Carbon Emission Issues Research Articles

Carbon offset payment intentions in aviation industry: Altruism vs. Egoism and airline sustainability

Purpose The issue of excessive carbon emissions continues to be a critical global challenge. As a prominent mode of transportation for long-distance travel, aircraft is widely acknowledged as a significant source of these emissions. Carbon offset initiatives function as ecological measures, helping to reduce the negative environmental impact. This study aims to explore how benefit appeals (BA) within the aviation industry impact tourists’ carbon offset payment intentions (COPIt). Design/methodology/approach In Studies 1 and 2, scenario-based experiments were conducted to explore how goal framing (GF) and (BA) interact to influence COPIt. Study 2 further investigated the mediating roles of moral responsibility and trust in airlines within this interaction. Study 3 used real-world surveys to examine the moderating influence of moral elevation, thereby supporting the interactive effects and mediation mechanisms identified in the earlier studies. Findings Across three studies, the authors consistently identified pivotal factors shaping COPIt in the context of air travel. Study 1 revealed that the combination of BA and GF significantly impacts COPIt, with egoistic appeals linked to loss framing and altruistic appeals connected to gain framing being particularly effective in encouraging COPIt. Study 2 extended these insights by showing that moral responsibility and trust in airlines serve as mediators between BA, GF and COPIt. In Study 3, moral elevation was found to moderate the influence of BA and GF on both moral responsibility and COPIt, deepening the understanding of these dynamics. Originality/value This study expands the range of factors affecting COPIt and delves into the underlying mechanisms through which BA and GF shape COPIt. Additionally, it advances current understanding by revealing the intricate processes influenced by moral elevation. The findings not only contribute to the existing knowledge on COPIt determinants but also offer practical guidance for the aviation industry and related sectors in promoting tourists’ participation in carbon offset programs.

The Contributing Factors of Individual Carbon Footprint based on Linear Regression

Public concerns have been raised by recent climate changes. The temperature rise has and will result in more extreme weather and the melting of the Atlantic’s ice, which must be paid attention to. Different organizations have stressed the importance of achieving carbon neutrality after conducting investigations on the environment. The purpose of this paper is to address the issue of carbon emissions and seek solutions to meet the government’s objectives. This article selects several daily activities and correlates them with carbon emissions. Using the linear regression model as well as the stepwise regression model, the collected data can demonstrate how various daily activities affect an individual’s carbon emissions. The result shows that almost all daily activities that use fuel will eventually have an impact on an individual’s carbon footprint, as found in this paper. Moreover, this paper suggests several possible ways for future development to be achieved in order to achieve the realization of carbon neutrality

Performance study of bimetallic Mn-Zn porous flower-like nanosheets as supercapacitor positive electrode materials

As environmental and carbon emission issues become increasingly severe, electrochemical energy storage systems have gained attention. Compared to traditional supercapacitors, zinc-ion hybrid capacitors (ZIHCs) are not only environmentally friendly but also cost-effective. MnO2, with its low cost, eco-friendliness, high theoretical capacity, and stable crystal structure, is considered a promising positive electrode material. In this study, the morphology of MnO2 was optimized using electrodeposition. During cycling, zinc ions reversibly intercalate and extract from MnO2, forming a cactus-like nanoflower structure. This structure is loose and porous, providing ample reaction space for active materials. The assembled device exhibits excellent performance in both energy density (85.68 Wh kg−1) and stability (91.60 %).

Facilitating Charge Separation of Donor–Acceptor Conjugated Microporous Polymers via Position Isomerism Strategy for Efficient CO2 Photoconversion

Photoreduction of CO2 into the chemical feedstocks of fuels provides a green way to help solve both the energy crisis and carbon emission issues. Nevertheless, undesirable charge separation and migration, as well as rapid reverse charge recombination results in the unsatisfactory photocatalytic activity of most conjugated microporous polymers (CMPs). Herein, a pair of donor–acceptor (D-A) CMPs for CO2 photoconversion was developed through position isomerism by altering linkage sites. The results show that Py-D27F with 2,7-site linkage exhibits a completely conjugated structure and a large molecular dipole moment, thus significantly accelerating charge separation and transfer. As a result, Py-D27F achieves a higher yield of 320.9 μmol g−1 h−1 for CO2-to-CO photoreduction (without the addition of any photosensitizers and precious metals), which is about three-fold greater than that of Py-D36F (3,6-site linkage). This study provides a valuable idea for exploring outstanding CMP photocatalysts for the efficient processing of photocatalysis.

Determinants of Carbon Inventory and Systematic Innovation Methods to Analyze the Strategies of Carbon Reduction: An Empirical Study of Green Lean Management in Electroplating an Factory

This study explored the relationship between lean management, carbon inventory, and carbon reduction, along with the main factors affecting factory carbon inventory and carbon reduction, and proposed a set of carbon emission reduction strategies based on green value stream mapping. An electroplating factory and its production line in Taiwan was selected. Fourteen carbon inventory records, three work circle meetings, and green value flowcharts were utilized to collect and analyze data. Furthermore, this study applied the DMAIC framework and TRIZ to solve the issue of carbon emissions. The key factor affecting factory carbon inventory and carbon reduction was found to be electricity waste, which could be reduced through energy-saving methods. After analyzing the green value stream mapping, effective carbon emission reduction can be implemented in the manufacturing process. The company was found to gradually progress towards carbon reduction after investigating seven major green wastes. This study confirms that lean management can help organizations achieve their carbon emission reduction goals and is valuable in improving the organization’s environmental performance and competitiveness.

Air passenger carbon offset and carbon neutrality strategies: Implementation mechanism by convolutional neural network

To more effectively address the issue of carbon emissions in the aviation industry, this study first analyzes the current development status of carbon offset and carbon neutrality strategies in the aviation industry, as well as examines the existing relevant research findings. Then, optimizations are made to the Convolutional Neural Network to improve the accuracy and efficiency of the prediction model. These optimizations include architectural improvements, the use of attention mechanisms to more accurately focus on important features, as well as the adoption of multiscale feature extraction and advanced optimization algorithms to enhance the model's learning ability and convergence speed. These comprehensive improvements not only enhance the model's generalization ability but also significantly improve its applicability in complex environments. Finally, by comparing the performance of Transformer Networks, Graph Convolutional Networks, Capsule Networks, Generative Adversarial Networks, Temporal Convolutional Networks, and the proposed optimization algorithm on datasets of airline carbon emissions and fuel usage, the performance of the proposed optimization algorithm is validated through comparison of accuracy, precision, recall, and F1-score calculated from the data. Simultaneously, simulation experiments are conducted to validate the effectiveness and feasibility of the proposed optimization algorithm by comparing prediction stability, strategy adaptability, response time, and long-term effectiveness. The experimental results show that the accuracy, precision, recall, and F1-score of the proposed optimized model reach up to 0.942, 0.967, 0.951, and 0.934 respectively, all higher than those of the compared models, validating the good performance of the proposed optimized model. In the comparison of simulation experiments, the scores of prediction stability and strategy adaptability of the proposed optimized model reach up to 0.944 and 0.953 respectively, much higher than those of other models. The response time is only 0.04s when the data volume is 1000, and the computational advantage of the proposed optimized model becomes more apparent with the increase in data volume. In the comparison of long-term effectiveness, the advantage of the proposed optimized model in this aspect also becomes more obvious with the increase in data volume. Through simulation experiments, the performance of the model in actual application scenarios is further evaluated to ensure its practicability. Therefore, this study not only provides a new optimization tool for carbon emission strategies in the aviation industry but also has certain significance for research on environmental sustainability.

Electrocatalytic formate synthesis from polyethylene terephthalate and carbon dioxide through sulfide-and-reconstruct engineering of catalyst

The global issue of plastic pollution and carbon emission have significantly impacted the survival of both ecosystems and human beings. Electrochemical coupling of polyethylene terephthalate plastic (PET) and CO2 into fine chemicals provides a potential approach to achieve waste resource utilization and carbon neutrality. Herein, we reported an innovative electrocatalytic system for concurrently upgrading PET hydrolysate and CO2 into value-added formic acid. Through a sulfide-and-reconstruct strategy, Ni3Bi2S2 and Bi2S3 were synthesized as pre-catalysts, which can undergo in situ reconstruction to form NiOOH/Ni3Bi2S2 at the anode and Bi (1.5 S) at the cathode during electrolysis. The exceptional electrocatalytic performance (96 % and 97 % at the anode and cathode, respectively) and stabilities (>30 h) of the anodic NiOOH/Ni3Bi2S2 and cathodic Bi (1.5 S) was attributed to the exposure of the true active site after reconstruction. By coupling PET hydrolysate oxidation reaction (POR) with CO2 reduction reaction (CO2RR), the assembled zero-gap MEA electrolyzer realized a total Faradaic efficiency of 175 % for formate. This study provided a concept for upcycling of PET.

The Influence of Climate Perception and Low-Carbon Awareness on the Emission Reduction Willingness of Decision Makers in Large-Scale Dairy Farming: Evidence from the Midwest of Inner Mongolia, China

In recent years, global climate change has profoundly influenced natural ecosystems and human societies, making climate mitigation and carbon emission reduction a point of consensus among the international community. The issue of carbon emissions in agriculture, particularly in the livestock sector, is garnering increasing attention. This study focuses on large-scale dairy farms in the central and western regions of Inner Mongolia, exploring their low-carbon production behavioral intentions and influencing factors. By constructing a structural equation model (PLS-SEM), we systematically analyze the relationships between variables such as climate perception, value judgment, attitude, subjective norms, and perceived control and their combined effects on low-carbon production behavioral intentions. The findings suggest that the influence of climate perception and low-carbon awareness is mediated. Thus, the stronger the farm owners’ perception of climate change, the more they recognize the value of low-carbon production and the greater the social pressure they experience and their sense of self-efficacy. The farm owners’ attitudes, perceptions of social norms, and evaluations of their own capabilities collectively determine their intentions regarding low-carbon production. Furthermore, multi-group analysis showed significant heterogeneity in behavioral intentions between different scales of dairy farms. Small-scale farms, due to their weaker economic capacity, tend to harbor negative attitudes towards low-carbon production, while large-scale farms, with greater economic power and sensitivity to policy and market demands, are more likely to take low-carbon actions. This study provides theoretical support for formulating effective low-carbon policies, contributing to the sustainable development of the livestock sector and agriculture as a whole.

Spatial-Temporal Evolution and Environmental Regulation Effects of Carbon Emissions in Shrinking and Growing Cities: Empirical Evidence from 272 Cities in China

Shrinking and growing cities are categories of cities characterized by population loss or add, and the issue of carbon emissions in these cities is often neglected. Environmental regulation, as an important influence on carbon emissions, plays an important role in promoting the low-carbon transition in Chinese cities. This study focused on the carbon emissions of 272 cities in China from 2012–2021, constructed a comprehensive indicator to classify four city types, and calculated carbon emissions. Spatial-temporal characteristics and evolution of carbon emissions and impacts of environmental regulation were investigated. Carbon emissions of rapidly growing cities showed a downward trend, whereas those of slightly growing, rapidly shrinking, and slightly shrinking cities showed upward trends. The more rapidly a city grew or shrunk, the higher its average carbon emissions. Growing cities’ center of gravity of their carbon emissions migrated northwest. Carbon emissions of rapidly and slightly shrinking cities were high in the northeast, and their carbon emission centers migrated northeast and southwest, respectively, with obvious spatial autocorrelation of city types. Strengthening environmental regulations significantly positively affected carbon emission reduction. The impact of environmental regulation on carbon emissions reduction was temporally and spatially heterogeneous and more significant in non-resource cities.

Landscape Design Planning and Research of Renewable Energy Application Projects from a Sustainable Perspective

The substantial emission of carbon exacerbates climate change, posing a threat to human survival and sustainable development. The primary inducement for the carbon emission issue is the excessive dependence of human activities on fossil energy. The construction industry is among the major sectors contributing to carbon emissions, and the low-carbon development within this industry serves as the cyclical engine for the path towards carbon reduction. Currently, there is limited research that integrates low-carbon development with the landscape design of renewable energy projects. Most studies emphasize the application of relevant low-carbon measures, overlooking the landmark effects of building decarbonization. Addressing how to truly achieve landscape design for renewable energy projects from a sustainable perspective based on relevant carbon emission quantification indicators during the architectural design phase is an urgent problem to be solved. This paper takes a sustainable perspective as the starting point and systematically summarizes landscape design concepts from this perspective. It proposes three major design approaches: ecological sustainability, cultural integration, and the application of advanced technology. Using these approaches, the paper tackles the balance between economics, energy, and the environment in landscape design, recognizing it as essentially a high-dimensional multi-objective optimization problem from a methodological perspective. The paper introduces an energy-economy-environment coupling model and utilizes big data. After low-carbon planning and design, both the net carbon footprint and total carbon footprint of the landscape significantly decrease. The Greenfield carbon sequestration per kilogram increases from 301,023.21 to the current 347,856.02, with an increase of 46,832.81.

Does digital economy help reduce carbon emissions from tourism? Evidence from China

ABSTRACT In the context of global warming and the continued expansion of tourism, the issue of carbon emissions from tourism (CET) has become more prominent. Development of the digital economy provides a new opportunity to reduce CET. Using data from 30 provinces in China from 2004 to 2020, this study examines the direct effect of the digital economy on CET by employing the Generalised Method of Moments (GMM). An intermediate effects model is applied to explore its indirect transmission mechanism, and some heterogeneity is analyzed. The results indicate that digital economy plays a significant role in reducing CET. Energy structure, environmental governance and technological innovation are three important channels for the digital economy to influence CET. Heterogeneity analyses reveal that digital economy has a more pronounced decarbonisation effect in the eastern and central regions, while it is not significant in the western region. And the decarbonisation performance of the digital economy is enhanced in regions with an easier financial environment and more developed pollution abatement facilities. This study offers a theoretical and practical foundation for the low-carbon development of China’'s tourism industry. It also provides insights into the sustainable development of tourism in other countries.

Design and optimization of zero-carbon integrated energy system for highway service area

Abstract The challenge of achieving dual-carbon targets arises from the issue of high carbon emissions in Chinese highway service areas. To address this, a comprehensive electric-thermal-hydrogen energy system is proposed, consisting of a new energy generation system, heat pumps, electrolyzers, hydrogen fuel cells, energy storage devices, and charging stations. A dispatch model for the integrated energy system is established, with the objective of minimizing the total operating cost while satisfying the constraints of each device and the electric-thermal-hydrogen balance. The model is implemented in MATLAB, and the ideal optimal solution for each decision variable is obtained using the Gurobi solver. The investment and operating costs, as well as the scheduling results for electricity, heat, and cooling, are analyzed. The results indicate that the electrolyzer-fuel cell system can improve the system’s operational stability and flexibility in dispatch optimization. Due to the instability of photovoltaic and wind power devices and the fluctuating nature of loads, energy storage devices are required for peak load shaving in the system. The zero-carbon integrated energy system model can calculate the most economical investment plan based on the load and meteorological conditions of different service areas.

Design of Carbon Monitoring System Platform for Agricultural Energy Internet

As global climate change and carbon emission issues become increasingly severe, the agricultural energy internet plays a crucial role in achieving low-carbon development. The merging of new energy with modern agriculture is progressively being realized, making effective carbon monitoring and accounting especially critical. This paper presents a novel design for a carbon monitoring system platform for the agricultural energy internet, using aquaponics greenhouses as the research object. Addressing the difficulties presented by the diversity and magnitude of agricultural carbon sources, the paper proposes a carbon accounting model applicable to such greenhouse environments. This model aims to enhance energy utilization efficiency and reduce carbon emissions through precise accounting and monitoring. Subsequently, a carbon monitoring system applicable to the agricultural energy internet is designed. This system enables real-time and accurate monitoring and assessment of carbon emissions and absorption in agricultural parks. The platform, developed using a B/S architecture with a separation model between front-end and back-end employing Vue and Spring Boot frameworks, provides a comprehensive solution for monitoring and managing carbon emissions in agricultural energy.

Optimizing lean and rich Vapor compression with solar Assistance for post-combustion carbon capture in natural gas-fired power plant

The integration of post-combustion capture (PCC) CO2 technologies with power plants and high-emission industrial processes has become imperative for reducing carbon emissions and mitigating environmental impacts. This study focuses on addressing the significant energy consumption associated with PCC and explores the potential of using solar thermal energy to optimize configurations, thereby enhancing sustainability and reducing dependency on conventional power sources. Simulate various configurations under steady-state conditions using Aspen HYSYS V11 and Aspen Economic Evaluation software. The results indicate a substantial reduction in energy consumption to 2.1 GJ/ton CO2 after the initial optimization steps. The key innovations involve the integration of Lean Vapor Compression (LVC), Solvent Split Flow (SSF), Rich Solvent Recycle (RSR), and Rich Vapor Compression (RVC). Further optimization was conducted by integrating a reboiler with parabolic trough collectors (PTC), to reduce energy penalties. The LVC + RVC + RSR + SSF + PTC configuration results reveal a notable improvement in exergoeconomic factors, ranging from 3.13% to 7.8%, highlighting the economic feasibility of the optimized configurations. Simultaneously, the energy penalty decreased by 11.1%, signifying enhanced sustainability in the proposed PCC system. This study contributes to the gap in research by demonstrating the efficacy of solar thermal energy in optimizing PCC configurations, providing a pathway towards more sustainable and energy-efficient solutions for carbon capture in high-emission industrial sectors. The findings offer insights for researchers, engineers, and policymakers aiming to address the pressing issue of carbon emissions from industrial processes.

Investigation into Spatial and Temporal Differences in Carbon Emissions and Driving Factors in the Pearl River Delta: The Perspective of Urbanization

Urbanization is a significant indicator of city progress, and as urbanization advances, carbon emissions exhibit an increasing trend that must not be disregarded. Therefore, it is imperative to thoroughly examine the spatial and temporal variations as well as the factors influencing carbon emissions during the urbanization process. In this paper, based on the 2009–2019 PRD region, carbon emissions are measured from energy consumption, industrial production process, solid waste, and wastewater according to the IPCC coefficients, and spatial and spatial differences in carbon emissions are combined with spatial analysis and the drivers analyzed using the gray correlation scale. The results show that: (1) The total carbon emissions in the PRD region have been increasing over the study period, and the distribution of total carbon emissions shows a pattern of “strong in the east and weak in the west”, with energy consumption accounting for the highest proportion of carbon emissions. (2) The global Moran Index of carbon emissions in the PRD has been decreasing, with low and low clustering concentrated in Shaoguan and Zhaoqing, high and high clustering concentrated in Dongguan and Shenzhen, and low and high clustering concentrated in Shenzhen and Guangzhou, with cold spots mainly concentrated in Zhaoqing and hot spots mainly distributed in Guangzhou, Shenzhen, and Dongguan. (3) The degree of economic growth has a substantial influence on carbon emissions in the PRD cities, and the influence of technical advancement has intensified. Guangzhou City is propelled by low-carbon regulations that have a more equitable influence on all elements. Zhuhai City has a more significant influence on energy intensity, while Foshan City has a more noticeable decrease in the effect of foreign investment. To address the issue of carbon emissions, the government should establish appropriate rules to regulate carbon emissions in areas with high emissions, foster collaborative efforts across cities, and encourage the growth of environmentally friendly enterprises.

The impact of women's status on HCEs: Evidence from Chinese families

Amidst the intensification of global efforts to reduce emissions, China has prominently advocated for “carbon peaking” and “carbon neutrality” goals, and the issue of domestic carbon emissions has attracted widespread attention. Within this context, a thorough examination of energy consumption and carbon emissions within the household sector assumes paramount importance in delineating effective pathways for reducing Household Carbon Emissions (HCEs). Focusing on indirect energy use as a key representative, this article empirically investigates the influence of women's status on per capita HCEs and its underlying mechanisms, drawing on data from the 2017 Chinese General Social Survey (CGSS). The analysis reveals a notable finding: a higher women's status significantly correlates with increased per capita HCEs. Delving into the mechanisms at play, it becomes apparent that women's advancement correlates with heightened household income. This income augmentation, in turn, fuels consumption upgrades, thereby amplifying per capita HCEs, namely a phenomenon attributed to the “income effect”. Simultaneously, the advancement of women prompts households to adopt sustainable consumption patterns, representing a notable “substitution effect”. This shift in consumption preferences paradoxically contributes to increased per capita HCEs. Moreover, as women's status ascends, families display a proclivity towards sustainable consumption patterns, further accentuating the “substitution effect” and consequent reductions in per capita HCEs. Presently, in China, the “income effect” resulting from women's status holds a greater sway over per capita HCEs than the “substitution effect”. This nuanced analysis not only sheds light on the intricate dynamics governing HCEs but also furnishes policymakers with invaluable insights for crafting strategies aimed at mitigating such emissions.

Acceleration of Photoinduced Electron Transfer by Modulating Electronegativity of Substituents in Stable Zr-Metal-Organic Frameworks to Boost Photocatalytic CO2 Reduction.

Photoreduction of CO2 with water into chemical feedstocks of fuels provides a green way to help solve both the energy crisis and carbon emission issues. Metal-organic frameworks (MOFs) show great potential for CO2 photoreduction. However, poor water stability and sluggish charge transfer could limit their application. Herein, three water-stable MOFs functionalized with electron-donating methyl groups and/or electron-withdrawing trifluoromethyl groups are obtained for the CO2 photoreduction. Compared with UiO-67-o-CF3-CH3 and UiO-67-o-(CF3)2, UiO-67-o-(CH3)2 achieves excellent performance with an average CO generation rate of 178.0 μmol g-1 h-1 without using any organic solvent or sacrificial reagent. The superior photocatalytic activity of UiO-67-o-(CH3)2 is attributed to the fact that compared with trifluoromethyl groups, methyl groups could not only elevate CO2 adsorption capacity and reduction potential but also promote photoinduced charge separation and migration. These are evidenced by gas physisorption, photoluminescence, time-resolved photoluminescence, electrochemical impedance spectroscopy, transient photocurrent characteristics, and density functional theory calculations. The possible working mechanisms of electron-donating methyl groups are also proposed. Moreover, UiO-67-o-(CH3)2 demonstrates excellent reusability for the CO2 reduction. Based on these results, it could be affirmed that the strategy of modulating substituent electronegativity could provide guidance for designing highly efficient photocatalysts.

Analysis of CCER Policy Application in the New Energy Vehicle Industry

As China's economy is booming, the issue of carbon emissions is becoming increasingly prominent. In terms of policy, China has established a domestic CCER mechanism to improve the carbon trading market, and the future development of CCER policy has received much attention, but there are still deficiencies in synergistic development with specific industrial sectors. This paper explains the CCER restart process based on the progressive policy model, through the analysis of energy type suitability, the future development path of new energy vehicles, and the existing CCER methodology, combined with the example of the development of new energy vehicle industry in Guiyang City, Guizhou Province, effectively demonstrates the suitability of new energy automobile industry and renewable energy CCER projects, and explores the combination of the two. It also explores the path of "light storage, charging and discharging", and then puts forward the following suggestions for CCER policy development: improve the policy content and strengthen the policy synergy.

Construction and Analysis of Machine Learning Based Transportation Carbon Emission Prediction Model

Addressing the issue of carbon emissions in the transportation sector, this research constructed various predictive models using multiple machine learning algorithms based on panel data from 30 provinces in China from 2005 to 2019. The study aimed to identify the optimal machine learning algorithm and key factors influencing the carbon emissions of transportation, providing potent references for policymakers and decision-makers to reduce carbon emissions and promote the sustainable development of the transportation sector. Initially, drawing from the concept of the fixed effects model, we included the heterogeneity differences among provinces as an important factor. We further employed a combined method of Pearson's correlation coefficient and Spearman's rank correlation coefficient to screen 18 factors influencing transportation carbon emissions. We then made a preliminary selection of seven common machine learning algorithms and used the screened factors as explanatory variables for model training. The three algorithms with the best performance were further optimized and trained. Subsequently, we utilized the K-fold cross-validation method; plotted learning curves to test the performance of each predictive model; and used MSE, MAE, R2, and MAPE as evaluation indicators to determine the best predictive model. SHAP values were chosen to calculate the importance of each explanatory variable in the optimal predictive model. The results indicated that the multicollinearity among the seven factors of provincial differences, total consumption of social goods, urban green space area, freight turnover, number of private cars, transportation industry output, and permanent population was weak, and all passed the significance test. They could be used as explanatory variables in the prediction model of transportation carbon emissions. The prediction results of the Random Forest and XGBoost algorithms were both outstanding, with R2 values above 0.97 and errors below 10 %, showing no signs of overfitting or underfitting. Among them, the XGBoost algorithm performed the best, whereas the KNN algorithm performed poorly. The importance ranking of the explanatory variables was as follows:provincial differences > total consumption of social goods > number of private cars > permanent population > freight turnover > urban green space area > transportation industry output. A comprehensive analysis of relevance and importance showed that provincial differences were an indispensable variable in the prediction of transportation carbon emissions. In conclusion, this study provides a new approach to the governance of carbon emissions in the transportation industry, and the results can serve as a reference for policymakers and decision-makers. In future policy design and decision-making, the distinctive factors of each province should not be overlooked. Measures targeted at specific regions need to be formulated to promote the sustainable development of the transportation industry.

Prioritas Peningkatan Kinerja Bus Listrik Metrotrans Rute 7A Kampung Rambutan – Lebak Bulus Jakarta

In DKI Jakarta, heavy traffic and oil-fueled car emissions are to blame for between 32% and 57% of the province's overall air pollution. The DKI Jakarta Provincial government started implementing electric buses as a proactive reaction to the carbon emission issue. One of these buses is located on Transjakarta feeder routes (non-BRT), namely Metrotrans electric bus Route 7A Kampung Rambutan - Lebak Bulus. Congestion and air pollution are the two main issues facing the transportation sector in DKI Jakarta. One mass transit option that addresses these issues is the battery-powered Metrotrans electric bus. This study's objectives are to assess the operational and service performance of Metrotrans electric buses Route 7A Kampung Rambutan - Lebak Bulus and to establish the order of importance for performance improvement. Operational performance study, which includes headway, journey time, waiting time, load factor analysis, Importance-Performance study (IPA), and Potential Gain Customer Value (PGCV) analysis, was the analysis approach employed. According to the findings, waiting times, headways, load factors, journey times, GPS, SOPs for handling crises, bus stop availability, and information about routes and routes to be served were the top priorities for performance improvement.