CO2 Emissions In Transport Sector Research Articles

Tourism Transport-Related CO2 Emissions and Economic Growth: A Deeper Perspective from Decomposing Driving Effects

This study explores the intricate dynamics of CO2 emissions stemming from transport within the tourism sector. It aims to unravel the multidimensional aspects of how transport-related tourism contributes to CO2 emissions and to elucidate the complex relationship between regional economic growth and CO2 emissions from transport-related tourism. Our study employed Logarithmic Mean Divisia Index (LMDI) and Panel Vector Autoregression (PVAR) models to analyze data from 30 Chinese provincial regions between 2010 and 2018. The tourism transport-related CO2 emissions were decomposed into four separate driving effects using the LMDI approach. Then, PVAR models were constructed to reveal dynamic interactions between each driving effect and per capita gross regional product (GRP). Our results demonstrate a decrease in both energy structure effect (tourism transport-related CO2 emissions from energy structure) and energy intensity effect (tourism transport-related CO2 emissions from energy intensity) during the pre-COVID decade. Notably, the positive impulse response of energy structure effect to per capita GRP is observed. However, we found no evidence of a cointegrated relationship between energy intensity effect and regional economic growth, although other factors demonstrated connections. These findings echo the necessity to integrate sustainable practices into the tourism transportation business, especially in the area of energy structure, in order to mitigate adverse environmental effects from tourism. This paper disseminates the main drivers of CO2 emissions in the tourism transport sector and their interrelationship with regional economic growth. It not only guides tourism policymakers in targeting efforts to reduce carbon footprints, but also sets a new benchmark for future studies on CO2 emissions.

Reducing transport sector CO2 emissions patterns: Environmental technologies and renewable energy

The research explores the impact of environmental technologies and renewable energy on carbon dioxide (CO2) emissions from the EU transportation sector (2007–2020). Utilizing panel corrected standard error and feasible generalized least squares methods, the study uncovers key drivers influencing declining CO2 emissions. The results reveal a significant and variable effect of environmental technologies and renewable energy on CO2 emissions in the EU transport sector, emphasizing the positive correlation between increased renewable energy adoption and emission reduction. This underscores the necessity for heightened EU investment in sustainable transport infrastructure and clean energy solutions, encompassing initiatives like electric vehicles, hydrogen fuel cells, and biofuels. The study further recommends promoting renewable energy sources for transport systems, aligning with the broader goals of the European Green Deal and the EU Climate Law. Additionally, the research provides essential insights into policy implications, emphasizing a multifaceted approach including comprehensive strategies for cleaner transportation, innovation, and education to accelerate the transition towards sustainable practices in the EU.

Urban street network design and transport-related greenhouse gas emissions around the world

This study estimates the relationships between street network characteristics and transport-sector CO2 emissions across every urban area in the world and investigates whether they are the same across development levels and urban design paradigms. The prior literature has estimated relationships between street network design and transport emissions – including greenhouse gases implicated in climate change – primarily through case studies focusing on certain world regions or relatively small samples of cities, complicating generalizability and applicability for evidence-informed practice. Our worldwide study finds that straighter, more-connected, and less-overbuilt street networks are associated with lower transport emissions, all else equal. Importantly, these relationships vary across development levels and design paradigms – yet most prior literature reports findings from urban areas that are outliers by global standards. Planners need a better empirical base for evidence-informed practice in under-studied regions, particularly the rapidly urbanizing Global South.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China.

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector

The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment.

Economic and environmental impacts of EVs promotion under the 2060 carbon neutrality target—A CGE based study in Shaanxi Province of China

As the world's largest emitter of CO2, China has committed to achieving carbon neutrality by 2060. For the high emission of the transport sector, the promotion of electric vehicles (EVs) is an important way to reduce CO2 emissions in the road transport sector. Due to the current high proportion of coal-fired power in Shaanxi province, the indirect emission of EVs is greater than the direct emission of fuel vehicles. In this study, two regional dynamic CGE models were constructed to evaluate the economic and environmental impact of EVs promotion and power generation structure optimization under the carbon neutral target in Shaanxi province. The results show that the promotion of EVs and optimization of the power generation structure will reduce the carbon emissions of electricity used in the transport sector from 4.5 million tons to 0.1 million tons. Compared with the baseline scenario, the NOX, SO2, and PM emission in the carbon neutrality scenarios has decreased by 50%, 65%, and 32%, respectively in 2060. Further, the GDP loss of carbon neutrality scenarios compared to the baseline scenario in 2060 is between 7.9% and 12.3%. Fortunately, the promotion of EVs and the promotion of the power generation structure can help to optimize the allocation of carbon emission allowance, which would alleviate the GDP loss caused by the carbon neutrality target. So it is necessary to encourage the development of EVs and vigorously develop renewable energy at the same time in other provinces with similar power structure like Shaanxi.

Investigating the Impact of Climate Change Mitigation Technology on the Transport Sector CO2 Emissions: Evidence From Panel Quantile Regression

Carbon emissions from the transport sector (COE) has witnessed unprecedented growth, which calls for special measures to control these emissions to achieve carbon neutrality by 2050. One of the measures taken to limit these emissions is climate change mitigation technology related to the transport sector (CCMT). However, there exists relatively scant literature that explores the CCMT-COE nexus. Hence, the present study explores whether CCMT curbs COE in the top transport sector carbon-emitting countries. For this purpose, we employ a panel quantile regression (PQR) approach, which probes the impact of CCMT on COE in low-, middle, and high-emission countries. The result shows that CCMT does not affect COE at the lowest quantile (i.e., 10th quantile) while CCMT plunges the emissions at all other quantiles. In particular, a 1% increase in CCMT curbs COE by 0.13% at the 25th, 50th, and 75th quantile, while a 1% upsurge in CCMT impedes COE by 0.22% at the 90th quantile. In the light of these findings, the carbon policy in the transport sector should invest in climate change mitigating technology in the transport sector and scale it up and out.

Impacts of regional development on emissions in China's transport sector.

The CO2 emissions in China's transport sector increased from 349.00 Mt in 2005 to 723.87 Mt in 2017. Thus, a number of climate change policies are being implemented to adjust regional structure and to decrease the emissions in China's transport sector at the regional level. However, few studies explored the impact of changes in regional structure (that is, measured regional share of the added value of transport sector) on emissions in China's transport sector. Therefore, based on the Kaya identity and LMDI analysis, we decompose 8 factors (including carbon intensity, energy structure, energy intensity, turnover intensity, transport intensity, regional structure, per-capita traffic activity, and population size) to analyze the driving factors of emissions in China's transport sector. The period 1997-2017 is divided into four phases according to the growth rate of emissions. The results show that regional structure increased CO2 emissions in China's transport sector between 2013 and 2017. The fast transport development in the Southwest region, reflected by the increase in the share of total transport value added, resulted in emissions growth during 2013-2017. Moreover, the change in the growth rate of the regional transport sector's value added is positively correlated with the change in the regional share of value added, which is positively correlated with the change in regional emissions.

Prediction of CO<SUB align="right">2 emission in transportation sector by computational intelligence techniques

Carbon footprint is considered the main cause of global warming. There are various studies on environmental sustainability carried out global scale. In this study, prediction models were developed for CO2 emissions in transportation sector. Artificial neural networks (ANN), simple membership functions and fuzzy rule generation technique (SMRGT), support vector machine (SVM) and adaptive neuro fuzzy inference system (ANFIS) methods, which are artificial intelligence techniques (AI), and also multiple linear regression (MLR), which is a statistical method, were used for the analysis. As a result of the comparison the best performance was seen in ANN model.

Analysis of Shared Electric Scooter Use in Riga

Abstract Shared electric micromobility has been becoming increasingly popular during last few years, especially in the context of Covid-19 and its impact on public transportation. Because of the electric propulsion, electric scooters are considered as potentially one of the ways how to decrease CO2 emissions in urban transportation sector. This article examines the electric scooter trip data obtained during 2021 scooter season in Riga city. The data analysis shows similarities between scooter trips in Riga and other cities, most notably the hourly trip distribution. There are differences however in many aspects, like average trip distance and duration; the largest difference being the slow average scooter trip speed in Riga, (5.4 km/h) which is comparable to fast walking. The observed significant variance between the trips indicates, that a systematic and holistic approach will be needed to use the scooter trip data making decisions about urban transportation and not relying on average trip data values. The findings from this article will add to understanding of urban transportation in North-Eastern Europe.

Transportation fuel subsidies and CO 2 emissions: The roles of economic complexity and uncertainty

Transportation fuel subsidies have been rising in several developing countries. In order to broaden our understanding of transportation fuel subsidies, many attempts have been made in the literature to investigate the impact of the transportation fuel subsidies. This is mostly because exposing the various negative effects of transportation fuel subsidies might stimulating the appropriate authorities to act against the growing transportation fuel subsidies. However, the impact of transportation fuel subsidies on pollution has not been adequately examined in the literature. This study investigates the determinants of environmental degradation with a focus on the role of transportation fuel subsidies in selected developing countries. The control variables considered in this study include economic complexity and uncertainty, which have not been sufficiently examined in the existing literature. Pollution from transport sector is used as an indicator of environmental degradation. The study adopts a dynamic panel data method, which offers more reliable results relative to analyses done via conventional methods. The empirical findings suggest that an increase in transportation fuel subsidies generate a rise in transportation sector CO2 emissions. Specifically, a 1% rise in transportation fuel subsidies per capita will increase the transport sector CO2 emissions by between 0.041% and 0.094%. The results further illustrate that an increase in population, real gross domestic product per capita or uncertainty raises environmental degradation, while a rise in economic complexity reduces environmental degradation. It is observed that the impact of transport oil subsidies per capita is less pronounced in countries with higher per capita income.

Costs and potentials of reducing CO2 emissions in China's transport sector: Findings from an energy system analysis

As China's transportation system expands it is producing a large amount of end-use emissions. To derive cost-effective measures for CO2 mitigation, the authors of this paper develop a marginal abatement cost (MAC) curve for China's transport sector. Using the TIMES model, MAC curves are derived which identify the linkages among all transport modes, considering, among other uncertainties, the impacts of a carbon peaking policy and driver preferences for plug-in hybrid electric vehicles (PHEVs). The results underline that the intercity passenger transport sector has a cost advantage, with a huge potential for emissions reduction (accounting for almost 38.8% of all CO2 emissions mitigation). The freight sector is another potentially significant contributor to CO2 reduction, underlining its vital role for emission mitigation in the longer run. The carbon peak policy, while making alternative fuel vehicles (AFV) more efficient, is found to increase the overall cost of emissions reduction. A higher ratio of electricity to fossil fuel use by PHEVs drivers would help the transport sector to reduce CO2 much faster. The sensitivity analysis of these and other factors is also estimated in this paper. The final MAC curves provide valuable insights for future low-carbon energy development strategies of China's transport sector.

Do environmental technologies help to reduce transport sector CO2 emissions? Evidence from the EU15 countries

A growing number of studies empirically investigate the nexus between CO2 emissions and technological progress in the existing literature. However, these studies largely ignore sectoral-level differences. We argue that as the contribution of different sectors to carbon emissions are not equal, the environmental impact of technological progress might vary across sectors. The main purpose of this paper is to fill this research gap for the transport sector. To this end, we specifically focus on the EU15 countries over the period between 1977 and 2015. In order to empirically analyze this link, we use the Mean Group, Common Correlated Effects Mean Group, and the Augmented Mean Group estimators. Unlike the earlier country-level empirical studies, our findings reveal that environmental technologies have a statistically insignificant positive effect on CO2 emissions from the transport sector. Moreover, while economic growth and energy consumption lead to more pollution, the effect of urbanization on emissions is not statistically significant. It is recommended that the policymakers of EU15 countries should pay special attention to the transport sector for becoming a carbon-neutral economy by 2050.

Achieving net-zero emissions in China’s passenger transport sector through regionally tailored mitigation strategies

As a major GHG emissions source with large growth potential, the passenger transport sector plays a crucial role in deep decarbonization in China. Large disparities among provinces in private vehicle ownership, sufficiency of public transport infrastructure, affordability of clean fuel vehicles, etc. highlight the importance of regionally tailored mitigation strategies to fully exploit carbon reduction potentials. We classify 31 provinces in mainland China into three regional clusters based on their passenger transport development level, then establish a provincial level bottom-up model to project energy demand and CO2 emissions of China’s passenger transport sector by 2050. Mitigation effects of improving vehicle energy efficiency, shifting to alternative clean fuels, and promoting public transport are compared, and regionally tailored policy priorities are then proposed. The results show that CO2 emissions of China's passenger transport sector will peak around 2045 at 647 MtCO2 and slightly declined to 642 MtCO2 in 2050 in the Current Policies Scenario. If fully implemented, regionally tailor mitigation strategies that maximize techno-economic carbon reduction potentials could cut CO2 emissions substantially to net-zero in 2050. Mitigation effects of different policy options vary among time periods and regions. Improving vehicle fuel efficiency contributes the most in carbon mitigation over short time scales especially in less developed provinces, where private vehicle ownerships are projected to increase rapidly. Well-established transport infrastructure and an optimally designed public transport system could play a larger role in wealthier provinces.

Potential gains of trading CO2 emissions in the Chinese transportation sector

The transportation sector is under high pressure to reduce their carbon emissions. Emission trading is widely used as a market-oriented policy instrument to reduce emissions and increase social and economic benefits. The present study introduces a new model for estimating the potential gains of CO2 emission trading for China’s transportation sector by simulating a trading market. This hypothesized trading market considers both price information and underlying emission trading process. The marginal abatement cost of transport sector’s CO2 emission is derived by designing a constrained maximum likelihood model with partial quantile order-α frontiers. Results showed an average marginal abatement cost of CO2 emission for China’s transport sector at around 1009$/tonne. The estimated potential gains of emission trading measured in monetary terms range between 2 and 22 billion dollars, accounting for 0.5% of the total profit generated by the transportation sector.

A blockchain-based emissions trading system for the road transport sector: policy design and evaluation

ABSTRACT Emissions trading is a cost-effective climate policy for reducing greenhouse gas emissions. It could also be useful for addressing road transport emissions, especially given that this sector is the largest CO2 emitter in the transportation sector and its emissions continue to increase. However, emissions trading for road transport (ETS-RT) has rarely been implemented due to its complexity. This paper designs a novel and practical policy framework for an ETS-RT based on advanced blockchain technology, including all related entities upstream, midstream and downstream of the road transport sector. First, the government determines the cap and allocates the initial permits. Then, fuel producers, vehicle manufacturers, and vehicle users are involved as regulated entities with tradable emission permits. They are responsible for the three determinants of CO2 emissions in the road transport sector: fuel emission factors, vehicle fuel economy, and vehicle miles travelled, respectively. With all the regulated entities collaborating on compliance, the three determinants can be synergistically optimized so that the efficiency of the emissions abatement can be maximized. In addition, all trading, monitoring, reporting, and verification of the emission permits are automatically executed and recorded via a smart contract deployed on a decentralized blockchain. This approach can dramatically reduce administrative costs, improve transparency and traceability, and eliminate double counting and fraud. Finally, the proposed policy was evaluated using a multicriteria analysis method compared with other possible ETS-RT approaches. Key policy insights Fuel producers, vehicle manufacturers, and vehicle users – who are respectively responsible for fuel emission factors, vehicle fuel economy and vehicle miles travelled – should be synergistically regulated in an ETS-RT to maximize the efficiency of emissions abatement. This can be enabled by advanced blockchain technology, which can eliminate the need for a central authority, while enhancing transparency, traceability and cost-effectiveness. Blockchain technology could also be useful for monitoring, reporting and verification under the Paris Agreement. A blockchain-based ETS-RT is found to outperform other forms of ETS on criteria of acceptability, feasibility and environmental performance.

Co-benefits of reducing CO2 and air pollutant emissions in the urban transport sector: A case of Guangzhou

The transport sector has become an important source of urban greenhouse gas (GHG) and air pollutant emissions, with the continuous growth of transportation demand. The co-benefits of reducing GHG and air pollutant emissions in the transport sector are receiving more and more attention. Taking Guangzhou as the case study city, this study quantitatively analyzed the co-benefits of reducing CO2 and air pollutant emissions under the sustainable scenario in the transport sector by developing a quantitative analysis model based on the Long-range Energy Alternatives Planning (LEAP) framework. The co-benefits quantitative parameter and the co-benefits radar chart analysis method are used to identify and evaluate the generated co-benefits, and then the co-benefits generated by implementing the sustainable measures are discussed. The results show that adjusting transport modes and increasing the application of electricity are the two most effective measures for achieving the co-benefits of reducing CO2 and air pollutant emissions in the Guangzhou transport sector. Thus, these two measures are highly preferential for mitigating CO2 and air pollutant emissions. In addition, increasing the application of biofuel and hydrogen energy also has good co-benefits. However, increasing the application of Liquefied Natural Gas (LNG) has poor co-benefits. Promoting the utilization of LNG intercity buses, trucks, and cargo ships will lead to emission increases of CO and HC compared to the current policy scenario, and small emission reductions of CO2 and other air pollutants. Therefore, to promote the utilization of LNG vehicles and ships, it is necessary to cooperate with the use of air pollutant end removal devices and high-pressure direct injection (HPDI) gas engines.

Decomposition study of energy-related CO2 emissions from Bangladesh’s transport sector development

The transportation sector has a dominant contribution to the fast-growing economy of the developing country Bangladesh. However, the nature of operating the transportation sector in the country requires an excessive amount of fossil energy which causes the rise of CO2 emissions. Ascertaining the impending factors and technologically to conserve energy, as well as governing CO2 emissions from this sector, are essential to attain sustainable development. The paper endeavors to determine the decomposition of driving factors that affect the relationship between Bangladesh's transport sector development and CO2 emissions due to energy consumption from the year 1990 to 2017 using the Logarithmic-Mean Divisia Index (LMDI) model. The decomposition factors are fragmented into five elements through consideration of five fossil energies which are used in Bangladesh's transportation sector. The result reveals a 106.94% growth of aggregate CO2 emissions in the transportation sector of Bangladesh. The results also show that aggregate influence of economic activity factor, population factor, economic structure factor, and energy intensity factor liable in increase CO2 emissions to 66.03%, 23.56%, 7.64%, and 6.25% respectively. On the contrary, the energy structure factor is accountable for the decrease in CO2 emissions to - 0.80%. Thus, the Bangladesh Government should proliferate mass responsiveness programs and cope with economic development through emphasizing quality of development rather than quantity which will ensure sustainable transport sector development.

Transport Infrastructure, Energy Use, Emissions, Tourism, and Economic Growth: A Multivariate Analysis

This paper provides empirical proof on the causal relationships between transport infrastructure development, transport sector energy consumption, transport sector CO2 emissions, tourism, and economic growth in the Philippines. The causal relationships were explored with respect to timing (i.e. lagged or no lag relationship), direction (i.e. unidirectional or bidirectional), and period (i.e. short-run effect vs. long run effect) by employing unit root, Johansen co-integration, and vector autoregressive (VAR) model tests. The results showed the existence of unidirectional short-run lagged causality running from transport infrastructure development, international tourism spending, transport sector energy consumption, transport sector CO2 emissions, economic growth, to international tourism revenue. There also exist bidirectional short-run lagged causality between transport sector energy consumption and international tourism revenue and bidirectional short-run lagged causality between transport sector CO2 emissions and international tourism revenue. No long-run relationships were detected among the variables. Recommendations for intervention include government subsidies, tax incentives, regulations, and transport-sector infrastructure development.

CO2 emission in transportation sector across 51 countries along the Belt and Road from 2000 to 2014

The transportation sector is a key economic sector and an important source of CO2 emissions. Due to these environmental implications and a desire to reduce emission levels, the number of empirical analyses on the transportation sector have increased significantly in recent times. Given this pretext, our study examines the heterogeneity and spatial autocorrelation CO2 emissions of the transportation sector for 51 belt and road economies from 2000 to 2014. As part of analysis, we examine the heterogeneity of CO2 emissions in the transportation sector through the use of the Theil index and semi-variogram analysis. Furthermore, our study also analyzed the global and local spatial autocorrelation of transport sector CO2 emissions using the Moran index for participating countries. From the analysis, this study found that the transportation CO2 emission intensity of Central and Western Asia and North Africa is significantly higher than that of other regions along the belt and road, and there is a strong spatial correlation in Southeast Asian countries. Our research provides a key reference point for governments by proposing carbon-reduction policies and the promotion of greener developmental initiatives within the transportation sector.