Transport CO2 Emissions Research Articles

Traffic, environmental and economic optimization of the recycling of concrete waste

The recycling of concrete waste is becoming increasingly important as a contribution to conserving resources and reducing mineral construction waste. Currently, concrete is mainly reused in a subordinate application area (downcycling), such as earthworks or road construction, or disposed (Kreislaufwirtschaft Bau 2021). Due to this, natural raw materials have to be mined for the production of new concrete, although there are processing techniques that allow the use of concrete waste for concrete production.This paper deals with the optimization of plant locations for the recycling of concrete waste. Taking the city of Cologne as an example, the recycling process is balanced with regard to the use of natural resources and CO2 emissions of the freight transport. This makes an important contribution to the conservation of natural resources and to climate protection.

Calculating heavy-duty vehicle CO2 emission reduction costs for Green Deal scenarios: Extension of the DIONE model

The transport sector is one of the highest greenhouse gas emitters in the EU. The JRC's DIONE model calculates the costs for road transport CO2 emission reduction, based on functions associating vehicle emission reduction with their costs (cost curves). This paper describes the development of cost curves for a wide range of additional powertrains and classes of trucks, busses and coaches, and trailers. The model extension is a basis for analyzing the socio-economic impacts of reducing CO2 emissions from heavy-duty vehicles.

Comparison of Research Topics on Transportation Decarbonization Between Asian and Non-Asian Regions: Using Topic Modeling and Machine Learning Algorithms

The growing global interest to decarbonize the transportation industry has resulted in numerous scientific publications. This study reviews the rapidly expanding body of research and identifies the knowledge gaps in transport decarbonization between regions. This study employs a hybrid approach combining topic modeling and machine learning to identify research topics and their knowledge structures, and then compares the main debated topics between Asia and non-Asian regions. A dataset of 777 articles, including 410 Asian and 367 non-Asian articles, published between 1990 and 2022 was extracted from the Scopus database. The latent Dirichlet allocation topic modeling results showed that five potential topics were derived from Asia, while six were derived from non-Asian regions, and the knowledge structure of each topic differed between the two regions. The K-nearest neighbor machine learning algorithm results indicated a 92% accuracy for Asian topics and an 89% accuracy for non-Asian topics. The findings suggest that the Asian studies focused on “energy use in transportation” and “drivers of CO2 emissions in transportation,” while the non-Asian studies focused on “electric vehicles” and “fuel consumption.” This paper will keep academics and practitioners updated on the paradigm shift in the research on transportation decarbonization.

Design of a robust waste recycling network integrating social and environmental pillars of sustainability

The design of an efficient Municipal Solid Waste (MSW) management network can reduce the network costs, such as investment, operational and recycling costs, and improve its sustainability, from economic to environmental and social perspectives. In this paper, a multi-objective scenario-based robust stochastic optimization model for designing a sustainable MSW management network under uncertainty is proposed. The proposed model has four objectives to seek sustainability from two quantitative and qualitative aspects. Considering the dynamicity of the factors affecting an MSW management network as well as the multiplicity of sustainability perspectives, the proposed model allows, on one hand, reaching a robust solution considering the potential scenarios, and on the other hand, integrating sustainability indicators while creating a balance between the quantitative and qualitative evaluation of such indicators. Moreover, the waste treatment technologies, as the highest added-value echelon of the MSW management network, which also distinguishes the network from a generic waste recycling network, has been investigated in the model. Finally, fuel consumption, have been particularly emphasized as critical factor, highly contributing to transportation costs and Co2 emission that are decisive criteria from both economic and environmental points of view. The proposed model and solution approach are validated through a real case study.

Comparing the Sustainability of Multimodal Transportation Between China and Europe

The China-Europe trade trend and sustainability issues are rising the global attention. This paper will explore six alternative routes for sending goods from Shenzhen, China to Budapest, Hungary; analyze the current and future sustainability issues of these routes under the Paris Agreement. The core of this paper is the calculation of carbon dioxide emissions of each route, summarized the policies of countries along the routes and predicts the future carbon dioxide emissions of the routes. based on these policies, the modeling in this paper improved the original method in the 2016 IPCC Guidelines for National Greenhouse Gas Inventories to do calculations. This paper produced three conclusions. Firstly, CER express can definitely reduce the CO2 emissions of the China Europe international transport. Secondly, if the current policies of countries along the route are implemented smoothly in the future, the carbon dioxide emission reduction of transport between China and Europe has been calculated. Thirdly, calculation based on current proportion of each route, forecast the future China- Europe shipments could be at least doubled under the Paris agreement.

Do transportation taxes promote pro-environmental behaviour? An empirical investigation.

The transportation sector is a crucial driver of energy intensity and environmental degradation. Therefore, we aim to explore the nexus of transportation taxes, energy intensity, and CO2 emissions for the BICS economies. The econometric approaches, CS-ARDL and PMG-ARDL, have been employed to compute the estimates. The long-run estimates of the green transportation tax variable are negatively significant in both energy intensity and CO2 emissions models irrespective of the estimation technique. These findings imply that green transportation taxes help reduce energy intensity and CO2 emissions in BICS economies. Conversely, in the short-run, the effects of transportation taxes on energy intensity and CO2 emissions are mixed and inconclusive. Hence, transportation taxes are necessary to keep the polluters under control not only from the transport sector but also serve as a deterrent for other sectors as well.

Spatial-temporal characteristics and decoupling effects of China's transportation CO2 emissions.

Both the realization of the "double carbon" goal and the low-carbon economy development requires a focus on transportation CO2 emissions. Calculating Chinese transportation CO2 emissions and exploring its principles are essential for achieving high-quality development of the transportation industry. Firstly, we use a "top-down" method to assess carbon emissions from transportation operations from 2003 to 2019. Secondly, the study decomposes the influencing factors of transportation CO2 emissions in China using the log-average weight decomposition method. Thirdly, the Tapio decoupling model is applied to study the decoupling effect of transportation CO2 emissions in each province of China. The findings suggest that China's transport carbon emissions are growing at an annual rate of roughly 16%. All GDP per capita, transportation energy intensity, and population size increase the growth of transportation CO2 emissions. Contrastly, energy use per unit of turnover and transportation intensity decrease the growth of transportation CO2 emissions. There is much variation in China's carbon emission decoupling index from year to year. Policy recommendations are proposed in response to the study of the above findings and the differences in carbon reduction potential among provinces.

Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management

Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers' trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China's leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day's total passenger transportation CO2 emission is about 36,435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40,000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h·km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to support green transportation policies (e.g., green travel rewards and carbon credits in transportation) have been discussed.

Narrowing fossil fuel consumption in the Indian road transport sector towards reaching carbon neutrality

Road transportation accounts for 56% of India's transportation sector's CO2 emissions. Reaching carbon neutrality before 2070 requires the deep decarbonization of this sector. This study assesses the potential of battery electric vehicles (BEV) and fuel-cell vehicles (FCV) as the least-cost pathway toward carbon neutrality. We estimate the future demand for passenger and freight services and evaluate the impact of EV policies using the Integrated Model of Energy, Environment, and Economy for Sustainable Development/Technology (IMED/TEC). The study covers road transport emissions, energy, and air pollution transitions under four scenarios, including reference, low, medium, and high penetration of BEV and FCV, which align with the decarbonization target agreed upon under India's national policy. Results show that the carbon neutrality target would be challenging with BEV alone in road passenger and freight transportation because it is less efficient. Combined penetration of BEV and FCV can reduce air pollutant emissions significantly. Operative implementation of FCV could diminish more than 96% of the total road transport CO2 emissions. The analytical framework also proposes local climate change policies towards a carbon neutrality strategy to escalate the share of BEV and FCV in the Indian road transport sector.

The importance of the functional mixed entropy for the explanation of residential and transport CO2 emissions in the urban center of China

The influence of urban spatial form on the environment is complex and lengthy. The spatial analysis for the urban form and residential-related CO2 emissions at the city scale is challenging due to the lack of extensive urban form data and fine-grain CO2 emission maps. This research uses remote sensing data and downscaling interpolation to generate residential and transport (RTCE) maps in 130 m spatial resolution of urban center regions from 31 major cities in China, then investigates the relationship between 3 types of urban form indicators (Internal characteristics, external morphology, and development intensity) and RTCE through Geographical Weighted Regression method. The results reveal that urban form indicators could explain about 45.9% of RTCE. The 2D building shape indicator has the second greatest positive impact as the external morphology indicator, which complicates the influence. The internal characteristics indicators have relatively strong influences than the development intensity indicators. For instance, the influence of functional mixed entropy (FME) is the greatest positive influence and decreases exponentially with FME increases. Therefore, cities with the FME lower than the threshold (0.28) should increase it appropriately, while cities with the FME around and larger than 0.28 should maintain and probably reduce it.

Forecast of Transportation CO2 Emissions in Shanghai under Multiple Scenarios

A reduction in CO2 emissions from transportation is of great significance to achieve the goal of “peak carbon and carbon neutrality” in China. For 2003–2019, this paper calculates the transportation CO2 emissions in Shanghai and constructs an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model for forecasting. The result shows that from 2003 to 2019, total and per capita CO2 emissions from Shanghai’s transportation sector increased, but the rate of growth decreased. Oil consumption was the main source of emissions, accounting for more than 92%. The study extended the STIRPAT model to analyze the driving factors for emissions. It shows that population size, passenger turnover, per capita GDP, transportation intensity, and energy intensity are positively correlated with emissions. Energy structure (the proportion of clean energy) has a negative impact, restraining growth. Under multiple scenarios, the forecast shows that Shanghai’s transportation sector can reach a CO2 emissions peak before 2030. However, overgrowth of the transportation sector should be avoided. Progress in green and low-carbon technology is particularly important to achieve China’s peak carbon goal. Shanghai should actively build an efficient green transportation system, continue to optimize the transportation energy structure, and promote green and low-carbon travel for residents.

Transport infrastructure, CO2 emissions, mortality, and life expectancy in the Global South

This study examines the interplay between transport infrastructure, CO2 emissions, and health outcomes. The study focuses on economies in the Global South from 2006 to 2016 periods. To achieve the study's aims, CO2-induced health outcomes are generated, and the effects of transport infrastructure on CO2-induced life expectancy and mortality are assessed. The instrumental variable and system GMM estimates suggest that the quality of transport infrastructure in these economies has not improved the reduced life expectancy induced by CO2 emissions. A similar conclusion follows when mortality is analysed, such that transport infrastructural quality in these economies worsens the death rate caused by CO2 emissions. Further improvement in infrastructural facilities and a reduction in CO2 emissions will be necessary for achieving a good health outcome.

How can current German and EU policies be improved to enhance the reduction of CO2 emissions of road transport? Revising policies on electric vehicles informed by stakeholder and technical assessments

The electrification of the car fleet is an essential transformation to a meaningful reduction of greenhouse gas emissions in road transport. This has been a major goal of European transport policies, but other actions can also enhance the effectiveness of EVs to reduce emissions. In this paper we analyse four key European and German transport policies and assess how these could be improved to increase their potential to reduce emissions. Using iterative feedback from 12 interviews across various stakeholder groups, we have developed proposals for revised policies on electric vehicles. The results show that current policies in the EU and Germany are not making use of the full environmental potential of EVs, because they do not differentiate sufficiently between different EVs, and have been designed for the era of combustion vehicles. We suggest that the introduction of a new Bonus-Malus Registration Scheme and the overhaul of the existing Road Tax System are the most promising changes both in terms of their potential to reduce emissions and their likelihood of adoption.

Testing the Presence of Outliers in Regression Models*

We propose two sets of tests for the overall presence of outliers in regression models. First, ‘simple’ tests on whether the proportion and the number of detected outliers deviate from their expected values. Second, ‘scaling’ tests on whether the proportion of outliers decreases with the cut‐off used to detect outliers. We apply our tests to a panel difference‐in‐differences model of transport CO2 emissions in response to the introduction of North America's first major carbon tax. Our tests show the presence of significant outliers in the un‐taxed control group, which results in an overestimation of the estimated impacts of the tax.

The nonlinear effect of land freight structure on carbon emission intensity: new evidence from road and rail freight in China.

The extensive literature has debated the varying effects of factors on carbon dioxide (CO2) emissions. However, it has paid little attention to land freight structure (FS), including road and rail freight share, which may have different effects on CO2 emissions. Based on the data from 6 eastern provinces in China during 2005-2019, the panel threshold model is used to explore the dynamic influence mechanism of road and rail freight share on transport carbon emission intensity (CE), respectively. The results show different nonlinear relationships between the share of road and rail freight and transport carbon emission intensity. First, the effect of road freight share on carbon emission intensity is all positive across different stages of trade openness, while such effect goes through a process of increasing and then decreasing with the level of trade openness improving. Second, the driving effect of rail freight share on carbon emission intensity exhibits a "negative-positive-negative" feature as the level of trade openness increases. Third, trade openness generates a double-threshold effect on carbon emission intensity. The differentiated nonlinear effects provide significant evidence of the modal shift from road to rail freight, which would be effective to alleviate transport CO2 emissions.

Uncovering freight corridors’ embodied CO2 responsibilities: Evidence from the Yiwu-Ningbo corridor, China

The ability to accurately estimate and identify the hotspots (top influencing factors) of freight embodied carbon dioxide (CO2) emissions is essential for introducing efficient control strategies. However, most existing studies ignore the life cycle and secondary activities of CO2 emissions in freight transportation. This research gap hinders policy makers from having a more complete understanding of hotspots, which should be focused upon to efficiently reduce CO2 emissions. Hence, a generalized framework for the complete estimation and hotspot identification of freight embodied CO2 emissions is constructed. This framework is also applicable to different regions and countries. Then, the proposed framework is applied to the Yiwu-Ningbo case, and the results indicate that: (1) the studied road transportation chain generates 9.25% higher embodied CO2 emissions than the studied rail transportation chain when transporting one container, indicating the small marginal influence of modal shift measures in terms of reducing embodied CO2 emissions. (2) Supply-side hotspots, especially the direct CO2 emissions of road transportation, should be focused upon, in order to effectively reduce embodied CO2 emissions. (3) To effectively reduce the embodied CO2 emissions of a freight corridor, the effectiveness and implementability of reduction measures should be considered in a balanced manner.

The carbon footprint of cold chain food flows in the United States

Abstract The food system is an important contributor to carbon dioxide (CO2) emissions. The refrigerated food supply chain is an energy-intensive, nutritious and high-value part of the food system, making it particularly important to consider. In this study, we develop a novel model of cold chain food flows between counties in the United States. Specifically, we estimate truck transport via roadways of meat and prepared foodstuffs for the year 2017. We use the roadway travel distance in our model framework rather than the haversine distance between two locations to improve the estimate for long-haul freight with a temperature-controlled system. This enables us to more accurately calculate the truck fuel consumption and CO2 emissions related to cold chain food transport. We find that the cold chain transport of meat emitted 8.4 × 106 t CO2 yr−1 and that of prepared foodstuffs emitted 14.5 × 106 t CO2 yr−1, which is in line with other studies. Meat has a longer average refrigerated transport distance, resulting in higher transport CO2 emissions per kg than processed foodstuffs. We also find that CO2 emissions from cold chain food transport are not projected to significantly increase under the temperatures projected to occur with climate change in 2045. These county-level cold chain food flows could be used to inform infrastructure investment, supply chain decision-making and environmental footprint studies.

Modeling the Baltic countries’ Green Transition and Desynchronization from the Russian Electricity Grid

In the next ten years, the Baltic countries ― Estonia, Latvia, and Lithuania ― are planning large investments in renewable power generation and transfer capacity, substantial phase-out of fossil-based power generation, and desynchronization from the Russian electricity grid. In this article, the operational impacts of these changes on the Baltic energy system from 2017 to 2030 are studied with an open-source Backbone energy system model. The operation of Estonian, Latvian and Lithuanian power and heat, transport, and building sectors are optimized simultaneously on an hourly level, and results are analysed with operational, environmental, economic, and security indicators. Results suggest that the planned transition would support Baltic targets in renewable generation (from 45% to 92%) and self-reliance (2.3 TWh increase in domestic power generation and 5.5 TWh decrease in natural gas imports) with a moderate impact on system costs. However, an increase in transport CO2 emissions could risk national non-ETS targets. The hourly operation of the system, with a high share of wind and solar, is based on active use of storages and interconnectors. Model results raise concerns about the amount of Estonian dispatchable capacity, the commercial feasibility of Latvian natural gas CHP’s, and the high ramping rates of Lithuanian interconnectors.

The Impact of Public Transportation on Carbon Emissions—From the Perspective of Energy Consumption

Background: Transportation has become the second-largest source of global carbon emissions. Promoting low-carbon development by means of public transport and green travel and analyzing the mechanism and path of the carbon emissions reduction effect of public transport have become key to reducing carbon emissions in the transportation field and achieving “carbon peak and carbon neutrality”. Methods: The data from 30 provinces (2010–2019) were extracted from China Emission Accounts and Datasets (CEADs), China Energy Statistical Yearbook, China Statistical Yearbook, and China Automobile Statistical Yearbook. The two-way fixed-effect model was used to explore the carbon emissions reduction effect of public transport development level. The mediating-effect model was used to verify the transmission role of energy consumption in the carbon emissions reduction effect of public transport development level. Results: The study suggests that the public transport development level and CO2 emissions are negatively correlated, showing an “Inverted U-shaped” curve relationship. Energy consumption is the transmission path of the carbon emissions reduction effect of public transport development level. The public transport development level adjusts the energy consumption structure through the traffic substitution effect, energy input optimization effect, and industrial structure optimization effect and then acts on carbon emissions. Moreover, the contribution rate of energy consumption is about 4.22%. In addition, regional heterogeneity is present in the transmission path of the carbon emissions reduction effect of public transport development level based on energy consumption. The carbon emissions reduction effect of public transport development level is more significant in the central and western regions than the eastern and northeast regions of China. Conclusion: The transmission mechanism of energy consumption in the carbon emissions reduction effect of public transport is worthy of attention. To promote low-carbon and circular development in the transportation sector, it is urgent to accelerate the green upgrading of transportation infrastructure, promote the low-carbon transformation of energy production and consumption, promote carbon emissions reduction in public transport, and strengthen the linkage regulation between effective government and an effective market.

CO2 Emission Reduction Potential of Road Transport to Achieve Carbon Neutrality in China

Under the targets of peaking CO2 emissions and carbon neutrality in China, it is a matter of urgency to reduce the CO2 emissions of road transport. To explore the CO2 emission reduction potential of road transport, this study proposes eight policy scenarios: the business-as-usual (BAU), clean electricity (CE), fuel economy improvement (FEI), shared autonomous vehicles (SAV), CO2 emission trading (CET) (with low, medium, and high carbon prices), and comprehensive (CS) scenarios. The road transport CO2 emissions from 2020 to 2060 in these scenarios are calculated based on the bottom-up method and are evaluated in the Low Emissions Analysis Platform (LEAP). The Log-Mean Divisia Index (LMDI) method is employed to analyze the contribution of each factor to road transport CO2 emission reduction in each scenario. The results show that CO2 emissions of road transport will peak at 1419.5 million tonnes in 2033 under the BAU scenario. In contrast, the peaks of road transport CO2 emissions in the CE, SAV, FEI, CET-LCP, CET-MCP, CET-HCP, and CS scenarios are decreasing and occur progressively earlier. Under the CS scenario with the greatest CO2 emission reduction potential, CO2 emissions of road transport will peak at 1200.37 million tonnes in 2023 and decrease to 217.73 million tonnes by 2060. Fuel structure and fuel economy contribute most to the emission reduction in all scenarios. This study provides possible pathways toward low-carbon road transport for the goal of carbon neutrality in China.