A Panel Investigation of High-Speed Rail (HSR) and Urban Transport on China’s Carbon Footprint

Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas.

Can the opening of high-speed rail boost the reduction of air pollution and carbon emissions? Quasi-experimental evidence from China

Impact of high-speed rail on the operational capacity of conventional rail in China

The impact of high-speed rail and low-cost carriers on European air passenger traffic

In order to provide better transportation systems, Indonesian Government is planning to develop a new high-speed rail system in Java that will connect two biggest cities in Java Island, Jakarta and Surabaya, with approximately 685 kilometers of entirely new track. This paper reviewed the Indonesian Government’s plan to develop the high-speed rail in term of comparison to existing modes of transport. This study employs demands projection of high-speed rail using JETRO method and benchmarking from other countries’ high speed rails. Furthermore, air pollution caused by transport mode was calculated based on the emission factor from CACP & CNT. The last is generalized cost that considers total time to travel as value of money. It can be concluded that journey time and fare of the high-speed rail is very competitive to the air transport in Jakarta-Surabaya corridor. The journey time to travel from Jakarta to Surabaya is 4 hours and 19 minutes by high-speed train and 4 hours and 40 minutes by air. Based on the benchmarking analysis, the suitable fare for the high-speed rail should be 70% of the air transport. This study predicted that 61% of air passenger, 18% of conventional rail passenger and 12% of bus passenger will switch to the high-speed rail service in 2020. In total, the high-speed rail will have 24% of market share for the passenger transport and becomes the second largest market share after road transport (52%). The conventional rail and air transport have 14% and 9% of total market share to travel from Jakarta to Surabaya and vice versa. The high-speed rail development reduces carbon emissions caused by transportation systems in Java Island. It has been calculated that there are 2.542 million tonnages of CO2 per annum without introducing high-speed rail, however, the CO2 emissions decrease to 1.694 million tonnages per annum if the high-speed rail is developed in Java Island. Generalized cost of the high-speed rail is higher than road and conventional rail. However, it is lower than air transport. Keywords: Java high-speed rail, HSR Comparison, modal share, journey time

Are conventional train passengers underserved after entry of high-speed rail?-Evidence from Chinese intercity markets

Implementing carbon mitigation through urban spatial optimisation is a possible solution for alleviating global warming. However, the relationship between urban carbon emissions and urban spatial structure has not been well clarified, as adequate mapping of high-spatial-resolution urban carbon emissions from different sectors (particularly residential sectors), a precondition to solving the problem, has yet to be achieved. This study proposes a hybrid method of mapping the spatial distribution of urban residential carbon emissions on a 1 km × 1 km scale using multi-source data and exemplifies it via a case study of the Chinese city of Suzhou. The purpose of using this method is to differentiate residential carbon emissions by commuter population and home-based population, as the time they spend at home differs. The mobile signalling data of Suzhou were used to identify commuter and home-based populations. The number and spatial distribution of these two groups were then calibrated by referring to land use and O-D data. Using calibrated data, the proportion of electricity consumed by the two groups in different residential districts across the city was calculated. Total urban residential carbon emissions were then proportionally allocated to 1 km × 1 km grids. By validating estimated data against the data from the Statistical Yearbook, we found that the proximity level is higher than 93%. Furthermore, comparing these outcomes against the results estimated by using NTL data and the size of the identified population as the proxy data, it was observed that the results estimated by the hybrid method are of higher accuracy and stability.

ObjectiveThis study recalculates the carbon emissions of urban and rural residents in China, analyzing the dynamic evolution trends of urban and rural carbon emissions. It explores the spatial spillover effects centered around the inequality in carbon emissions between urban and rural areas.MethodsThe study calculates the carbon emissions of urban and rural residents in each province based on the IPCC method. Non-parametric kernel density estimation is employed to depict the dynamic evolution characteristics of national, urban, and rural carbon emissions. The Theil Index is used to measure the disparities in urban and rural carbon emissions in major strategic regions, further applying the Theil Index to evaluate the inequality of urban and rural carbon emissions across provinces. This helps identify the driving factors affecting the inequality of urban and rural carbon emissions and their spatio-temporal effects.FindingCarbon emissions from urban and rural residents in China present a divergent development pattern. Urban emissions have increased, with inter-provincial disparities widening; rural emissions tend to stabilize, with slight growth in inter-provincial gaps. The overall inequality of carbon emissions in various regions of China experiences a three-phase journey of rise, decline, and stabilization. Urban inequality first increases then decreases, while rural inequality gradually lessens, showing clear regional and urban-rural differences. Market and government factors significantly impact the inequality of urban and rural carbon emissions. The development of the digital economy aids in reducing inequality and generates significant spatial spillover effects. The relationship between economic development level and carbon emission inequality is U-shaped. Industrial structure optimization can reduce urban-rural inequality, but its spatial spillover effect is not significant. Government intervention has limited effects, while environmental regulations may increase inequality. Opening up to the outside world helps reduce inequality, and the impact of population density is complex.

When green transportation backfires: High-speed rail's impact on transport-sector carbon emissions from 315 Chinese cities

PDF HTML阅读 XML下载 导出引用 引用提醒 长三角城市群碳排放与城市用地增长及形态的关系 DOI: 10.5846/stxb201707101242 作者: 作者单位: 浙江大学公共管理学院土地科学与不动产研究所,浙江大学公共管理学院土地科学与不动产研究所,浙江大学公共管理学院土地科学与不动产研究所,浙江大学公共管理学院土地科学与不动产研究所,浙江大学环境与资源学院农业遥感与信息技术应用研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上项目（41771244）；国家留学基金（201706320200）；中央高校基本科研业务费专项资金资助；浙江大学文科教师教学科研发展专项项目 Relationships between carbon emission, urban growth, and urban forms of urban agglomeration in the Yangtze River Delta Author: Affiliation: Institute of Land Science and Property, School of Public Affairs, Zhejiang University,,Institute of Land Science and Property, School of Public Affairs, Zhejiang University,, Fund Project: National Natural Science Foundation of China（Grant No.41771244）; China Scholarship Council (Grant No.201706320200); supported by “the Fundamental Research Funds for the Central Universities”; supported by “the Teaching and Research Development Funds for Humanities and Social Sciences of Zhejiang University” 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市是一种重要的碳源，城市扩张过程中的用地面积增长和空间特征变化均会影响城市碳排放。分析1995-2015年长三角城市群碳排放重心转移，查明碳排放和城市用地增长的脱钩状态时空变化，并通过构建面板数据模型探究城市形态对碳排放的影响，得出以下结论：（1）1995-2015年长三角城市群碳排放重心经历了西南向-西北向-东南向-西北向的转移过程，这种转移过程与其相应时期内部分城市的工业发展与产业结构调整有关；（2）1995-2015年，长三角城市群碳排放与城市用地增长的脱钩状态存在着显著的时空异质性。研究区由以扩张负脱钩为主变化为以弱脱钩为主，2005年以后，区域之间的脱钩差异开始缩小，总体来看研究区脱钩状态趋向于同质。至2015年，近70%的城市已达到了脱钩，其中上海等城市实现了强脱钩；（3）连续完整的地块在区域内的主导程度会对城市碳排放产生负向的影响，而城市用地斑块的破碎化程度和聚集程度对碳排放有着正向的影响，且相对而言，聚集程度的正向影响更为显著。 Abstract:Cities are one of many carbon sources. According to the Intergovernmental Panel on Climate Change (IPCC) AR5, CO2 emissions from fossil fuel combustion and industrial processes contributed about 78% to the total Green House Gas (GHG) emission increase between 1970-2010. Total annual anthropogenic GHG emissions have increased by about 10GtCO2-eq between 2000-2010. The increase directly came from energy (47%), industry (30%), transport (11%), and building (3%) sectors, which mainly exist in cities. Urban expansion and urbanization can affect urban carbon emission. Studies show that there is a long-term and stable relationship between urbanization and carbon dioxide emissions. The relationships between urban carbon emissions and indicators, including urban development intensity, urban land use, and the industrial sector, are studied extensively. During urban expansion, the quantitative and spatial features of urban lands can both affect carbon emissions. Therefore, urban form was added to the possible factors influencing carbon emissions in this study, which may be different from previous research that has focused on the relationship between urban growth and carbon emissions. However, in some related research, when urban form has been added to the indicators, the objects were residents or the transport sector, and they lacked quantitative indicators to verify the conclusions. The definition of "urban form" in this study was landscape pattern which was characterized by landscape metrics, and the study area consisted of 13 cities in the Yangtze River Delta. In this study, we analysed the shift of the gravity center from 1995-2015 for carbon emissions of the study area, and defined the decoupling index as well as analysing the temporal-spatial changes of the decoupling relationships between carbon emissions and urban growth in the study area. We also built panel data models to estimate the impact of urban forms on carbon emissions. Based on that, the conclusions are as follows:(1) The shift of the gravity center from 1995-2015 for carbon emissions of the study area was southwest-northwest-southeast-northwest. The shift may be related to the development of industry and change of industrial structure in some cities during this period. (2) There was a significant temporal-spatial heterogeneity in the decoupling relationships between carbon emissions and urban growth from 1995-2015. The leading decoupling relationship between carbon emissions and urban growth of the study area changed from expansive negative decoupling to weak decoupling from 1995-2015. The difference of decoupling relationships between cities narrowed after 2005 and the overall decoupling relationship of the study area became homogeneous. In 2015, almost 70% of cities reached the decoupling state and the decoupling states of Shanghai, Shaoxing, and Taizhou were strong. (3) Urban carbon emissions can be negatively influenced by the dominance of complete patches, and positively influenced by the degree of fragmentation and aggregation of urban patches. Carbon emissions can be more sensitive to the more aggregative distribution of the urban patches. This study analysed the relationship between carbon emissions and urban growth, as well as exploring how urban form can affect carbon emissions. The conclusions could provide scientific references for the policy making of low-carbon development strategies and land use and urban planning of urban agglomeration in the Yangtze River Delta. 参考文献 相似文献 引证文献

Achieving the "dual carbon" goal requires focusing on the issue of urban transportation carbon emissions. This study derives the mechanism of transportation infrastructure on urban carbon emissions and uses panel data from 284 cities in China from 2004 to 2017 as a basis for empirical analysis through the two-stage least squares method (2SLS). The results of the study show that the improvement of transportation infrastructure has a significant negative effect on the level of urban carbon emissions, and it is greater than the positive spillover of the increase in the number of motor vehicles on urban carbon emissions. Further, research has shown that improvement of transportation infrastructure has no significant impact on the purchase of motor vehicles; therefore, the transportation infrastructure will not affect the "induced traffic" through the purchase of motor vehicles, thereby further affecting the level of urban carbon emissions. The enlightenment of this article include the following: In urban planning and construction, attention should be paid to reducing urban carbon emissions by improving the construction of transportation infrastructure to help China achieve its carbon peak and carbon neutral goals at an early date.

In order to study the influence of urban green space landscape pattern on urban carbon emissions， nighttime lighting data， socioeconomic development data， and land use remote sensing data from 2000 to 2020 are used as the basis of analysis， and the three major coastal economically developed regions in China-Bohai Rim， Yangtze River Delta （YRD）， and Pearl River Delta （PRD） （nearly 100 cities in total） are used as the study area to analyze the spatial and temporal evolution characteristics of urban carbon emissions， as well as the influence of urban green space landscape pattern and its spatial and temporal changes. We also explored the influence of 10 urban green space landscape pattern indices on urban carbon emissions by using the random forest model and the Lasso regression model and further analyzed the four factors （number of patches， density of patches， dispersion of patches， and complexity of the shape of patches） that had a greater influence by using the spatio-temporal geographically weighted regression model， to explore the results of the spatial and temporal evolution of the influence of the urban green space landscape pattern on carbon emissions. The main findings of this study are as follows： ① Carbon emissions in the three study areas showed a slow growth trend， with the Bohai Rim showing a relatively fast growth rate. Carbon emissions were spatially aggregated in the selected study areas， with the majority of cities in the "high and high" agglomeration and the "low and low" agglomeration regions. There was spatial aggregation of carbon emissions in the selected study areas， with the majority of cities in "high and high" agglomeration and "low and low" agglomeration. The land-averaged carbon emissions in the three study areas were dispersed in all directions， with the economically strong cities as the core， and the overall carbon emission level was dispersed from the center to the surroundings. Additionally， along the rivers and coastal areas， carbon emissions were higher due to the concentration of ports， industrial zones， and cities. ② Landscape occupied by patches， number of patches， and density of patches had a significant negative correlation with urban carbon emissions， which indicates that the higher the number， density， and proportion of the landscape occupied by urban green space patches， the more it could hinder the growth of carbon emissions. On the contrary， the shape index and patch fragmentation index had a positive correlation with urban carbon emissions， indicating that the higher the shape complexity of urban green space patches and the higher the fragmentation degree of patches， the more it promoted the growth of urban carbon emissions. In addition， the aggregation index also showed a significant negative correlation with urban carbon emissions， which indicates that the higher the degree of aggregation of patches， the more it could inhibit the growth of carbon emissions. ③ The correlation between the green space landscape pattern index and carbon emissions showed significant spatial and temporal differences， with large changes around 2010. In the Bohai Rim Region， the influence of the urban landscape pattern index on carbon emissions remained relatively stable， and its influence over time generally showed a decline. In the YRD Region， the shape complexity and dispersion of urban green space had a greater impact on carbon emissions than the number of patches and patch density factors. However， on the contrary， in the PRD Region， the impacts of the number of urban green spaces and density index were increasing. In addition， the spatial influence changes on all showed the clustering of regression coefficients. The impact of urban green space on carbon emissions varied greatly across locations and time， suggesting that policy makers cannot rely on a one-size-fits-all approach to urban green space planning. In the Bohai Rim Region， it is more important to balance the distribution of urban green space with other land uses to maintain stability； in the YRD Region， highly fragmented and overly complex green space patch planning should be reduced； and in the PRD Region， priority should be given to increasing the amount and distribution density of urban green space.

Spatio-temporal distribution of Chinese cities’ air quality and the impact of high-speed rail

The research on urban carbon emissions based on the full life cycle assessment is an important basis for formulating collaborative urban emission reduction strategies. Although there are increasingly fruitful research results, the spatial scales, industrial areas, and research methods of different studies differed greatly. We reviewed literature in both Chinese and English between 1998 and 2024, summarized the research trends and disciplinary differentiation characteristics of multiscale urban carbon emissions from a life cycle perspective, and then compared the key factors and mechanisms of the life cycle of carbon emissions at the three scales of city, block, and building based on the method of knowledge graph analysis. As the research scale shifted from macro (cities) to micro level (buildings), the methods transitioned from input-output model-based analysis to life cycle-based analysis, and the factors affecting the life cycle of carbon emissions shifted from socio-economic and urban form characteristics to buil-ding functional forms, building materials, and structures. Finally, we explored urban collaborative carbon reduction strategies from three aspects: building a multiscale carbon emission life cycle data management platform, analyzing the dynamic evolution mechanism of the life cycle of urban carbon emissions, and achieving cross-regional and multi-sectoral carbon reduction collaborative management. These strategies would provide reference for low-carbon oriented urban sustainable development and the achievement of dual carbon goals.

The need for urban form data in spatial modeling of urban carbon emissions in China: A critical review