Carbon Emission Inventory Research Articles

Advanced method for compiling a high-resolution gridded anthropogenic CO2 emission inventory at a regional scale

ABSTRACT As global climate change accelerates, the need for precise and high-resolution carbon emission inventories becomes increasingly urgent, particularly in rapidly developing regions. This study introduces an advanced methodology for constructing regional-scale gridded anthropogenic CO2 emission inventories, integrating the latest energy consumption data and remote sensing technologies. By systematically calculating emissions across various sectors and energy types, and utilizing Geographic Information System (GIS) for spatial distribution, we have generated a high-resolution emission map with a 1 km × 1 km grid. The accuracy of the map was validated through regression analysis with the Multi-resolution Emission Inventory for China (MEIC) dataset, ensuring its reliability. This work provides critical insights for targeted emission reduction strategies, contributing to the formulation of sustainable development policies that align with global climate objectives.

Innovation and application of inter-provincial carbon emission transfer accounting model in China’s domestic production network

The New Development Paradigm will result in the significant development of domestic production networks and the accelerated growth of carbon transfers among provinces in China. However, the existing value chain or the trade of intermediate goods decomposition method cannot completely account for the carbon content of intermediate goods. So the paper developed a accounting model for inter-regional intermediate goods trade based on input-output model. The most significant advantage of this accounting model is that by further decomposing final output into three components—final consumption within the region, final consumption flowing to other regions, and final output flowing to other regions as intermediate goods that are not returned to the region—it achieves a more comprehensive decomposition of the value chain in comparison to the established models. This approach allows for the tracking of longer value chains and the accounting for intermediate goods inflows and outflows simultaneously. Furthermore, the accounting of trade in intermediate goods can be conducted for any number of countries, regions, and sectors within the input-output system, thereby providing a foundation for the comprehensive accounting of inter-regional carbon transfers within production networks. With the input-output tables and carbon emission inventories from the CEADs (the China Carbon Emissions Accounting Database), the paper has calculated the changes of the carbon transfer among provinces in the China’s domestic production network from 2012 to 2017 and find that the inter-provincial intermediate goods trade and carbon transfer among provinces is increasing significantly. Each province has a strong incentive to overuse the carbon embodied in the intermediate goods from others, but lacks the motivation to reduce their own carbon emission. In the inter-provincial transfer of the carbon content of intermediate goods in China’s domestic production network, the difference between the average value of the ratio of the carbon content of intermediate goods from other provinces used by each province and that supplied for use by other provinces to the ratio of the carbon content of intermediate goods produced by itself increased by 13.6% between 2012 and 2017. Only a few provinces are evolving towards a win-win between economic and environmental benefits, while most are still facing the evolutionary dilemma in choosing between economic and environmental benefits. In the future, we should comprehensively explore the cooperative governance of carbon emission reduction in the domestic production network, including establishing a national standard for calculating the carbon transfer in domestic production network, improving the carbon emission responsibility sharing mechanism and carbon emission reduction compensation systems.

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Carbon emissions from road transportation in China: from past to the future.

Road transportation is an important contributor to carbon emissions. China's car ownership is rapidly increasing, ranking first worldwide; however, there are limited data about carbon emission inventories. This study assesses carbon emissions from road transportation from the past to the future across China, using market survey, COPERT (Computer Programme to Calculate Emissions from Road Transport) model, and a combination method of principal component analysis and backpropagation neural network. From 2000 to 2020, the national carbon emissions from road transportation grew from 11.9 to 33.8 Mt CO2e, accounting for 0.47% of national total emissions by then. Trucks generally emit a higher proportion (77.3%) of total emissions than passenger cars (18.9%); however, the emission proportion of passenger cars (18.9-31.0%) has increased yearly. The carbon emissions at the prefecture level show an urban agglomeration trend, decreasing from the eastern coastal areas to central China. Future car ownership is expected to grow rapidly at 3.1% during 2021-2049, but only half of that growth rate during 2051-2060. Those vehicles are expected to contribute carbon emissions of 27.2-39.1 Mt CO2e under different scenarios in 2060. Scientifically reducing emissions and innovatively reducing the carbon emission coefficient, combined with a reasonable new energy vehicle growth scenario, are efficient methods for reducing national carbon levels. This study demonstrates that the uncertainty is within an acceptable range. This work details the carbon emission inventories associated with road transportation in China and provides basic data for developing a better carbon reduction policy for China's car industry.

Embodied CO2 emissions of equity portfolios for Chinese asset managers

The 2015 Paris Agreement has set out the climate change target of limiting global warming to 1.5 °C, which poses a serious challenge to countries to reduce emissions. As the world’s largest carbon emitter, promoting the realization of the “dual-carbon” goal is the key to realizing China’s green transformation and high-quality development. Chinese asset managers play active roles in the capital market as an important channel of asset allocation. Currently, the vast majority of Chinese asset managers hold high percentages of high-carbon industries in their portfolios, and lack quantitative data of their carbon footprints embodied in equity investments, which faces huge carbon-related risks. Therefore, it’s an urgent need to comprehensively and scientifically measure financed emissions of Chinese asset managers, which is of great significance for asset managers’ carbon risk management and sustainable investment. This paper develops a detailed inventory of carbon emissions for equity portfolios managed by Chinese asset managers from 2010 to 2020, which stands as a pivotal reference for in-depth analysis of emission characteristics.

Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data

Carbon emission inventories in urban built-up areas are critical for formulating effective emission reduction policies. Nighttime light data, reflecting socioeconomic activities, is routinely used as a proxy for estimating emissions. However, the reliability of this proxy is limited in areas with minimal or absent lighting and demonstrates variability according to the urban development. To address these limitations, a novel approach was developed. Firstly, it enhances the representation of human activities under different light values through integrating light values and population distributions into multidimensional proxy features. Secondly, cities with different socio-economic development were clustered to minimize the development disparities within the same cluster. Four clusters were detected including ‘Agriculture-dependent & Low-density’, ‘Agriculture-dependent & High-density’, ‘Industry-oriented’, and ‘Tertiary-oriented’. Then A multilayer perceptron regressor, refined with a moving average method, was employed to model the nonlinear relationships between emissions and these proxy features. The results show a significant increase in modeling accuracy when regional development disparities are incorporated through this clustering strategy. Additionally, the Shapley Additive Explanatory method was utilized to analyze the influence of regional development heterogeneity on the estimation model. It demonstrated that areas with high light intensity and population significantly influence emission estimates in all clusters, except for the ‘Industry-oriented cluster’. This finding underscores the varying effectiveness of proxy features across different regions, emphasizing the importance of considering regional development disparities. Finally, based on the estimated inventory obtained, spatio-temporal hot spot detection pinpointed eastern and central China as significant emission hot spots. This research not only enhances the data resources for managing carbon emissions but also underscores the need for inter-city cooperation strategies to achieve China's carbon reduction targets.

Inventory and Distribution Characteristics of Carbon Emissions from Agricultural Sources in Henan Province from 2011 to 2020

Agriculture is an important part of Henan's economic development, and agricultural production is also an important source of greenhouse gas emissions. In this paper, the activity level data of agricultural carbon emissions in 18 cities in Henan Province were collected. Based on the calculation method provided by the Guidelines for Compiling Provincial Greenhouse Gas Inventories and referring to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, agricultural CO2, CH4 and N2O emissions were carefully estimated. An agricultural greenhouse gas inventory was compiled for Henan Province from 2011 to 2020. Then, based on the spatial distribution and emission intensity, the carbon emissions of each city in Henan Province in 2019 were further analyzed. The results show that carbon emissions decreased from 57,603,500 tons in 2011 to 44,517,300 tons in 2020, with an average annual decrease rate of about 2.66%. The total agricultural carbon emissions in Henan showed a downward trend, and agricultural land was the dominant factor of carbon emissions, accounting for about 47% of the total carbon emissions over the years.

Inventory, Dynamic Evolution, and Scenario Projections of Agricultural Carbon Emissions in Shandong Province, China

The reduction in agricultural carbon emissions (ACEs) in Shandong Province is essential to China’s carbon peak and carbon neutrality objectives. In this regard, we constructed an ACE inventory for Shandong Province at a resolution of 1 km × 1 km, integrating the emission factor method with geographic information system (GIS) technology. Building upon this, we explored the dynamic evolution patterns of ACEs using kernel density estimation and conditional probability density estimation. Additionally, long short-term memory networks were trained to predict ACEs under various scenarios. The results showed that: (1) ACEs in Shandong Province exhibited two stages of change, i.e., “rise and decline”. Notably, 64.39% of emissions originated from the planting industry. The distribution of emissions was closely correlated with regional agricultural production modes. Specifically, CO2 emissions were predominantly distributed in crop cultivation areas, while CH4 and N2O emissions were primarily distributed in livestock breeding areas. The uncertainty of the emission inventory ranged from −12.04% to 10.74%, mainly caused by emission factors. (2) The ACE intensity of various cities in Shandong Province is decreasing, indicating a decoupling between ACEs and agricultural economic growth. Furthermore, the emission disparities among different cities are diminishing, although significant spatial non-equilibrium still persists. (3) From 2022 to 2030, the ACEs in Shandong Province will show a continuous downward trend. By 2030, the projected values under the baseline scenario, low-carbon scenario I, and low-carbon scenario II will be 6301.74 × 104 tons, 5980.67 × 104 tons, and 5850.56 × 104 tons. The low-carbon scenario reveals greater potential for ACE reduction while achieving efficient rural economic development and urbanization simultaneously. This study not only advances the methodology of the ACE inventory but also provides quantitative references and scientific bases for promoting low-carbon, efficient, and sustainable regional agriculture.

Promote the decarbonization pathways of eco-industrial parks by considering their CO2 emissions structures and characters

China has proposed the goals of a carbon peaking target in 2030 and a carbon neutrality target in 2060 to mitigate climate change. In China, Eco-industrial parks (EIPs) are one of the platforms for achieving energy conservation and emission reduction. This study aims to investigate the future CO2 emissions reduction potential by evaluating two different types of national EIPs in China. Firstly, we constructed carbon emission inventories for EIP-N in Zhejiang Province and EIP-L in Shandong Province. Subsequently, leveraging these inventories, we developed an integrated model aimed at predicting the carbon emission peaks for both EIPs and discerning the main contributors. Secondly, we took EIP-N and EIP-L as examples and applied the Long-range Energy Alternatives Planning (LEAP) model to establish three different scenarios for analyzing the future trends in carbon emissions. In a Business as usual (BAU) scenario (continue the current energy conservation and emission reduction policies), EIP-L can achieve carbon peaking, while the carbon emissions of EIP-N will continue to increase. In the Emissions control (EMC) scenario (considering the reduction of fossil fuels and the increased use of cleaner energy sources), EIP-N and EIP-L are projected to peak in 2029 and 2027, respectively. In the Reinforce mitigation (RFM) scenario (with fewer emissions from fuels and a higher share of clean energy generation), both EIPs are expected to achieve carbon peaking by 2025. Finally, we proposed the deep decarbonization of EIPs with different industrial characteristics. This study applied the LEAP model to the EIP scale, explored the paths of deep decarbonization development in different EIPs under the constraint of dual-carbon targets, providing a demonstration for China and other developing countries to achieve the goal of carbon capping in EIPs during rapid industrialization.

Better cities better lives: How low-carbon city pilots can lower residents’ carbon emissions

Cities generate substantial carbon emissions while facing serious climate challenges. The construction of low-carbon cities is a vital measure to cope with climate change. China has implemented three batches of low-carbon city pilots since 2010, with advocating low-carbon lifestyles and consumption patterns for residents as an indispensable component. However, there is limited research on the impact of pilot cities on carbon emissions of residents, as well as a lack of theoretical guidance and accurate data. Based on 281 prefectural-level cities in China, this study established the difference-in-differences model to evaluate the impact of low-carbon city pilots on residents' carbon emissions. By introducing the Attitudes, Behaviours, and Circumstances theory as the analytical framework and compiling a direct carbon emission inventory of residents, this study attempts to fill the research gap. The results indicate that low-carbon city pilots could lower residents' carbon emissions by 7.3% annually, with a gradual strengthening trend over time. This study presents and validates three influencing mechanisms of the pilot policy, including improving green attitudes, advocating low-carbon behaviours, and optimizing living circumstances. The heterogeneity analysis demonstrates that the eastern and central regions with higher levels of economic development have generated significant policy effects. Although both the northern and southern pilot cities have lowered residents’ carbon emissions, the former has exhibited a stronger policy effect. Based on these findings, this study proposes several policy recommendations: innovate attitudinal guidance, nudge behavioural choices, shape external circumstances, and combine local characteristics.

Estimation Method of Covariance Matrix in Atmospheric Inversion of CO2 Emissions

Atmospheric inversion of CO2 Emissions is based on the correction of prior carbon dioxide flux inventories using concentration monitoring data and atmospheric transport models to obtain posterior carbon dioxide flux. In atmospheric inversion studies, fixed covariance functions are commonly used to generate covariance matrices, and the hyperparameters in the covariance functions are empirically estimated. In this study, we design and implement an ideal experiment based on meteorological data from the central urban area of Zhengzhou, using WRF-STILT to generate sensitivity matrices and construct real carbon emission inventories and prior inventories. Based on the real carbon emission inventories and sensitivity matrices of monitoring stations, simulated observation concentration values are generated. Firstly, based on the observed concentration values, sensitivity matrices of monitoring stations, prior inventories, and constructed covariance matrices, the values of hyperparameters are determined based on maximum marginal likelihood estimation. Then, the influence of different prior covariance functions on the inversion results is tested, and it is found that the prior covariance matrix generated by the balgovind covariance function is most suitable for the experimental data.

Carbon footprints: Uncovering multilevel spatiotemporal changes of ship emissions during 2019–2021 in the U.S.

Quantifying and understanding changes in carbon emissions is essential for the U.S. shipping industry to reduce carbon emissions, especially after its return to the Paris Agreement. We estimated carbon emissions from 48,321 ships in the U.S. Exclusive Economic Zone (EEZ) using the power-based method based on 3.6 billion Automatic Identification System (AIS) reports. We explored the characterization of carbon emissions from the national, regional, and port levels during 2019–2021 by allocating emissions on a 1 km*1 km grid through an activity-weighted method. The results show: (1) Due to the COVID-19 pandemic, emissions within the EEZ show a temporal trend of decreased and then rebound, specifically from 32.628 Tg in 2019 to 30.741 Tg in 2020 and then bounced to 31.786 Tg in 2021. The spatial differences in emissions show significant heterogeneity; (2) There are significant differences in emissions by vessel type, flag, and operational mode for the four regions of the U.S. (Great Lakes, Gulf Coast, Pacific Coast, and Atlantic Coast). Thus, emissions in these regions show different variability patterns over three years. Notably, “port congestion” led to record high emissions on the Pacific Coast; (3) Containerized cargo contributes the most to port core area emissions, so most ports with higher throughputs have higher emissions, with Long Beach and Los Angeles having the highest. Emissions from coastal ports are high and volatile, while inland ports are low and stable. This study provides the U.S. with a high spatiotemporal resolution inventory of carbon emissions from ships, and the findings are expected to provide some reference for controlling ship emissions.

Carbon reduction potential of a rain garden: A cradle-to-grave life cycle carbon footprint assessment

With emphasis on constructing low-carbon cities, the renovation of old communities highlights the importance of renewing rainwater management facilities. Rain garden is an exceptional Nature-based Solution for rainwater management. When applying them to renovate old communities, the carbon footprint should be considered. This study focused on renovating facilities in the Greater Bay Area and utilized the Energy Expert platform to calculate rain garden carbon footprint with the Life Cycle Assessment. The carbon reduction benefits during the operation were estimated. This study accounting the life cycle carbon footprint of the 5455 m2 rain garden was 1117.42 tCO2e. The carbon footprint contribution, from highest to lowest was the end-of-life phase, materials production phase, construction phase, use and maintenance phase. Regarding carbon emission inventory, waste soil landfill, fertilizer acquisition, and waste soil recycling ranked high. Transportation also accounted for a remarkable carbon emission. The rain garden showed great potential for climate change mitigation, with a total and net carbon reduction of 1927.41 and 809.99 tCO2e, respectively. To reduce the carbon footprint of the rain garden, we should focus on waste soil treatment and transportation. Recommendations included increasing waste soil recycling, in situ recycling and green transportation.

Ecological Restoration Effectively Mitigated pCO2 and CO2 Evasions From Severely Polluted Urban Rivers

AbstractUrban rivers are significant hotspots of CO2 emissions into the atmosphere, playing important roles in global carbon emission inventories. However, little is known about the effect of ecological restoration on CO2 dynamics in severely polluted urban rivers, and this strongly hindering our understanding of the positive effects of human activities on CO2 emissions in urbanized aquatic ecosystems. We measured CO2 partial pressure (pCO2) and fluxes in nine severely polluted urban rivers with varying degrees of ecological restoration, and assessed the relationships with water physicochemical parameters, pollution levels and basin environmental investment. Our results indicate that urban rivers with high pollution loadings were indeed hotspots of CO2 evasion. However, fully restored rivers had generally lower pCO2 and CO2 emissions than partially restored rivers and were observably lower than unrestored rivers, suggesting that watershed eco‐restoration could effectively reduce CO2 evasion. In particular, environmental investment per unit basin area had a significant negative correlation with CO2 evasion and could explain 51%–85% and 51% of total variability in pCO2 and CO2 fluxes among nine urban rivers respectively, emphasizing the potential benefits of carbon emission regulation resulting from positive environmental management. Nutrient removal and sewage interception during watershed eco‐restoration were key processes reducing pCO2 and CO2 fluxes in polluted urban rivers. pH alteration kept close correlations with pCO2 and acted as a sensitive regulator of CO2 evasion in the nine rivers. We highlight the importance of considering the coupling effects of pollution and restoration on the spatiotemporal variability of CO2 evasion and the uncertainty of related monitoring methods in urban watersheds.

Estimates of Global Forest Fire Carbon Emissions Using FY-3 Active Fires Product

Carbon emissions from forest fires release large amounts of carbon and have important implications for the global and regional carbon cycle and atmospheric carbon concentrations. Considering the significant spatial and temporal variations in different forest fires, this study explores the relationship between different forests and carbon emissions from forest fires. This study developed a high-resolution (0.05° × 0.05°) daily global inventory of carbon emissions from biomass burning during 2016–2022. The inventory estimates of carbon emissions from biomass burning are based on the newly released FY-3 data product, satellite and observational data of biomass density, and spatial and temporal variable combustion factors. Forest fire carbon emissions were assessed using active fire data from FY-3 series satellites from 2016 to 2022, and it was linearly compared with GFED, FEER, and GFAS data on time and spatial scales with R2 of 0.7, 0.73, and 0.69, respectively. The results show spatial patterns of forest cover and carbon emissions, with South America, Africa, South-East Asia, and northern Asia as high-emission zones. The analysis shows an overall upward trend in global forest fire carbon emissions over the study period. Different types of forests exhibited specific emission patterns and temporal variations. For example, most needleleaf forest fires occur in areas with low tree cover, while broadleaf forest fires tend to occur in areas with high tree cover. The study showed that there was a relationship between inter-annual trends in forest fire carbon emissions and land cover, with biomass burning occurring mainly in the range of 60–70% tree cover. However, there were also differences between evergreen broadleaf forest, evergreen needleleaf forest, deciduous broadleaf forest, deciduous needleleaf forest, and mixed forest indicating the importance of considering differences in forest types when estimating emissions. This study identifies the main sources of carbon emissions from forest fires globally, which will help policymakers to take more targeted measures to reduce carbon emissions and provide a reliable basis for appropriate measures and directions in future carbon mitigation actions.

A Plant Species Dependent Wildfire Black Carbon Emission Inventory in Northern Eurasia

AbstractWildfire emission inventories are usually applied with biome‐scale emission factors for atmospheric modeling. However, emission factors measured for different plant species vary substantially within the same biome. We apply the species‐specific emission factors and refine the Fire Emission Inventory‐northern Eurasia (FEI‐NE), and derive the wildfire black carbon emission inventory in northern Eurasia from 2002 to 2015. Our new inventory produces 61% more black carbon emissions than current estimates based on Global Fire Emission Database (GFED) and 33% less than FEI‐NE. Model simulations with different inventories are compared with ground‐based and satellite retrievals of aerosol absorption optical depth (AAOD). Compared with the Ozone Monitoring Instrument, the normalized root mean square deviation of AAOD over northern Eurasia is reduced from 1.0 under FEI‐NE to 0.95 through application of the new inventory. This study reveals the importance of applying sub‐biome‐scale emission factors for wildfire inventories development and revisiting emissions uncertainty in atmospheric modeling.

From political pledges to quantitative mapping of climate mitigation plans: Comparison of two European cities

BackgroundUrban agglomerates play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. Atmospheric monitoring of greenhouse gases offers an independent and transparent strategy to measure urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as adjust to rapidly changing urban landscapes.ResultsOur study of Paris and Munich demonstrates how climate action plans, carbon emission inventories, and urban development plans can help design optimal atmospheric monitoring networks. We show that these two European cities display widely different trajectories in space and time, reflecting different emission reduction strategies and constraints due to administrative boundaries. The projected carbon emissions rely on future actions, hence uncertain, and we demonstrate how emission reductions vary significantly at the sub-city level.ConclusionsWe conclude that quantified individual cities’ climate actions are essential to construct more robust emissions trajectories at the city scale. Also, harmonization and compatibility of plans from various cities are necessary to make inter-comparisons of city climate targets possible. Furthermore, dense atmospheric networks extending beyond the city limits are needed to track emission trends over the coming decades.

Research on Carbon Emission Structure and Model in Low-Carbon Rural Areas: Bibliometric Analysis

As the energy crisis and environmental problems are exacerbated, rural carbon emissions have gradually attracted increasing attention. Meanwhile, rural areas have the important function of ecological carbon sequestration, so the research field of carbon emissions has gradually expanded from urban to rural areas in recent years. To systematically sort out the research progress regarding low-carbon rural areas, a total of 583 papers published during 2013–2023 were acquired through the bibliometric analysis method from the “professional field of science of building technology” in the core database of Web of Science. Then, the research stage, author cooperation, institutional cooperation, national distribution, and keyword co-occurrence of “low-carbon rural areas” were analyzed via data visualization analysis software VOSviewer, and the current hot issues, such as carbon emission calculation list and elements, carbon emission measurement method/model, and energy saving and emission reduction paths, were further summarized and reviewed. The statistical analysis results show that: (1) in most of the current research on rural carbon emissions, geographical characteristics and rural carbon emissions have been rarely analyzed; (2) there is a lack of interdisciplinary research, e.g., less cooperation with geographic information or other disciplines; (3) the differences and characteristics of rural carbon emission factors in different economies and regions have rarely been analyzed. Based on the analysis, this research pointed out that future research can be comprehensively carried out in formulating rural carbon emission inventory, establishing a multi-scale and multi-regional statistical method and so on. This research also provides a comprehensive analysis and summary of the existing research on the structure and model of carbon emissions in low-carbon rural areas and also presents the problems that need to be paid attention to in the future research, pointing out the research direction for relevant scholars.

Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port

IntroductionIn recent years, the adverse effects of escalating maritime trade and international shipping– particularly in regard to increased greenhouse gas emissions and their impact on human health– have come to the fore. These issues have thus instigated a surge in pressure to enhance the regulation of shipborne carbon emissions.MethodsThe study utilized the automatic identification system (AIS) data, Lloyd’s register data, and pollutant emission parameters to calculate the carbon emissions from the main engine, auxiliary engine, and boiler of vessels under varying sailing conditions, utilizing the dynamic method of ships. In relation to geographic information and ship trajectory, a comprehensive inventory of ship carbon emissions was developed, revealing pronounced spatiotemporal characteristics. To assure the accuracy of the substantial AIS dataset, procedures including data cleaning, trajectory integration, data fusion, and completion were executed. Such processes are indispensable, given the potential for transmission and storage errors associated with AIS data. To forecast CO2 emissions over diverse time intervals, a temporal fusion transformer model equipped with attention mechanisms was employed.ResultThe paper furnishes a case study on Tianjin Port, wherein a high-resolution carbon emissions inventory was devised based on AIS data acquired from vessels. This inventory was subsequently employed to generate multi-feature predictions of future carbon emissions. Given the optimal parameter configuration, the proposed method attained P50 and P90 values of 0.244 and 0.118 respectively, thereby demonstrating its efficacy.DiscussionRecognizing the sources of ship carbon emissions in this region and forecasting such emissions in the future substantiates that this method accurately portrays the laws of ship carbon emissions. Our study provides a scientific basis for decision-making in port and pollution management, enabling the creation of targeted emission reduction policies for ships.

Japanese urban household carbon footprints during early-stage COVID-19 pandemic were consistent with those over the past decade

As urbanization accelerates worldwide, substantial energy and services are required to meet the demand from cities, making cities major contributors to adverse environmental consequences. To bridge the knowledge gap in the absence of fine-grained city-level climate protection measures due to data availability and accuracy, this study provides a detailed carbon emission inventory for analyzing the monthly fluctuations based on citizens’ daily consumption behaviors. Here, carbon emissions embodied in approximately 500 household consumption items were calculated in 47 prefectural-level cities in Japan from 2011 to June 2021. We analyzed the results considering the regional, seasonal, demand, and emission way-specific aspects, and compared the emission before and during the COVID-19 pandemic. Notably, the carbon footprints during the pandemic were consistent with the previous level despite downtrends in specific categories. This study provides an example of utilizing city-level emission data to improve household green consumption behavior as references for enriching city-level decarbonization paths.