Carbon Emission Factors Research Articles

Can crop production agglomeration reduce carbon emissions?—empirical evidence from China

Reducing carbon emissions is crucial for environmental protection and the survival of humankind, particularly in agricultural growth, as it ensures the sustainability of the food supply. This study examines the import of the crop production agglomeration on carbon emissions across several areas of China. It employs panel data spanning from 2012 to 2022. The crop production agglomeration was assessed using the average industrial agglomeration rate, whereas the carbon emissions were evaluated using the IPCC carbon emission factors. Empirical analyses were conducted using the panel fixed effects model and the Spatial Durbin Model . The results indicate that crop production agglomeration directly reduces carbon emissions. Moreover, the concentration of crop production has a geographical demonstration effect on carbon emissions, where greater levels of crop production agglomeration result in a more efficient decrease of carbon emissions in nearby regions. An analysis of heterogeneity indicates that the impact of crop production agglomeration on carbon emissions is more pronounced in the eastern and northeastern regions of China compared to the central and western areas. The study advocates for the formulation of tailored carbon reduction methods that align with the distinct attributes of crops in various locations. It promotes variety and low-carbon development in crop production to drive industrial advancement. The study advocates for enhancing cooperation among crop production enterprises across various areas to provide platforms for information exchange and technical innovation. Furthermore, it advocates for governments to design efficient methods and regulations to reduce carbon emissions in crop production.

Dynamic Carbon Emission Factors in Source–Network–Storage Power System Planning: A Focus on Inverse Modelling

In light of global climate change, China has set strategic goals for carbon peaking by 2030 and carbon neutrality by 2060, emphasizing the necessity of constructing a new power system with a high proportion of renewable energy sources. As coal-fired power plants are the main carbon emissions source in the power system, their low-carbon transition and morphology structure optimization is crucial. This paper explores the critical role of dynamic carbon emission factors within source–network–storage power system planning and proposes an innovative inverse dynamic carbon emission factor that effectively captures the nonlinear relationship between load rates and emissions. Comparative analyses using the HRP-38 test case demonstrate that the inverse model enhances computational efficiency, reduces solution times, and more accurately reflects the emissions characteristics of coal-fired units across varying operational conditions. Furthermore, the inverse model offers improved economic performance and broader flexibility in unit selection, highlighting its potential to balance carbon emissions control and economic optimization in future power system planning.

Dynamic Scenario Simulation of Building Carbon Peak from the Perspective of Urbanization

With the rapid development of urbanization in China, the energy consumption and carbon emissions in the building sector will continue to grow. Therefore, the future dynamic evolution of building carbon emissions must be crucially investigated to achieve the "dual carbon" target in China. A system dynamics model of "urbanization-building carbon emissions" was constructed from the perspective of urbanization for revealing the mechanism of urbanization-related factors on building carbon emissions. On this basis, a dynamic simulation of the evolution trend and peak situation of building carbon emissions in the Beijing-Tianjin-Hebei （BTH） Region from 2000 to 2050 was carried out using multi-scenario analysis. The results showed that： ① Under the baseline scenario, the carbon emissions of rural residential buildings, urban residential buildings, and commercial buildings peak in 2027, 2028, and 2037, respectively, with peaks （in CO2） of 0.60×108, 0.99×108, and 1.95×108 tons. ② Under the low-carbon scenario, the peak （in CO2, the same below） time of carbon emissions of urban residential buildings was the most advanced, reaching the peak of 0.88×108 tons in 2024. ③ In the deep low-carbon scenario, the peak time of carbon emissions of the overall buildings occurred five years earlier than that in the low-carbon scenario. It was predicted to peak at 2.61×108 tons in 2028, reaching the 2030 carbon peak target. ④ The sensitivity analysis showed that the carbon emission factor of commercial buildings and the per capita commercial building space floor in the spatial dimension were the major factors influencing the building carbon emissions, followed by the percentage of the urban population in the population dimension. Finally, suggestions are made for the government of the Beijing-Tianjin-Hebei Region to carry out energy conservation and emission reduction planning.

Research on Vehicle Path Planning Method with Time Windows in Uncertain Environments

With the growing complexity of logistics and the demand for sustainability, the vehicle routing problem (VRP) has become a key research area. Classical VRPs now incorporate practical challenges such as time window constraints and carbon emissions. In uncertain environments, where many factors are stochastic or fuzzy, optimization models based on uncertainty theory have gained increasing attention. A single-objective optimization model is proposed in this paper to minimize the total cost of VRP in uncertain environments, including fixed costs, transportation costs, and carbon emission costs. Practical constraints like time windows and load capacity are incorporated, and uncertain variables, such as carbon emission factors, are modeled using normal distributions. Two uncertainty models, based on the expected value and chance-constrained criteria, are developed, and their deterministic forms are derived using the inverse distribution method. To solve the problem effectively, a hybrid ant colony–zebra optimization algorithm is proposed, integrating ant colony optimization, zebra optimization, and the 3-opt algorithm to enhance global search and local optimization. Numerical experiments demonstrate the superior performance of the hybrid algorithm, achieving lower total costs compared to standalone ant colony, zebra optimization, genetic algorithm, and particle swarm optimization algorithms. The results highlight its robustness and efficiency in addressing complex constraints.

An analysis of the decomposition and driving force of carbon emissions in transport sector in China

China’s transport carbon emissions are increasing quickly and the issue of emission reduction is urgent. This article aims to calculate and decompose China’s transport carbon emissions during 2001–2019. It first calculates the China’s transport carbon emissions by IPCC carbon emission factor method, and then applies the Logarithmic Mean Divisia Index (LMDI) model for decomposition analysis. The conclusion indicates that: (1) Diesel, gasoline, kerosene, and fuel oil are the major energy sources used in China’s transport sector, with the combined consumption of diesel and gasoline exceeding 70%, and the annual growth rate of energy consumption reached 8.92% during 2000–2019. Among them, natural gas and liquefied petroleum gas (LPG) have the fastest growth rate, while the only one showing a downward trend is raw coal, indicating that China’s transportation energy structure is being optimized. (2) Although China’s transport carbon emissions have been increasing, the growth rate has declined since 2013. The proportion of carbon emissions from kerosene, diesel, natural gas, and LPG increased from 2000 to 2019, while that of raw coal, gasoline, and fuel oil decreased. This suggests that the use of clean energy, air transportation, and large-scale transportation is increasing, while the use of heavily polluting fuels and small-scale road transportation is decreasing. (3) Per capita GDP is the driving factor that has the most influence on the increase of China’s transport carbon emissions. Population positively influences transportation carbon emissions too, but issues such as aging, low fertility rates, and insufficient labor force may change the direction of the impact in the next 30 years. (4) The negative effect of energy intensity on transport carbon emissions is the greatest, followed by industrial structure and energy structure. The development of highways, new energy vehicles, railway electrification, multimodal transportation, third-party logistics, and logistics information technology in China has improved the energy structure, reduced energy intensity, and brought China’s transport sector into an important stage of innovation driven and pursuit of coordinated development with the environment.

Carbon emission reduction potential of land use in typical alpine meadow region in China

Alpine meadows are primarily situated in mountainous areas and plateaus, exhibiting a high sensitivity to both climate fluctuations and human activities. However, understanding the factors influencing land use carbon emissions change in these regions remains complex, with uncertain mechanisms. Thus, it is imperative to accurately grasp the factors affecting carbon emissions in alpine meadows. We conducted a study estimating land use carbon emissions in the Upper Yellow River in Gannan from 1990 to 2020, using the Logarithmic Mean Divisia Index decomposition model and the Tapio decoupling model to identify driving factors of land use carbon emissions. Additionally, we analyzed the correlation between these factors and economic growth. The findings showed that the land use efficiency in the Upper Yellow River in Gannan region improved substantially by 436 %. Particularly, construction land emerged as the primary source of carbon emissions. The land use carbon emission increased with the increase of carbon emission intensity of construction land and the expansion of the size of construction land. Moreover, economic development and land use carbon emissions are weak decoupled. These results suggest that economic development is considered the primary driving force behind the increase in land use carbon emissions in the region. To achieve the decoupling of economic development and land use carbon emissions, we propose to improve land use efficiency by adjusting the industrial structure as well as improving land use efficiency.

Towards low-carbon travel trips through carbon footprint: A case study of marine tourism in Sichang Island, Thailand

The growing concern over climate change has heightened the need for low-carbon tourism practices. The study aims to evaluate the carbon footprint of marine tourism on Sichang Island, Thailand. The carbon footprint assessment is set for the three travel trips identified based on the tourist's lifestyle: Route I: Merit-making trip, Route II: Nature trip, and Route III: Relaxation, Adventure, and Education. The greenhouse gas (GHG) emissions calculation under each travel trip covers transportation, accommodation, meals, recreational activities, and waste management. The results revealed that the total GHG emissions for tourism routes I, II, and III are 9.9, 10.5, and 26.2 kg CO2e/person/trip, respectively. The study has analyzed the variations in carbon footprint results based on factors including the modes of transportation, i.e., ferry rides, high-speed boats, motorized three-wheeled vehicles (Tuk-Tuk), motorcycle and car rental; types of accommodation; food and beverage menu; recreational and marine tourism activities and solid waste management practices. The major contributors to the GHG emissions of routes I and II are the sea transport, meals, and land transport on the island. Route III has the highest carbon footprint because of the accommodation required for the tour program. The obtained carbon emission factors can be used to plan and manage marine tourism activities. By comparing activities, stakeholders can make informed decisions to minimize carbon footprints. The carbon emission reduction measures and the effective carbon offset programs for the island have been recommended.

Study on the Spatial and Temporal Evolution of Building Carbon Emissions and Influencing Factors in the Urban Agglomeration of the Yangtze River Economic Belt

With the rapid urbanization process, the construction industry has become a significant source of urban carbon emissions in China. The carbon emissions from buildings in the urban clusters of the Yangtze River Economic Belt, a crucial region for China’s economic development, have attracted considerable attention. This study focuses on urban buildings and aims to investigate the primary influencing factors of building carbon emissions in the urban clusters of the Yangtze River Economic Belt. The study highlights the innovative use of nighttime light remote sensing data to analyze urban carbon emissions and provides an in-depth exploration of the spatiotemporal characteristics of building carbon emissions in the urban clusters of the Yangtze River Economic Belt. Utilizing nighttime light remote sensing data similar to DMSP-OLS and provincial-level building carbon emissions, combined with spatial autocorrelation and spatiotemporal geographically weighted regression models, the study estimates and analyzes the building carbon emissions from 2012 to 2021 in 71 prefecture-level and above administrative regions within the three major urban clusters of the Yangtze River Economic Belt. The results indicate a continuous increase in total building carbon emissions in the three major urban clusters of the Yangtze River Economic Belt, with an accelerating growth rate. Spatially, urban building carbon emissions exhibit enhanced convergence but decreasing correlation over time, demonstrating evolving spatiotemporal patterns. Furthermore, the study identifies economic development level, population size, built-up area, and industrial structure as the main factors influencing building carbon emissions, with industrial structure showing significant impact.

An Interval Fuzzy Programming Approach to Solve a Green Intermodal Routing Problem for Timber Transportation Under Uncertain Information

This study investigates an intermodal routing problem for transporting wood from a storage yard of the timber harvest area to a timber mill, in which the transfer nodes in the intermodal transportation network have multiple service time windows. To improve the environmental sustainability of timber transportation, a carbon tax policy is employed in the routing to reduce the carbon emissions. Uncertain information on the capacities and carbon emission factors of the transportation activities in the intermodal transportation network is modeled using interval fuzzy numbers to enhance the feasibility of the routing optimization in the actual timber transportation. Based on the above consideration, an interval fuzzy nonlinear optimization model is established to handle the specific routing problem. Model defuzzification and linearization are then conducted to obtain an equivalent formulation that is crisp and linear to make the global optimum solution attainable. A numerical experiment is conducted to verify the feasibility of the proposed model, and it reveals the influence of the optimization level and service time windows on the routing optimization, and it confirms that intermodal transportation is suitable for timber transportation. This experiment also analyzes the feasibility of a carbon tax policy in reducing the carbon emissions of timber transportation, and it finds that the performance of this policy is determined by the optimization level given by the timber mill and is not always feasible in all cases. For the case where a carbon tax policy is infeasible, this study proposes a bi-objective optimization that can use Pareto solutions to balance the economic and environmental objectives as an alternative. The bi-objective optimization further shows the relationship between lowering the transportation costs, reducing the carbon emissions, and enhancing the reliability on capacity and budget by improving the optimization level. The conclusions provide managerial insights that can help the timber mill and intermodal transportation operator organize cost-efficient, low-carbon, and reliable intermodal transportation for timber distribution, and support sustainable forest logistics.

Assessing the Spatial Distribution of Carbon Emissions and Influencing Factors in the Yellow River Basin

The spatial distribution and trend of carbon emissions in the Yellow River Basin—an important ecological barrier and economic belt in China—directly affect the stability of the ecosystem and the sustainable development of the regional economy. Based on the data for carbon emissions in China’s counties from 1997 to 2017, this paper utilizes standard deviation ellipses, Theil index nested decomposition, and geographic detector models to make a comprehensive description of the spatial and temporal distribution and dynamic evolution characteristics of carbon emissions in the Yellow River Basin. Factors influencing carbon emissions are also analyzed from multiple dimensions. According to the findings, (1) carbon emissions at the county level show a clear upward trend without reaching a peak, exhibiting a spatial distribution of higher emissions in the east and lower in the west and higher in the south and lower in the north, with the mid-lower reaches being the center. The junction of the Shandong, Shaanxi, and Gansu provinces further exhibits a significant expansion, forming two core areas of carbon emissions. (2) Carbon emissions at the county level in the Yellow River Basin are influenced by both economic and geographic factors, exhibiting a significant high carbon spillover effect and a low carbon lock-in effect. The gravity center of the distribution has shifted towards the mid-lower reaches, with the upper reaches displaying dispersion tendencies. (3) Intra-regional disparities are the main source of the overall spatial differences in carbon emissions, with the largest disparities being observed in the upper reaches, followed by the middle reaches, and the smallest disparities being observed in the lower reaches. Further analysis shows that the level of economic development is the primary factor influencing the spatial variation of carbon emissions, and the combined effects of population size and industrial agglomeration are the key drivers of the annual growth in carbon emissions.

Spatial distribution and influencing factors of carbon emissions from private cars in China: simulation analysis based on trajectory big data

Spatial distribution and influencing factors of carbon emissions from private cars in China: simulation analysis based on trajectory big data

Prediction and Scenario Simulation of Carbon Emissions Peak of Resource-Based Urban Agglomeration with Industrial Clusters—Case of Hubaoe Urban Agglomeration Inner Mongolia Autonomous Region, China

China has implemented a “dual-carbon” policy in response to the Paris Agreement’s global climate change objectives. Hohhot, Baotou, and Ordos (HBO-UA) is a resource-based urban agglomeration that is noteworthy for having significant heavy industry in China. Based on the extended STRIPAT model, which broadens the study indicators into six aspects—population, economics, technology, urbanization, industrial energy, and industrial structure—this paper develops a research framework of “Driving–Predicting–Simulating” for carbon emissions. According to the “one formula for one city” principle, driver models were constructed for Hohhot, Baotou, and Ordos, respectively. The following conclusions were drawn: (1) Population and urbanization are the dominant factors of carbon emissions in HBO-UA, following the economy and industrial energy. (2) Carbon emissions are multifactor-driven in Hohhot, double-factor-driven in Baotou, and single-factor-driven in Ordos. (3) Hohhot can achieve its carbon emissions peak under more efficient and lower policy costs, while Ordo is under great pressure to reduce carbon emissions. (4) We suggest multiple strategies to accomplish the “dual-carbon” goals for resource-based urban agglomeration with industrial clusters. These strategies include fostering diversified consumption by continuously enhancing urban functions, directing the transformation of the industrial structure, and fostering the growth of emerging industries.

County-Level Spatiotemporal Dynamics and Driving Mechanisms of Carbon Emissions in the Pearl River Delta Urban Agglomeration, China

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods.

Spatio-temporal heterogeneity and scenario prediction of influencing factors of transportation carbon emissions in the Yangtze River Economic Belt, China

Abstract Reducing carbon emissions in the transportation sector is a crucial aspect of China achieving its carbon peak and carbon neutrality goals. This study investigates the spatiotemporal differentiation characteristics of carbon emissions from transportation in the Yangtze River Economic Belt. Using the Geographically and Temporally Weighted Regression(GTWR) model to reveal the spatio-temporal heterogeneity of factors influencing transportation carbon emissions. Additionally, the Support Vector Regression(SVR) is trained to predict the carbon emissions reduction potential of transportation under different scenarios. The results showed that: From 2000 to 2021, the transportation emissions of the Yangtze River economic belt showed an overall upward trend. The high carbon emission regions are Jiangsu Province, Shanghai, Zhejiang Province and Hubei Province, and the emission center is located in Hubei Province. The total population, urbanization rate, per capita GDP, carbon emission intensity, passenger turnover volume, and civilian vehicle ownership all have a positive effect on transportation carbon emissions, while energy structure has a negative impact. Moreover, the influence of each factor exhibits significant spatial heterogeneity. Under three scenarios: baseline, low-carbon scenario I, and low-carbon scenario II, transportation carbon emissions in the Yangtze River Economic Belt are projected to peak by 2030. With the application of clean energy and a reduction in population size, low carbon scenario II demonstrates greater potential for carbon emission reduction, with a projected value of 88.552 million tons by 2032.

Agricultural carbon emissions in China: measurement, spatiotemporal evolution, and influencing factors analysis

IntroductionThe agricultural sector is the second largest emitter of greenhouse gases, accounting for 23% of global anthropogenic carbon emissions. Analysis of the basic state of carbon emissions from China's agriculture is helpful to achieve carbon reduction targets.MethodsAgricultural carbon emissions were calculated using the emission factor method, based on data from the China Rural Statistical Yearbook and various provincial statistical yearbooks. To analyze spatial patterns, the standard deviation ellipse method and the center of gravity migration model were employed, uncovering the migration path of agricultural carbon emissions. Regional disparities and the driving factors of agricultural carbon emissions were further examined using the Theil index and the Logarithmic Mean Divisia Index (LMDI) model.ResultsThe analysis indicated that the emissions center has gradually shifted towards the central and western regions, reflecting changes in agricultural production activity areas. Intraregional differences are the primary contributors to the imbalance in agricultural carbon emissions, with pronounced disparities in grain production and consumption balance regions. Key influencing factors include agricultural production efficiency, adjustments in agricultural industrial structure, economic structure and output, and urbanization levels. The economic output effect and urbanization effect are identified as the main drivers of increased carbon emissions, while declining production efficiency has hindered emission reduction efforts.ConclusionThe findings provide valuable insights for regional management and policymaking in China's agricultural sector, highlighting the need to enhance production efficiency and optimize agricultural structure to reduce emissions.

Study on carbon emission calculation during the materialization phase of subway stations and comparative analysis of carbon emissions from various construction methods

To enhance the low-carbon construction level of subway stations, this paper, based on life cycle theory and the carbon emission factor method, summarizes the carbon emission calculation methods and relevant standards. Preliminary versions of the "Carbon Emission Database Software for subway station Civil Engineering" and "Carbon Emission Calculation Software for subway station Civil Engineering" were developed, enabling rapid calculation of carbon emissions for subway stations. From the perspective of low-carbon construction, a subway station in Guangzhou was used as an example to calculate the carbon emissions of three low-carbon construction methods. The results indicate that: (1) The carbon emission intensity of the materialization phase of the case station using the cover excavation reverse method is 3.46 tCO2e/m², demonstrating a lower carbon advantage compared to the other two methods; (2) The carbon emission of the building materials production phase accounts for over 84 % of the total carbon emission during the materialization phase for all three methods, making it the main source of carbon emission in the materialization phase of subway stations; (3) For subway stations of the same cost, the carbon emission of the open cut method is 13.77 % higher than that of the cover-excavation method and 10.18 % higher than that of the cover excavation reverse method.

Investigating carbon peak prediction scenarios and implementation strategies in higher education institutions: A case study of Shandong Jianzhu university

The potential for energy conservation and emission reduction in Chinese universities is significant. The exploration of scenarios for predicting carbon peaking and identifying implementation pathways can offer valuable insights and demonstrations for achieving dual carbon goals. This paper develops a research methodology for carbon peaking in universities, with a focus on four main aspects: carbon emission accounting, the analysis of influencing factors, carbon emission prediction, and implementation pathways for carbon peaking. By utilizing the campus of Shandong Jianzhu University as a case study, the paper practically demonstrates the prediction of carbon peaking scenarios and offers technical solutions for universities. The results demonstrate that: (1) Using the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the influencing factors of carbon emissions in universities, we confirm that the energy structure effect, economic effect, and population effect have a promoting impact on the increase of carbon emissions at Shandong Jianzhu University, while the energy intensity effect exhibits a significant inhibitory effect. (2) By employing Partial Least Squares (PLS) regression analysis of annual carbon emission statistics from universities, we construct a STIRPAT model with high predictive accuracy for carbon emission regression. The carbon emission prediction model for Shandong Jianzhu University is: lnC^t=-25.899+3.571lnPt+0.966lnARt+0.716lnTMt-0.279lnTEt (3) Both the low-carbon scenario and the enhanced scenario for Shandong Jianzhu University are capable of achieving carbon peaking by 2027. From the perspectives of energy-saving renovation of existing building envelopes, improving the efficiency of lighting systems, utilizing solar photovoltaic buildings, and electrifying cooking energy, implementation pathways for carbon peaking under low-carbon and enhanced scenarios are delineated.

A comprehensive quantitative lifecycle cost and environmental impact analysis model for computing infrastructure

This paper presents a comprehensive quantitative model for quantitative assessment of the lifecycle costs and environmental impacts of computing infrastructure, with a focus on internet data centers (IDCs) and high-performance computing (HPC) facilities. The key innovation lies in the integration of interdisciplinary cost evaluation and carbon emission methods for the establishment of this quantitative model. This framework, which outlines key cost components and carbon emission factors, enables the calculation of total costs, electricity expenses, and greenhouse gas emissions throughout the lifecycle of infrastructure. With IDCs as a case study, the research clarifies the intricate cost structure associated with equipment procurement, energy usage, land acquisition, and operational expenses. This paper provides an in-depth understanding of the cost structure and environmental impact of computing infrastructure in support of sustainable decision-making in its development.•Based on established cost estimation methods, such as Lifecycle Cost Analysis (LCCA) and the Analogous Estimating Method, this study examines costs across construction and operation phases.•The Emission Factor Method is used to quantify environmental impact, emphasizing the significance of regional energy mix and power usage effectiveness (PUE).

Assessing the influence of actual LNG emission factors within the EU emissions trading system

The shipping sector has been incorporated into the European Union Emissions Trading System (EU-ETS). Undoubtedly, the carbon emission factor is a critical consideration when determining carbon allowances in the EU-ETS, known as EU Allowances (EUAs). In this study, an all-performance carbon emission test was conducted on a marine LNG engine to investigate the differences between the actual carbon emission factor and the reference value. It was observed that the actual carbon emission factor is smaller than the reference value. The test results indicate a discrepancy of approximately 3%, resulting in potential savings of 10,000 to 20,000 dollars in EUAs for a ship consuming 2000 tonnes of LNG fuel annually. Furthermore, variations in the carbon emission factor were observed under different engine operational conditions, fluctuating by around 2% at constant revolutions per minute (RPM). This suggests the need for more suitable emission testing methods to accurately reflect actual emissions and better achieve emission control goals.

Measurement methods and influencing factors of carbon emissions from residents' travel

Residents' travel is a significant source of transport carbon emissions. Revealing the mechanisms behind travel-related carbon emissions can assist transportation departments in developing effective emission reduction policies. However, current research often focuses on specific aspects such as measurement accuracy or the impact of certain factors, lacking a comprehensive review and discussion. This study addresses the research gap by conducting a systematic literature review on measurement methods and influencing factors of carbon emissions from residents' travel. Through evaluating each method's applicability and limitations, we establish a systematic framework for measuring carbon emissions from residents' travel. By selecting appropriate measurement methods, detailed quantitative analysis of influencing factors is enabled, providing crucial data for subsequent emission reduction strategies. Influencing factors are categorized into four groups, including public infrastructure, road traffic facilities, land use and population, and traveler's socio-economic attributes, with a total of nine sub-factors across these categories. The relationship between each factor and carbon emissions is examined through statistical and qualitative analyses, establishing a robust quantitative analysis methodology for future research. Finally, a roadmap for reducing carbon emissions in residents' travel is presented.