify cross-sectoral synergies and potential risks.
• Refined Simulation and Equity Assessment: Quantify the social equity impacts of different policy portfolios, particularly concerning travel accessibility for different income groups and vulnerable populations, to ensure that the green transition reduces inequalities (SDG 10).
• Impact of Disruptive Innovation: Integrate emerging technologies such as autonomous driving and shared mobility platforms into models to simulate their large-scale impact on infrastructure investment (SDG 9), energy demand, and urban form.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 7: Affordable and Clean Energy

  The article directly addresses clean energy by focusing on the transition from traditional fossil fuels to cleaner alternatives in urban transport. It analyzes the role of “new energy technologies,” “new energy vehicles (NEVs),” and electricity as a primary energy source for rail transit and electric buses. The study’s scenarios involving “energy technology upgrades” to reduce “unit energy consumption” are central to achieving cleaner energy goals in the transport sector.

• SDG 9: Industry, Innovation, and Infrastructure

  This goal is central to the article’s discussion on upgrading urban transportation systems. The research emphasizes the importance of investing in and expanding sustainable infrastructure, such as increasing “total urban rail transit mileage.” Furthermore, it highlights technological innovation through the “adoption and promotion of new energy technologies” and “continuous improvements in vehicle energy efficiency technologies” as key drivers for decarbonization.

• SDG 11: Sustainable Cities and Communities

  The entire study is framed within the context of sustainable urban development. It aims to solve critical urban problems like traffic congestion and pollution by promoting sustainable transport systems. The article evaluates policies for “public transit optimization,” increasing the “share of public transportation,” and encouraging “green travel behavior” (cycling, walking, public transit), all of which are essential for creating sustainable, resilient, and inclusive cities.

• SDG 13: Climate Action

  Climate action is the primary driver of the research presented in the article. The study is motivated by China’s “Dual Carbon Goals” (“Carbon Peak” by 2030 and “Carbon Neutrality” by 2060). The core objective is to find effective “carbon reduction pathways” for urban passenger transport, which is identified as a major contributor to global carbon emissions. The analysis of different policy scenarios for emission reduction is a direct response to the urgent need for climate change mitigation.

• SDG 10: Reduced Inequalities

  Although not a primary focus of the main analysis, this goal is explicitly mentioned in the “Future research directions” section. The article suggests that future studies should assess the “social equity impacts of different policy combinations,” including “disparities in travel accessibility across income groups and regions” and ensuring the “mobility rights of vulnerable populations.” This acknowledges that the transition to green transport must be equitable and not exacerbate social inequalities.

2. What specific targets under those SDGs can be identified based on the article’s content?

• SDG 7: Affordable and Clean Energy

  • Target 7.3: By 2030, double the global rate of improvement in energy efficiency. The article directly relates to this target by simulating “energy technology optimization policies” designed to reduce the “unit energy consumption of new energy vehicles” by 5% and 10%. This focus on enhancing energy efficiency is a core strategy discussed for reducing emissions.

• SDG 9: Industry, Innovation, and Infrastructure

  • Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure… with a focus on affordable and equitable access for all. The study’s evaluation of “public transport policies,” specifically through the “increase of total urban rail transit mileage,” directly supports the development of sustainable transport infrastructure.
  • Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies. This target is addressed through the article’s emphasis on “technological innovation,” the “adoption of new energy technologies,” and the shift towards electric vehicles and efficient rail transit, which represent cleaner and more efficient technological solutions for the transport sector.

• SDG 11: Sustainable Cities and Communities

  • Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all… notably by expanding public transport. The article’s core analysis revolves around this target. It evaluates the effectiveness of “public transport development policies,” such as expanding rail transit and increasing the share of public buses, as a primary means to create a sustainable urban transport system.
  • Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities. The central theme of the article is the reduction of “passenger transport carbon emissions,” which is a major adverse environmental impact of cities. The entire system dynamics model is built to simulate and find pathways to minimize this impact.

• SDG 13: Climate Action

  • Target 13.2: Integrate climate change measures into national policies, strategies and planning. The article’s methodology of simulating single-policy and dual-policy scenarios (e.g., “public transport and energy technology policies”) to achieve China’s “dual-carbon goals” is a clear example of analyzing how to integrate climate change measures into urban transport planning and policy-making.

• SDG 10: Reduced Inequalities

  • Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all. This target is connected through the “Future research directions” section, which calls for assessing the “social equity impacts” of transport policies to ensure they do not negatively affect vulnerable populations and instead promote equitable “travel accessibility.”

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Indicators for SDG 7 (Target 7.3)

  • Unit energy consumption of new energy vehicles: The article explicitly uses this as a variable in its “Energy technology policy” scenarios, simulating reductions of 5% and 10%. This is a direct measure of energy efficiency improvement.
  • Total energy consumption from passenger transport: This is an implied indicator, as the model calculates energy consumption from different modes (diesel, electricity) to determine total carbon emissions.

• Indicators for SDG 9 (Targets 9.1 & 9.4)

  • Total urban rail transit mileage: This is a key indicator used in the “Public transport policy” scenarios to measure the expansion of sustainable infrastructure.
  • Share of new energy vehicles: The model includes variables like the “Share of New Energy Vehicles” and “Proportion of Private Electric Vehicles,” which are direct indicators of the adoption of clean technologies.
  • Fixed asset investment in rail transit: The model uses “Fixed Asset Investment” and the “Share of Rail Transit Investment” as drivers for infrastructure development.

• Indicators for SDG 11 (Targets 11.2 & 11.6)

  • Passenger volume of public transport (rail and bus): The article uses historical data on “urban rail transit passenger volume” and “Public Bus/Trolleybus Passenger Volume” for model validation, implying these are key metrics for a sustainable transport system.
  • Share of public transportation: The article mentions “increasing the share of public transportation” as a structural adjustment, making it an important indicator for progress towards Target 11.2.
  • Total carbon emissions from urban passenger transport: This is the primary output variable of the entire study, serving as a direct indicator of the environmental impact of the city’s transport system.

• Indicators for SDG 13 (Target 13.2)

  • Percentage reduction in carbon emissions: The article quantifies the effectiveness of different policy scenarios by the percentage of emission reduction achieved (e.g., “public transport and energy technology policies (11.32%)”). This directly measures the impact of climate policies.
  • Carbon emissions by transport mode: The study breaks down emissions by public buses, taxis, and rail transit, allowing for targeted policy-making to mitigate climate impact.

4. Table of SDGs, Targets, and Indicators

Source: nature.com