A global comparative study of low-carbon domestic transportation transition.

This study aims to identify the causality between bank financial performance measured by Return on Assets (ROA), Return on Equity (ROE), Net Interest Margin (NIM), and Operating Expenses to Operating Income (BOPO) with Green Financing Portfolio and CO2 emissions in the transportation sector. This study uses descriptive quantitative research methods and content analysis of the Sustainability Report of Bank Mandiri, Indonesia for the period 2016 to 2022. In this study, we collected data from Bank Mandiri's financial statements which included information on ROA, ROE, NIM, and BOPO. In addition, we also collected CO2 emission data available from 2016 to 2022. The research sample is Bank Mandiri as one of the state-owned banks in Indonesia. We used purposive sampling technique to select samples that meet the inclusion criteria. The collected data was then analyzed using statistical methods to test the relationship between the variables involved, namely the bank's financial performance (ROA, ROE, NIM, and BOPO), Green Financing Portfolio, and CO2 emissions in the transportation sector. We use content analysis to illustrate the results of Bank Mandiri's financial statements in graphical form. The results of the analysis show that the increase in Green Bond that started in 2016 has a significant impact on the increase in fund allocation for Green Financing Portfolio. This indicates a positive causality between Green Bond and Green Financing Portfolio. In this context, the causality between Green Financing Portfolio and CO2 emissions can be explained through the influence of investment in green technology and sustainable practices. With significant funds allocated through the Green Financing Portfolio, companies and institutions can implement projects that aim to reduce CO2 emissions in the transportation sector. This means that the use of Green Bond as a sustainable funding source has the potential to reduce the negative impact of transportation on the environment.

The transport sector is recognized as one of the largest carbon emitters. To achieve China's carbon peak commitment in the Paris Agreement on schedule, it is indispensable to explore the peak carbon emissions and mitigation strategies in the transport sector. Many researches in the past have contextualized in China's total emissions peak, while the study about forecasting China's transport CO2 emissions peak seldom appeared, especially the application of intelligent prediction model. To further investigate the determinants and forecast the peak of transport CO2 emissions in China accurately, a novel bio-inspired prediction model is proposed in this paper, namely, the extreme learning machine (ELM) optimized by manta rays foraging optimization (MRFO), hereafter referred as MRFO-ELM. Adhering to this hybrid model, the mean impact value (MIV) method is then employed to evaluate and differentiate the importance of thirteen influencing factors. Additionally, three scenarios are set to conduct prediction of China's transport CO2 emissions. The empirical results indicate that the proposed MRFO-ELM has excellent performance in terms of the optimization searching velocity and prediction accuracy. Simultaneously the level of vehicle electrification is verified to be one of the emerging major factors affecting China's transport CO2 emissions. The transport CO2 emissions in China would peak in 2039 under the baseline model scenario, while the plateau would occur in 2035 or 2043 under sustainable development mode and high growth mode, respectively. The peak years imply much pressure on China's transport carbon emissions abatement currently, whereas active policy adjustments can effectively urge the earlier occurrence of the emission peak. These new findings suggest that it is essential for China to improve the energy mix and encourage the electric energy replacement in line with urbanization pace, so as to achieve CO2 emissions mitigation in the transport industry.

Transport is an essential infrastructure for development. With its high share of gross domestic product (GDP), it makes a significant contribution to total CO2 emissions in Malaysia. It is therefore important to pay greater attention to reducing CO2 emissions and sustainable development in this sector. Therefore, this study aims at estimating the relationship between transport CO2 emissions and its key drivers using the Autoregressive Distributed Lag (ARDL) technique. The time period covered by the study extends from 1978 to 2018. It further investigates the response of CO2 emissions to shocks in the value of other variables by employing the generalized impulse response approach. The results suggest that urbanization is the major contributor to the increase in CO2 emissions followed by the carbon intensity of energy in the long-run. Carbon intensity of energy, GDP per transport worker and urbanization contribute significantly to increases in transport CO2 emissions in the short- and long-run. Testing the Environmental Kuznets Curve (EKC) hypothesis recommends that Malaysia continue to be on track to reach the highest level of income and welfare to give pay more attention to the environment. Therefore, the country maintains its CO2 emissions level in the future because of economic development. Therefore, these findings show that energy and environmental policymakers need to pay more attention to improving energy efficiency and the use of low-carbon technologies and electrification in the transport sector and the use of high-quality public transport, particularly in urban areas, for sustainable urban development.

Decoupling and mitigation potential analysis of CO2 emissions from Pakistan's transport sector

Since 2005, China has become the largest emitter of CO2. The transport sector is a major source of CO2 emissions, and the most rapidly growing sector in terms of fuel consumption and CO2 emissions in China. This paper estimated CO2 emissions in the transport sector across 30 provinces through the IPCC (International Panel on Climate Change) top-down method and identified the spatiotemporal pattern of the decoupling of transport CO2 emissions from economic growth during 1995 to 2016 by the modified Tapio’s decoupling model. The CO2 emissions in the transport sector increased from 103.10 million ton (Mt) in 1995 to 701.04 Mt in 2016. The year, 2005, was a turning point as the growth rate of transport CO2 emissions and the intensity of transport CO2 emissions declined. The spatial pattern of transport CO2 emissions and its decoupling status both exhibited an east-west differentiation. Nearly 80% of the provinces recently achieved decoupling, and absolute decoupling is beginning to take place. The local practices of Tianjin should be the subject of special attention. National carbon reduction policies have played a significant role in achieving a transition to low-carbon emissions in the Chinese transport sector, and the integration of multi-scale transport CO2 reduction policies will be promising for its decarbonisation.

China’s transport industry is a priority area for energy conservation and CO2 emission reduction due to its great carbon emissions. Based on the data of 1980–2012, this study first analyzes the CO2 emission differences among China’s four transport sub-sectors, i.e., road, waterway, aviation and railway, using the σ-convergence and β-convergence methods. Subsequently, it explores the influencing factors for CO2 emissions in China’s four transport sub-sectors, respectively, with the logarithmic mean Divisia index method. The following results are obtained. First, road and waterway are the major sources for China’s transport CO2 emissions, and the road sector acts as the relatively largest CO2 emitter. In 2012, the CO2 emissions of road, waterway, aviation and railway accounted for 65.21, 29.07, 3.61 and 2.11 % of the total, respectively. Second, the CO2 emission differences among China’s transport sectors gradually shrank during the period 1980–1996, while gradually widened during 1996–2012. Third, the economic growth served as one of the most important factors, driving China’s transport CO2 emission growth over the period 1980–2012. In this period, the CO2 emissions of road, waterway, aviation and railway sectors increased by 561.71, 265.27, 54.07 and 28.28 Mt, respectively.

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.

Scenario simulation and synergistic effect analysis of CO2 and atmospheric pollutant emission reduction in urban transport sector: A case study of Chengdu, China

Analysis of CO2 emissions for island ferry services

This study applies an economic input-output life cycle assessment (EIO-LCA) approach for 180 industrial sectors of the Thai economy. The approach is used to evaluate energy consumption and CO2 emissions of the agricultural sector for the entire supply chain and particularly focusing on transportation. Transportation emits the second largest amount of CO2 by sector in Thailand. Road transportation is the dominant form of cargo transport, accounting for 80% of all modes. The results show that transportation emissions from all modes of cargo transport account for 1–6% of total supply chain emissions. The vegetable sector shares highest CO2 emissions in transportation. By changing the transportation mode from road freight to rail, emissions could be expected to decline. If 50% of road transportation was shifted to rail, CO2 emissions would drop by 30%.

The purpose of this article is to empirically find the Environmental Kuznets Curve (EKC) relationship between income and carbon dioxide (CO2) emissions and to analyze the influence of population aging on such emissions. We utilize Korean regional panel data of 16 provinces during the period from 1998 to 2016. To account for the nonstationary time series in the panel, we employ a fully modified ordinary least squares (FMOLS) and estimate long-run elasticity. From the empirical results, we can find the nonlinear relationship between income and CO2 emissions. Additionally, we verify the fact that population aging reduces CO2 emissions. A 1% increase in the proportion of the elderly results in a 0.4% decrease in CO2 emissions. On the other hand, the younger population increases CO2 emissions. These results were in line with those of additional analysis on residential and transportation CO2 emissions, for the robustness check.

Chapter 9 - Energy Use and CO2 Production in the Urban Passenger Transport Systems of 84 International Cities: Findings and Policy Implications

Transport Infrastructure and CO <sub>2</sub> Emissions in the OECD Over the Long Run

This paper aims at analyzing energy consumption and CO2 emission in the Chinese transportation sector. Historical data is used to investigate the energy consumption and intensity in China. Firstly, the energy consumption values in transportation sub-sectors are reviewed. Secondly, the CO2 emission in the transportation sub-sector are calculated. At last, the review of policies such as fuel econmy standards are summarized in this study. It is found in the study that highways transport was the biggest energy consumner and the biggest contributor to CO2 emission in China. It is urgent to adopt suitable energy policy to balance the energy consumption and matierial and material production and reduce emission in transportation sector.

The transport sector, particularly road transport, is a major factor in the overall emissions balance of the substances involved in air pollution for the majority of developing countries. This paper investigates the dynamic links between information and communication technology (ICT), transport energy, environmental degradation and growth for Tunisia. The authors used a Johansen co-integration analysis to determine this econometric relationship using data during 1990–2015. In order to test the Granger causality links in the short and long run, a panel Vector Error Correction Model is used. The variance decomposition is used to confirm the existing links between the different variables. Different results are found. These findings show the existence of bidirectional in short- and long-run causality between transport energy and CO2 emissions. By cons, ICT does not minimize significantly pollution in Tunisia. These findings are very important for the transport sector and in terms of the choice of government policy decisions in order to minimize the pollution.