Carbon Emission Factors Research Articles

Spatial-temporal evolution of carbon emissions and spatial-temporal heterogeneity of influencing factors in the Bohai Rim Region, China.

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.

Multi-objective optimal scheduling considering low-carbon operation of air conditioner load with dynamic carbon emission factors

As global temperatures rise and climate change becomes more severely. People realize that air conditioning systems as a controllable resource and play an increasingly important role in reducing carbon emissions. In the past, the operation optimization of air conditioning systems was mainly oriented to user comfort and electricity costs ignoring the long-term impact on the environment. This article aims to establish a multi-objective model of air-conditioning load to ensure user temperature comfort performance and reduce the total cost (i.e., electricity cost and carbon emission cost) simultaneously. Multi Sand Cat Swarm Optimization (MSCSO) algorithm combined with gray target decision-making (GTD) is used to explore optimal solution. Meanwhile four competitive strategies are applied to validate the effectiveness of the proposed method, i.e., genetic algorithm (GA), MSCSO-comfort objective, MSCSO-total electricity cost objective and unoptimization. The simulation results show that the MSCSO-GTD based objective method can significantly reduce total costs while taking into account appropriate indoor temperature comfort.

Innovative measurement, trade-off-synergy relationship and influencing factors for agricultural net carbon emissions and effective supply of agricultural products in China

Sensitive zone of global climate change has been formed in China, and it has become a hot topic how can agriculture ensure food security and the supply of important agricultural products while achieving the “Dual Carbon” goal in the country. Based on such background, this paper uses the IPCC carbon emission calculation method, environmental input-output model and economic-water-carbon coefficient method to measure agricultural net carbon emissions, adopts bivariate spatial auto-correlation analysis and SYS-GMM to explore separately the relationship between agricultural net carbon emissions and effective supply of agricultural products, as well as the carbon reduction effect, growth effect and reasonable range of green technology innovation. The results show that: (1) China's agricultural net carbon emissions reveal a spatial distribution of “higher in the east than in the west than in the center” and a temporal characteristic of increasing year by year; China's effective supply of agricultural products shows an increasing trend and a spatial distribution of “higher in the east than in the center than in the west” in 2006–2012 and “higher in the east than in the west than in the center” in 2013–2020. (2) In 2006, 2010, 2015 and 2020, the number of provinces that belong to low-low agglomeration trade-off zone, low-high agglomeration synergy zone, non-significant zone, high-low agglomeration non-trade-off-synergy zone and high-high agglomeration trade-off zone averagely accounted for 12.500 %, 30.000 %, 26.667 %, 9.167 % and 21.667 % of the totality, respectively. (3) The carbon reduction and production growth effects of green technology innovation both show an inverted “U-shape”, and green technology innovation is conducive to both reducing agricultural net carbon emissions and improving supply of agricultural products when it is within a reasonable range of greater than 0.930. (4) Green technology innovation not only has significant spatial and temporal heterogeneity impact, but also exhibits a differential effect on productive agricultural carbon emissions, agricultural trade carbon emissions, agricultural carbon sinks, total output of agricultural products and agricultural net imports in international trade. Therefore, it is proposed that China should establish and improve green technology innovation incubation platforms, guide all participants to ensure the investment and application of green technology products within a reasonable range, formulate and implement regional differential policies and plan in accordance with local conditions, drive ultimately coordinated promotion of agricultural carbon emission reduction and product supply guarantee and lay an important foundation for achieving high-quality economic development and efficient ecological protection.

Study on life-cycle carbon emission factors of electricity in China

Abstract As China’s largest carbon-emitting sector, the power sector has been the subject of extensive attention from relevant scholars and organizations. As China’s most widely used secondary energy source, greenhouse gas (GHG) emissions from electricity often account for a significant share of total corporate or product carbon emissions. In this study, we calculated the life-cycle carbon emission factors of China’s electric power based on the ISO 14067 and PAS 2050 standards and the current development of China’s power generation industry in recent years. This study makes a preliminary prediction of China’s electricity life-cycle carbon emission factors in 2025 and 2030. The results show that the life-cycle carbon emission factors of China’s thermal, solar, hydro, wind, and nuclear power generation are 0.9976 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$, 0.0142 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$, 0.0188 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$, 0.0100 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$, and 0.0122 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$, respectively, and ultimately obtain the life-cycle carbon emission factors for China’s comprehensive electricity footprint is 0.6835 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$. Thermal power generation has a much larger carbon emission factors than other power generation modes, tens to nearly a hundred times larger than other power generation modes and contributes more than 90% of the carbon emission factors of China’s comprehensive electricity footprint; among the remaining modes of power generation, wind power generation have relatively small carbon emission factors. The predicted carbon emission factors for China’s electric power in 2025 and 2030 are 0.6144 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$ and 0.5154 ${\textsf{tCO}}_2\textsf{e}/\textsf{MWh}$, respectively. This study improves the accuracy of accounting for product carbon emission factors and provides data references to peer organizations, enterprises, and the government to carry out product carbon emission factors accounting.

Research on carbon-neutral calculation model of urban parks based on life-cycle assessment: a case study from Beijing, China

Abstract Urban parks are an essential component of the urban carbon sink system. Calculating a city’s overall carbon balance is significant by predicting when urban parks will achieve carbon-neutral. This research is based on the theory of life-cycle assessment and uses carbon emission factors to calculate the project’s carbon emissions. Using the established model, we calculated the urban park with a total area of 6.5 hectares in a certain city in Fangshan District, Beijing, China. The results indicate that the carbon emission intensity of the city park during the construction materials production phase is $5.97$${\mathrm{tCO}}_2\mathrm{e}/\mathrm{ha}$; during the construction phase, it is 18.947${\mathrm{tCO}}_2\mathrm{e}/\mathrm{ha}$; and during the operation phase, it is $5.97{\mathrm{tCO}}_2\mathrm{e}/\mathrm{ha}/\mathrm{Year}$. The total carbon reduction of the photovoltaic system in the first 25 years is $260.007{\mathrm{tCO}}_2/\mathrm{ha}$. Carbon sinks have a relatively minor impact on achieving carbon neutrality goals for urban parks. The case urban park will take 20–21 years to achieve the carbon-neutral target. We propose a future low-carbon strategy for urban parks through the study results. This will also contribute to developing uniform evaluation standards for carbon neutrality in urban parks.

Energy, exergy, economic, and life cycle environmental analysis of a novel biogas-fueled solid oxide fuel cell hybrid power generation system assisted with solar thermal energy storage unit

Biogas production and its derived hydrogen production technology have broad application prospects. In this paper, an integrated biogas power generation system with solid oxide fuel cells is proposed, which mainly consists of four units: a solar thermal energy storage unit, a biogas production and hydrogen generation unit, a SOFC-MGT unit, and a waste heat utilization unit. The presented system is first studied using energy, exergy, economic, and life cycle environmental analyses and the survey results are contrasted with those of renewable energy systems discussed in the references. Besides, a parametric study is conducted to explore the effect of thermodynamic parameters of solar irradiance, ambient temperature, work fluid temperature, reforming temperature, inlet H2O/CH4 ratio of the reformer, and the current density of SOFC. Results indicate that the proposed system's energy efficiency, exergy efficiency, electric power output and exergy destruction are 43.29%, 37.4%, 414.16 kW (net power generation is 351.43 kW) and 1434.59 kW, respectivily. Moreover, the system's levelized cost of electricity and carbon emission factor are $0.076/kWh and 335.6gCO2eq/kWh, having good economic and environmental benefits simultaneously. The parametric study shows that PTSC can be designed shorter with more solar irradiance and high ambient temperature. Besides, the increase of inlet H2O/CH4 ratio and reforming temperature make the system's energy efficiency fall by 2.5% and 20%. For every 300 A/m2 increase in the current density of SOFC, the system efficiency rises by 1.01%.

Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach

This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong.

Carbon Emission Accounting during the Construction of Typical 500 kV Power Transmissions and Substations Using the Carbon Emission Factor Approach

Carbon emission calculation during power transmission and substation construction provides valuable insights into the trend of carbon emissions and the development of low-carbon power grids. In this regard, this study divides the power transmission and substation construction process into production, transportation, and construction stages based on the sources of carbon emissions and employs a life cycle assessment to calculate the total carbon emissions using the carbon emission factor method for typical 500 kV projects. The results show that in the construction process the production stage contributes the most carbon emissions, with material and equipment production for power transmission accounting for 78% and 14% of the total emissions, respectively. The transportation and construction stage contribute 1% and 7% of the total emissions, respectively. For substations, material and equipment production contribute 67% and 30% of the total emissions, respectively. The transportation and construction phases contribute 1% and 2% of the total emissions. Through the qualitative and quantitative analysis of the carbon emission results, the construction scale and the topography and geology have significant impacts on carbon emissions from power transmission and substation projects. Finally, some targeted recommendations for carbon emission reduction for power transmissions and substations are proposed based on the influencing factors of each stage of the construction.

Process-based evaluation of carbon emissions from the on-site construction of prefabricated steel structures: A case study of a multistory data center in China

Prefabricated steel structures are being promoted in China as an important measure to reduce building carbon emissions (CEs). Currently, high-quality carbon emission factors and calculation methods that identify the sources of CEs from construction process are lacking. This study proposed a process-based method for calculating on-site construction CEs and empirically demonstrated its feasibility using a case of a multistory steel structure with typical column-beam systems. Six construction processes comprising 38 subprocesses were first categorized, then the energy and materials consumed for each subprocess were determined to obtain the first-hand CE parameters. The CEs from hoisting the first- and second-section columns were 10.52 kgCO2/column and 14.41 kgCO2/column, respectively, and the CEs from their installation were in the range 38.16–69.18 kgCO2/column. The CEs from hoisting the primary and secondary beams using a car crane were 7.73 kgCO2/beam and 4.52 kgCO2/beam, respectively, with lower CEs of 2.40 kgCO2/beam and 2.22 kgCO2/beam, respectively when a tower crane was used for hoisting. The 14 connection types used to install the primary beams and the 11 connection types used to install the secondary beams had CEs of 13.84–125.96 kgCO2/beam and 10.39–43.49 kgCO2/beam, respectively. The total on-site construction CE was subsequently calculated by summing the CEs from each process. This method was able to track the CE sources for each construction process and their contributions to identify those with high CE intensities. In addition, the case study was evaluated to provide specific feedback on the current national standard for calculating CEs from building construction.

Net-zero energy transition in ASEAN countries: The evolutionary model brings novel perspectives to the cooperative mechanism of climate governance

In an era marked by escalating climate change, the fragile ecological balance faces increasing strain. Whilst significant knowledge exists regarding the accumulation of carbon emission within the Association of Southeast Asian Nations, little is known about when and how countries could reach net-zero emission goal as agreed in Paris Agreement. For this purpose, our study examines the primary driving factors of carbon emission from 1990 to 2020 across the ten countries using the Logarithmic Mean Divisia Index model. We leverage the random forest model to explore the net-zero scenarios and the Autoregressive Integrated Moving Average approach to identify the evolutionary trajectories of carbon emission trends. Our findings underscore the imperative need for expediting decarbonization efforts, emphasizing the urgency for widespread adoption of clean technologies and substantial investment in green initiatives. Countries at similar stages of progress might establish a cooperation mechanism of clean energy base construction, energy storage allocation and policy formulation. These insights can help us better estimate future demand of clean energy, explore strategies for decarbonization, and inform historical commonalities of carbon emission growth.

An Investigation of The Impact of Carbon Emission Factor Selection on Carbon Accounting of Electric Power Equipment

With the implementation of China’s carbon peaking and carbon neutrality goals, the power industry as a key industry urgently needs to carry out comprehensive carbon accounting work. Carbon accounting of electric power equipment is an important part of carbon accounting in electric power industry. In the process of carbon accounting for electric power equipment, different carbon emission factors may lead to different final accounting results. Based on this, this paper calculates the carbon emission factors of two main raw materials required for the production of electric power equipment in different regions and different years, and applies them to the carbon footprint calculation of SFPZ-240,000/330 transformer. According to the above results, the influence of carbon emission factor selection on carbon accounting of electric power equipment is analyzed.

A Study on the Influence Factors of Agricultural Carbon Emissions in Hunan Province Based on Random Forest Model

Agricultural carbon reduction contributes to the realization of “carbon peak and carbon neutrality” strategy. This article analyzes the previous years’ data of agricultural carbon emissions in Hunan Province, and obtains the variation of carbon emissions; Using the random forest method to analyze the influence factors of agricultural carbon emissions in Hunan Province, it is proven that unit crop production value, unit energy carbon emissions in agriculture, and unit agricultural GDP energy consumption are the three most critical factors affecting agricultural carbon emissions in Hunan Province. LDMI method is used for validation. Sensitivity analysis was conducted on factors such as agricultural energy consumption, total production value of AFAF(Agriculture, forestry, animal husbandry, and fishing), total agricultural production value, crop yield, crop planting area, and non-urban population. The first five changes have a positive impact on agricultural carbon emissions, while non-urban population has a downward and then upward trend.

Data-driven analysis of carbon emissions from buildingization under the CRISP-DM framework

Abstract As a crucial pillar of the national economy, the healthy and stable development of the construction industry provides strong support to the national economy. In the current developmental context, it is particularly important to address the relationship between growth and low-carbon emission reduction. This paper proposes a study of carbon emissions from buildings using the CRISP-DM framework. According to the current situation of carbon emissions from building construction, data on the building stage is obtained from two parts: prefabricated works and on-site pouring. Because the original list data of engineering materials is partially missing, the data is processed by the weighted average method for each building material usage and carbon emission to avoid affecting the analysis results of the overall data. Combining the carbon emission measurement formula, carbon emission factor, and CRISP-DM, the carbon emission measurement model for buildingization is constructed using CRISP-DM. According to the model, the carbon emission in the transportation phase of prefabricated building materials is 1039925.978 kgCO2e, and the carbon emission of C30 concrete is the largest, with a value of 510,156.027 kgCO2e. In addition, the carbon emission in the transportation phase of cast-in-place building materials is 180.812 kgCO2e, of which the largest carbon emission is crushed stone (85.007 kgCO2e). This study can provide a reference for the construction industry in each province to set the carbon emission reduction target according to the local conditions, which is of practical significance to promote the low-carbon development of building construction.

Research on Regional Differences and Influencing Factors of China's Carbon Emissions

Research on Regional Differences and Influencing Factors of China's Carbon Emissions

Towards Low-Carbon Agricultural Production: Evidence from China's Main Grain-Producing Areas

Low-carbon agricultural development is inseparable from the support of green finance. This study utilizes the GDIM model and the ARDL method to examine several key drivers of agricultural carbon emissions in China's main grain-producing areas from 2000 to 2020. The objective is to offer a valuable policy framework for green finance in promoting low-carbon agricultural production. The results indicated that despite being a driving factor in agricultural carbon emissions, the effect of the chemical fertilizer input shifted from rising to falling, and an agriculture-based CO2 EKC was confirmed. Moreover, from the perspective of the causal links between agricultural carbon emissions and their drivers, this study found that chemical fertilizer input had both long- and short-term driving effects, agricultural mechanical application had a long-term inhibiting effect, and the aging of the agricultural labor force had a short-term driving effect. The findings of this paper will help policymakers develop green financial resources to guide capital towards low-carbon agricultural production.

Decoupling Effect and Influencing Factors of Transportation Carbon Emissions in Hainan Province

Decoupling Effect and Influencing Factors of Transportation Carbon Emissions in Hainan Province

Evaluation and Comparison of Research Methods on Driving Factors of Carbon Emission in Industrial Parks

As the primary territories of regional carbon emission and energy consumption, most industrial parks only simply stack technology and policy under the urgent goal of emission reduction and carbon reduction. As a result, certain measures may even have negative impacts. As the primary means to determine the characteristics of carbon emissions in industrial parks, studying the driving factors of carbon emissions and proposing targeted measures can effectively achieve the carbon emission reduction goals. However the mechanism analysis of driving factors of carbon emission in industrial parks is the important means to study the characteristics of carbon emission. The selection of driving factors is many and complicated, and the selection of mechanism analysis methods is different, so it is difficult to have a comparison of research results Based on the quantitative relationship model between carbon emissions and driving factors and the method of action mechanism analysis in industrial parks, this paper evaluates and compares the relevant studies, summarizes their advantages and disadvantages, and makes relevant comments and prospects. For providing reference for the practice of driving factors of carbon emission in industrial parks, and put forward the basis for the research of carbon emission prediction.

Multi-source data-driven technology research on carbon emission dynamics prediction in electric power industry

Abstract This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

Regional spatial econometric Analysis of carbon footprint of energy consumption based on clustering algorithm

Abstract The structure of energy consumption and reducing the carbon footprint has become an important issue in the field of carbon and energy conservation. This paper adopts spatial econometric Analysis to construct a framework for analyzing the influencing factors of carbon emissions based on the STIRPAT model. It applies the K-means algorithm to effectively cluster and classify the energy consumption of different regions. Then, the article analyzed these clustering results in depth using the Kaya constant equation to calculate the carbon emissions of each area. The results of the carbon footprint analysis reveal that the Gini coefficient of carbon emissions in the eastern region peaked at 0.352 in 2014, while decreasing to a low of 0.284 in 2019. the western and central areas have the highest Gini coefficients of carbon emissions at 0.271 and 0.248, respectively. furthermore, from 2015 to 2022, the ecological pressure on the carbon footprint of the whole industry has always remained at 3.033 above, reaching a historical high of 3.433 in 2022.The application of this paper not only helps to solve the problems in the existing carbon footprint evaluation methods, but also provides a scientific basis for more effective management and reduction of carbon emission.

Theoretical research on carbon emission factors of distributed smart distribution network under high proportion of renewable energy access

In the context of the “dual carbon” goal, a high proportion of renewable energy connected to the grid to replace traditional high-carbon units has become a general trend. Taking the high proportion of renewable energy power system as the research object, the medium and long term planning of the installed capacity of source, grid, load and storage of power system is carried out considering the two dimensions of investment and operation maintenance, and the optimization model of source, grid, load and storage of power system is established. A low-carbon dispatching model considering carbon emission and demand response of distribution network is also proposed. Firstly, considering the characteristics of distribution network receiving power from the main network, the carbon emission model of distribution network based on carbon emission flow theory is constructed, and the low-carbon index of distribution network is proposed. Secondly, the improved dynamic carbon emission factor of distribution network is proposed, which is used as a guide signal to provide reference for low-carbon electricity consumption of distribution network users. According to the characteristics of users in the distribution network, the industrial demand response model aiming at low dispatch cost and low carbon and the residential demand response model aiming at electricity consumption satisfaction and low carbon satisfaction are constructed respectively.