Simulation of urban transport carbon dioxide emission reduction environment economic policy in China: An integrated approach using agent-based modelling and system dynamics

Wang, Huihui; Shi, Wanyang; He, Wanlin; Xue, Hanyu; Zeng, Weihua

doi:10.1016/j.jclepro.2023.136221

Cited by 21 publications

(6 citation statements)

References 31 publications

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“…Promoting green transportation, sustainable supply chains, and expanding investments and support for renewable energy are essential steps. Thirdly, optimizing public transportation systems and encouraging greater use of public transport to reduce private vehicle usage and decrease traffic pollution are crucial ( Wang et al, 2020a ; Wang et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal characteristics and co-effects of air quality and carbon dioxide emissions changes during the COVID-19 epidemic lockdown measures in China

Liang

Wang

Xue

et al. 2023

Journal of Cleaner Production

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal characteristics and co-effects of air quality and carbon dioxide emissions changes during the COVID-19 epidemic lockdown measures in China

Liang

Wang

Xue

et al. 2023

Journal of Cleaner Production

Self Cite

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“…Consequently, our research contributes to the development of more refined low-carbon passenger and freight transportation, as well as public transit planning strategies. Regarding the development of low-carbon transportation efficiency in Beijing, it is necessary to promote new transportation modes, such as autonomous vehicles, shared mobility, and intelligent transportation systems, to enhance transportation efficiency and reduce CO 2 emissions from the sector [58].…”

Section: Forecast Under Multiple Scenariosmentioning

confidence: 99%

“…Previous studies on the CO 2 emission forecast of the transportation sector in Beijing mainly focus on the changes in energy consumption structure, predictions of CO 2 emissions, and comparisons of CO 2 emission reduction effects under different transportation policy scenarios, such as public transportation electrification substitution [51], vehicle purchase restrictions and promotion of new energy vehicles [52], growth in motor vehicle ownership [53], license plate restrictions and lottery [54], and various policy combinations [55]. Additionally, micro-level research has been conducted on the CO 2 emissions from daily transportation [56] and commuting emissions forecasts [57,58]. However, few studies have examined the overall CO 2 emission forecast of the transportation sector in Beijing, and there are also issues with data timeliness [59,60], which has limitations in reflecting the changing trends and peak predictions of Beijing's transportation CO 2 emissions in recent years.…”

Section: Introductionmentioning

confidence: 99%

Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model

Yang,

Dong,

Ren

et al. 2024

Sustainability

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The transportation sector plays a pivotal role in China’s efforts to achieve CO2 reduction targets. As the capital of China, Beijing has the responsibility to lead the era’s demand for low-carbon development and provide replicable and scalable low-carbon transportation development experience and knowledge for other cities in China. This study calculates the CO2 emissions of the transportation sector in Beijing from 1999 to 2019, constructs an extended STIRPAT model (population, affluence, technology, and efficiency), employs ridge regression to mitigate the effects of multicollinearity among the eight indicators, reveals the extent and direction of influence exerted by different indicators on CO2 emissions, and predicts the development trends, peak times, and quantities of transportation CO2 emissions in nine scenarios for Beijing from 2021 to 2035. Finally, adaptive low-carbon planning strategies are proposed for Beijing pertaining to population size and structure, industrial layout optimization, urban functional reorganization and adjustment, transportation infrastructure allocation, technological research and promotion, energy transition planning, and regional collaborative development. The results are as follows: (1) The total amount of CO2 emissions from Beijing’s transportation sector exhibits a trend of gradually stabilizing in terms of growth, with a corresponding gradual deceleration in the rate of increase. Kerosene, gasoline, and diesel are the main sources of transportation CO2 emissions in Beijing, with an annual average proportion of 95.78%. (2) The degree of influence of the indicators on transportation CO2 emissions, in descending order, is energy intensity, per capita GDP, population size, GDP by transportation sector, total transportation turnover, public transportation efficiency, possession of private vehicles, and clean energy structure. Among them, the proportion of clean energy structure and public transportation efficiency are negatively correlated with transportation CO2 emissions, while the remaining indicators are positively correlated. (3) In the nine predicted scenarios, all scenarios, except scenario 2 and scenario 4, can achieve CO2 emission peaks by 2030, while scenarios 7 and 9 can reach the peak as early as 2025. (4) The significant advancement and application of green carbon reduction technologies have profound implications, as they can effectively offset the impacts of population, economy, and efficiency indicators under extensive development. Effective population control, sustainable economic development, and transportation efficiency improvement are viable means to help achieve carbon peaking and peak value in the transportation sector.

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“…The deployed modeling process consists of both system dynamics (SD) and agent-based models (ABMs) [84]. ABM and SD methods are widely used throughout the literature in areas like supply chain sustainability, economics, environment, and other fields [85,86]. Their application aids the decision-making processes of businesses since these methods provide valuable information regarding the various factors of firms' internal and external environments.…”

Section: Hybrid Model and Ai Proceduresmentioning

confidence: 99%

Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling

Giannakopoulos,

Terzi,

Sakas

et al. 2024

Information

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Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms’ digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector’s employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications.

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Simulation of urban transport carbon dioxide emission reduction environment economic policy in China: An integrated approach using agent-based modelling and system dynamics

Cited by 21 publications

References 31 publications

Spatiotemporal characteristics and co-effects of air quality and carbon dioxide emissions changes during the COVID-19 epidemic lockdown measures in China

Spatiotemporal characteristics and co-effects of air quality and carbon dioxide emissions changes during the COVID-19 epidemic lockdown measures in China

Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model

Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling

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