Estimates Of Carbon Emissions Research Articles

Thermal Environmental Impact of Urban Development Scenarios from a Low Carbon Perspective: A Case Study of Wuhan

Amidst the increasingly escalating global concern regarding climate change, adopting a low-carbon approach has become crucial for charting the future developmental trajectory of urban areas. It also offers a novel angle for cities to avoid high-temperature risks. This paper estimates carbon emissions in Wuhan City from both direct and indirect aspects. Then, the ANN (artificial neural network)–CA (Cellular Automata) model is employed to establish three distinct development scenarios (Ecological Priority, Tight Growth, and Natural Growth) to predict future urban expansion. Additionally, the WRF (Weather Research and Forecasting Model)—UCM (Urban Canopy Model) model is used to investigate the thermal environmental impacts of varying urban development scenarios. This study uses a low-carbon perspective to explore how cities can develop scientifically sound urban strategies to meet climate change challenges and achieve sustainable development goals. The conclusions are as follows: (1) The net carbon emission for Wuhan in 2022 is estimated to be approximately 20.8353 million tonnes. Should the city maintain an average annual emission reduction rate of 10%, the carbon sink capacity of Wuhan would need to be enhanced by 382,200 tonnes by 2060. (2) In the absence of anthropogenic influence, there is a propensity for the urban construction zone of Wuhan to expand primarily towards the southeast and western sectors. (3) The Ecological Priority (EP) and Tight Growth (TG) scenarios are effective in alleviating the urban thermal environment, achieving a reduction of 0.88% and 2.48%, respectively, in the urban heat island index during afternoon hours. In contrast, the Natural Growth (NG) scenario results in a degradation of the urban thermal environment, with a significant increase of over 4% in the urban heat island index during the morning and evening periods. (4) An overabundance of urban green spaces and water bodies could exacerbate the urban heat island effect during the early morning and at night. The findings of this study enhance the comprehension of the climatic implications associated with various urban development paradigms and are instrumental in delineating future trajectories for low-carbon sustainable urban development models.

Estimating Carbon Emissions of Northeast Brazil Railway System

This article addresses the developing of a framework to obtain specific GHG emissions for the railway system and proposes mitigation strategies. To achieve this purpose, a comprehensive life cycle assessment (LCA) method was employed with input data from various sources to analyze the contribution of energy consumption and the emissions of the railway system. This paper included gathering data from an infrastructure operation and maintenance for detailed GHG emissions impact. This study also presents a comparative analysis of the GHG emissions in different urban railway transportation systems in Northeast Brazil, providing valuable contextual insights. As a result of the combination of total GHG emissions analysis from the states of the Northeast Brazil railway system, a total of 11,996.11 metric tons of CO2 equivalent (tCO2e) was estimated. The main line traction was a prominent source of the greenhouse gas footprint, especially for the diesel traction systems at Paraiba. The proposed framework shows that significant environmental benefits can be realized with proper decision-making to increase the number of passengers–kilometer transported by rail.

Life-cycle assessment tools for road design: analysing linearity assumptions

Road infrastructure significantly impacts how people move, live, and the emissions associated with travel behaviour. The design of roads is crucial in mitigating emissions. This paper reviews existing transport Life-Cycle Assessment tools that have been developed by various entities and can be used for roads. The review focuses on several aspects in terms of data sources used in the analysis, the methods of estimating carbon emissions, the underlying software that is used to make the estimates and any limitations of the tools. A critical issue identified in Life-Cycle Assessment analysis is the erroneous assumption that relationships within the assessed systems are linear. The current tools focusing on transport infrastructure assessment were developed based on the linear assumptions and limitations of the Life-Cycle Assessment analysis. A significant research gap identified is that existing Life-Cycle Assessment tools are not integrated with the design process. The analysis is an add-on process to design and the results of an assessment are not then used iteratively to enhance the design. A case study on aggregate road design found that road area significantly correlates with emissions, slope adjustments reduce emissions, and soil type impacts emissions, suggesting future research should explore nonlinear relationships for sustainable road design.

A Perspective on the Decarbonization of the Metals Industry

The decarbonization of the metals industry is a major challenge for the energy transition. Metals are indeed essential elements in the expansion of renewable energy installations worldwide, but they also represent a relevant source of carbon emissions. Therefore, metals producers need to carefully shift their technologies towards less carbon intensive routes. After ranking all the metals in terms of world production volume and total estimated carbon emissions, the three most relevant ones have been selected: steel, aluminum and chromium. Concentrating the rest of the analysis on them, several production processes are available for implementing the decarbonization step, but none of them is currently capable of overcoming the challenge alone and being compatible with the 1.5°C trajectory. In this perspective, the main production routes are reviewed and proper combinations of proven or emerging technologies are streamlined with the aim to provide an industrially feasible approach to curb the carbon emissions from the metals industry.

Real time estimation of carbon emissions for industrial users based on load monitoring in advanced metering infrastructure

To reduce carbon emissions, it is important to monitor carbon emissions by industrial users related to electricity. Current monitoring schemes have limited effect in real-time carbon emission monitoring while the development of Advanced metering infrastructure (AMI) and emission factors for carbon-related devices introduces a fresh outlook. Hence, a method based on carbon-related load monitoring in AMI is proposed. The proposed method comprises three essential components: the one-hot component, the random convolution component, and the grid search component. The one-hot component can transform multi-state and multi-device identification into multi-classification problems, making CO2 emissions calculations easier for industrial users. The random convolution component effectively distinguishes the electricity characteristics to identify various states of carbon-related devices, while the grid search component optimizes hyperparameters to enhance recognition accuracy and decrease carbon emission monitoring errors. The effectiveness of the proposed approach is evaluated through experiments conducted on several industrial users. Comparative analysis with alternative methods demonstrates the superior performance of the proposed approach, indicating its effectiveness in accurately estimating carbon emissions for industrial users on these metrics including ACC, Recallmicro, Recallmacro, Precisionmicro, Precisionmacro, F1micro and F1macro.

Sustainable practice in gastroenterology: travel-related CO2 emissions for gastroenterology clinic appointments in Canada

Abstract Background Telemedicine is increasingly common in gastroenterology and may represent an opportunity for improving sustainability in medical care. The purpose of this study was to determine the carbon emissions related to travel for in-person gastroenterology clinic appointments. Methods We conducted a cross-sectional analysis evaluating carbon emissions associated with travel to gastroenterology appointments over a 2-week period. We determined the average number of appointments per day and used patient’s postal codes to estimate travel distances. We estimated carbon emissions based on these travel distances and completed sensitivity analyses to model methods for emissions reductions. Results We assessed 975 clinic appointments, of which 71 were excluded (eg, insufficient data, non-physician appointments), leaving 904 included appointments of which 75% were follow-up (678) and the remainder were new consultations (226). Sixteen different gastroenterologists had an average of 22.7 patients per clinic. The mean return distance travelled per appointment was 57.3 km which translates to 14.9 kg CO2 per patient visit. An average day at our clinic was equal to burning 146.6 L of gasoline or the annual carbon capture of 15.5 trees. By changing follow-up appointments or those with a travel distance over 100 km to telehealth, emissions were reduced by 77%. Conclusions We demonstrate that a relatively modest change in the number of in-person visits can save thousands of litres of gasoline emissions annually from each practicing clinician. While we cannot avoid emissions related to travel for procedure-based appointments, the use of telemedicine is one potential strategy to reduce healthcare-related emissions.

A Methodological Approach for Enriching Activity–Travel Schedules with In-Home Activities

In-home activities are inevitably important parts of individuals’ daily schedules, as people spend more time working and doing various other activities (e.g., online shopping or banking) at home. However, conventional activity-based travel demand models (ABMs) only consider travel and travel-related out-of-home activities, ignoring the interaction between in-home and out-of-home activities. To fill in this gap and increase the understanding of what people do at home and how in-home and out-of-home activities affect each other, a new method is proposed in this study. The approach predicts the types and durations of in-home activities of daily schedules generated by ABMs. In model building, statistical methods such as multinomial logit, log-linear regression, and activity sequential information are utilized, while in calibration, the Simultaneous Perturbation Stochastic Approximation (SPSA) method is employed. The proposed method was tested using training data and by applying the approach to the schedules of 6.3 million people in the Flemish region of Belgium generated by a representative ABM. Based on the statistical methods, the mean absolute errors were 0.36 and 0.21 for predicting the number and sum of the durations of in-home activities (over all types) per schedule, respectively. The prediction obtained a 10% and 8% improvement using sequential information. After calibration, an additional 60% and 68% were gained regarding activity participation rates and time spent per day. The experimental results demonstrate the potential and practical ability of the proposed method for the incorporation of in-home activities in activity–travel schedules, contributing towards the extension of ABMs to a wide range of applications that are associated with individuals’ in-home activities (e.g., the appropriate evaluation of energy consumption and carbon emission estimation as well as sustainable policy designs for telecommuting).

Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model

Abstract Wildfires in boreal forests release substantial amounts of carbon into the atmosphere. However, current land-surface models are limited in their representation of fire processes, including their ignition and spread. This study thus developed FireDL, a novel data-driven machine-learning model for the prediction of natural wildfires, and combined it with a land-surface model to better understand the impact of fire on carbon fluxes. FireDL has a two-stage deep learning structure that sequentially combines a long short-term memory (LSTM) algorithm and an artificial neural network (ANN). Preliminary random forest analysis identified fire duration as an important factor in predicting the burned area. Thus, in FireDL, the LSTM algorithm was employed to predict fire occurrence and duration, utilizing lightning, vegetation, and climate datasets. Subsequently, the ANN predicted the total burned area using the LTSM-derived fire duration predictions and climate datasets as input. FireDL produced a robust performance in predicting large fires (>10 000 ha), achieving a correlation coefficient of 0.72. The daily-scaled burned area predictions derived from FireDL were integrated into the Community Land Model version 5—Biogeochemistry (CLM5-BGC) to produce CLM5-BGC-FireDL. This integration considerably improved carbon emission estimations. Notably, the total net ecosystem exchange (NEE) estimated using CLM5-BGC-FireDL in 2019, the year with the highest recorded burned area during our study, was twice that estimated using the standard CLM5-BGC. Discrepancies in the NEE can significantly influence atmospheric CO2 levels, highlighting the importance of our fire prediction model in forecasting the burned area and carbon emissions. The use of FireDL with future climate scenarios is thus anticipated to yield valuable insights into ecosystem management and climate change mitigation strategies.

Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R2 of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

Augmented pre-training-based carbon emission accounting method using electricity data under small-sample condition

IntroductionAccurate and rapid carbon accounting method for the power industry is crucial to support China’s low-carbon transformation. Currently, carbon emission accounting methods are based on slowly updated fuel statistics or expensive monitoring equipment, resulting in high costs and delays in carbon emission estimation. Power data offers high real-time availability, accuracy, and resolution, and exhibits a strong correlation with carbon emissions. These characteristics provide a pathway for achieving rapid and precise annual carbon emission accountings. However, carbon emission data inherently exhibits small sample characteristics, making these methods less effective in small sample conditions and leading to lower accounting accuracy.MethodsTherefore, this paper proposes an augmented pre-training-based “electricity-to-carbon” method under small sample conditions.ResultsThis approach utilizes the correlation between electricity and carbon data as well as the autocorrelation characteristics of carbon emission data to construct a machine learning-based electricity-carbon fitting model for rapid and accurate carbon emission estimation. To address the challenges of small sample learning, this paper introduces an interpolation pre-training method to optimize the model’s hyperparameters and conserve samples for model training, thereby improving the model’s generalization and robustness.DiscussionCase studies on a real dataset verifies the effectiveness of the proposed method. The findings of this study can promote the development of carbon measurement technology and facilitate the low-carbon transition of developing countries.

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.

Estimation of national and provincial carbon emissions, terrestrial carbon sinks and their relative contribution to emission reductions during 1980~2020

Estimation of national and provincial carbon emissions, terrestrial carbon sinks and their relative contribution to emission reductions during 1980~2020

Spatial and temporal correlation between green space landscape pattern and carbon emission—Three major coastal urban agglomerations in China

Urban green spaces, including parks, gardens, and tree-lined streets, can play a crucial role in mitigating atmospheric CO2 levels. Understanding the distribution and dynamics of these green spaces is essential for their effective incorporation into urban planning to reduce carbon emissions. However, previous literatures have largely overlooked the integration of green space patterns in urban planning, thereby constraining our capacity for effective carbon mitigation. This study utilizes an enhanced Long Short-Term Memory network with a Self-Attention Mechanism to estimate carbon emissions and evaluates the influence of urban green spaces. Results from the Bohai Rim (CBS), the Pearl River Delta (PRD), and the Yangtze River Delta (YRD) reveal spatial clustering of carbon emissions radiating outward from core cities. Additionally, the analysis demonstrates that the number, density, shape complexity, and spatial aggregation of green spaces can significantly impact carbon emissions. Specifically, the quantity and concentration of green spaces help reduce emissions, while greater shape complexity and spatial aggregation tend to have the opposite effect. Based on these findings, the study offers insights for optimizing urban green space planning to support carbon emission reduction strategies.

A 28-time-point cropland area change dataset in Northeast China from 1000 to 2020

Abstract. Based on historical documents, population data, published results, remote sensing data products, statistical data, and survey data, this study reconstructed the cropland area and the spatial pattern changes at 28 time points from 1000 to 2020 in Northeast China. The period from 1000 to 1600 corresponds to historical provincial-level administrative districts, while the period from 1700 to 2020 corresponds to modern county-level administrative districts. The main findings are as follows: (1) the cropland in Northeast China exhibited phase changes of expansion–reduction–expansion over the past millennium. (2) The cropland area in Northeast China increased from 0.55×104 km2 in 1000 to 37.90×104 km2 in 2020, and the average cropland fraction increased from 0.37 % to 26.27 %; (3) from 1000 to 1200, the cropland area exhibited an increasing trend, which peaked in 1200. The scope of land reclamation was comparable to modern times, but the overall cropland fraction remained low. The cropland area significantly decreased between 1300 and 1600, with the main land reclamation area being reduced southward into Liaoning province. From 1700 to 1850, the cropland area increased slowly and the agricultural reclamation gradually expanded northward. After 1850, there was almost exponential growth, with the cropland area continuously expanding to the whole study area, and this growth trend persists until 2020; (4) the dataset of changes in the cropland of administrative districts in Northeast China, reconstructed based on multiple data sources and improved historical cropland reconstruction methods, significantly enhances time resolution and reliability. Additionally, the dataset shows relatively better credibility assessment results, which can provide a refined database for historical land use and land cover change (LUCC) dataset reconstruction, carbon emission estimation, climate data construction, etc. The dataset can be downloaded from https://doi.org/10.6084/m9.figshare.25450468.v2 (Jia et al., 2024).

Carbon Emission Reduction Associated With Utilisation Of Telehealth In Outpatient Clinics In An Australian Quaternary Health Service

Objective: To assess the impact of implementing telehealth in outpatient clinics on the carbon emissions associated with the delivery of health care. Design & Setting: Retrospective cohort study in large metropolitan quaternary referral health service from January 2021- December 2022. Participants: All patients who attended an outpatient clinic appointment during the study period, either in-person, via telehealth or via telephone. Main outcome measures: The estimation of carbon emissions in tonnes (t) of CO2-equivalent (CO2-e) associated with in-person and telehealth appointments based on emissions associated with travel, telehealth platform usage and N95 mask usage. Results: There were 571,121 outpatient clinic appointments during the study period. Of the appointments, 251,458 (44%) were conducted remotely, resulting in an estimated reduction in 3,629t of CO2-e emissions in the two-year period. Telehealth consultations in this time contributed 4.5t of CO2-equivalent emissions. The total emission usage of telehealth clinic was only 0.12% of emissions generated from face-to-face clinic appointments. Conclusion: Telehealth offers the opportunity of substantial carbon emissions reduction within the healthcare sector, while also providing cost and time-saving benefits for healthcare services and patients. Limitations include generalisation of transportation modes and the retrospective nature of the data collection.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

The neglect of the change in inundation area leads to overestimation of carbon emission in cascade reservoirs

Reservoir carbon emissions reflect greenhouse gases emitted by flooded land post-construction. However, pre-construction flooded land has been overlooked in previous assessments. Utilizing annual land cover data from 1990 to 2022 and pertinent parameters of cascade reservoirs in the Lancang River (LCR), we calculated the actual flooded areas of these reservoirs. Subsequently, the Tier1 model was employed to estimate the carbon emissions during the reservoir's life cycle and the annual carbon emissions from newly flooded land during construction. Our findings indicate that the LCR cascade reservoir's carbon emission throughout its life cycle is 4.324Tg CO2eq (1.818–8.879Tg CO2eq). When compared with previous results, our estimated figures (0.496–2.106 g CO2eq/(kw·h)) fall below the global hydroelectric carbon footprint's average threshold range (IPCC: 4–14 g CO2eq/(kW·h)). This implies that the previously estimated carbon emissions from the reservoir may be inflated due to the flooded land prior to reservoir construction. Notably, the nutrient state of the water body predominantly governs reservoir carbon emissions. This research sheds light on the intricacies of carbon emissions from cascade reservoirs and underscores the importance of accurately delineating reservoir boundaries and managing nutrient inputs to mitigate carbon emissions.

Considering the Environmental Impact of Cleft Lip and Palate Surgery.

We sought to calculate the waste generated by cleft lip and palate (CL/P) procedures and to increase awareness of the environmental impact of our speciality. Waste from 5 CL/P procedures was categorised into 5 streams and weighed. A carbon calculator tool was used to convert weight of waste in to estimated carbon emission over a 12-month period. The study was carried out in a university teaching hospital. This was an assessment of the waste produced from 5 paediatric CL/P procedures. Weight of waste produced as result of CL/P procedures, measured in kilograms (kg); weight of CO2, measured in kg. We found that 768.5 kg of surgical waste was generated by CL/P procedures at our centre annually. This equates to 2653 kg of CO2. This study serves as a reminder of surgeons' responsibility to oversee how the waste we produce is disposed of.

Estimation of carbon emissions in various clustered regions of China based on OCO-2 satellite XCO2 data and random forest modelling

Atmospheric carbon dioxide (CO2) stands as one of the most important greenhouse gasses, with steadily increasing concentrations attributable to human activities. In the pursuit of reaching peak carbon and carbon neutrality goals, it is essential to quantify carbon emissions and evaluate carbon reduction strategies. To establish a high-precision observation with full time series and spatial coverage, a spatio-temporal interpolation method was developed to obtain XCO2 data over mainland China at a resolution of 0.5° × 0.5° for the years 2015–2021. An east-west gradient, higher levels in the east and lower levels in the west, was observed, exhibiting a seasonal pattern of elevation in spring and reduction in summer. Subsequently, the research area is classified into seven clusters based on time-series XCO2 anomalies (ΔXCO2) and ODIAC (Open Source Data Inventory of Anthropogenic Carbon Dioxide) carbon emission data. This classification aims to emphasize the differentiation of spatial heterogeneity in carbon emissions and the results highlight that regions with high ΔXCO2 reflect higher carbon emission. Finally, the carbon emissions of each cluster were estimated by using a random forest model individually yielding an R2 of approximately 0.6. For assessing the variables influencing carbon emission predictions, the importance of each variable was calculated. Specifically, NightTime Lighting data (NTL), representing human production activities, emerged as a crucial variable influencing carbon emission predictions in most clusters. In comparison, Gross Primary Productivity (GPP) is considered a more critical variable in Southwest China (SWC), primarily owing to the intricate vegetation carbon sink system in this region. Temperature (T) emerges as a key variable influencing the estimation of carbon emissions in certain developed cities in Eastern China (EC), driven by the urban heat island effect which amplifies energy consumption, modifies land use, and impacts urban systems, influencing the spatial patterns of carbon emissions. Carbon emissions in different characteristic regions was quantified by establishing machine learning models with remote sensing data, which can provide new insights and support for refined carbon monitoring and management strategy.

Estimation and Prediction of Carbon Emissions in the Farmland Ecosystem of Kaifeng City, China

Estimation and Prediction of Carbon Emissions in the Farmland Ecosystem of Kaifeng City, China