Carbon Emission Factors Research Articles

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

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

Spatiotemporal Evolution and Driving Factors of Land Use Carbon Emissions in Jiangxi Province, China

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is of great importance for improving land use structure and promoting regional low-carbon economic development. This study, based on remote sensing and statistical yearbook data from 1995 to 2020, calculated the carbon emissions from land use in Jiangxi Province, China. Multiple spatial analysis methods and the logarithmic mean Divisia index were used to elucidate the spatiotemporal evolution and driving factors of carbon emissions, and the findings revealed the following: (1) The spatiotemporal changes in land use in Jiangxi Province during 1995–2020 were substantial as forest land accounted for 65% of the entire land area, while construction land increased by 98.1%. Cultivated land decreased the most, followed by forest land. (2) There was a fourfold rise in carbon emissions in Jiangxi Province, driven primarily by construction land, and northern areas produced higher carbon emissions compared with central and southern regions. Forest land was the main carbon sink. (3) Economic development (257.36%) and the impact of the proportion of construction land (211.31%) were the primary factors contributing to the increase in carbon emissions from land use, while other factors had inhibitory effects. This study transformed the macroscale low-carbon development strategy of cities into targeted local policies, and the research theories and methods adopted could provide scientific reference for other regions in urgent need of carbon reduction worldwide.

Comparative Analysis of Gini Coefficient, GDP, Energy Consumption, and Transportation Modes on CO2 Using NARDL (Nonlinear Distributed Lag Autoregressive Model) for the USA

The significance of the transportation sector, notably in terms of the carbon emission factor, is an undeniable fact. Along with this fact, individuals’ transportation preferences depend on their income levels. In this context, when the issue is considered, the income level in the USA pushes people toward cheap air travel. The main reason for this is that it is cheap, accessible, and transports one to their destinations quickly. Thus, from the perspective of road transportation, bus transportation is popular among the public. The reason why both air and road transportation modes are empirically evaluated together through income distribution is due to the preference of the US people. In this context, the effectiveness of active transportation on both air and highways in the USA from 1975 to 2023 is investigated by taking into consideration the income distribution. Empirical findings obtained through the FMOLS, DOLS, CCR, and NARDL models demonstrate that all independent variables, including GDP, energy use, air transportation, and the Gini coefficient, affect carbon dioxide emissions. In addition, wavelet analysis is performed to comprehend the form of and fluctuations in the series, which are vital to monitoring the periodical changes.

Examining the relationship between the built environment and carbon emissions from operating vehicles: enlightenment from nonlinear models.

Carbon emissions from urban transportation significantly contribute to overall transportation emissions and are a major cause of the continuous rise in global temperatures. Understanding the spatial distribution and influencing factors of carbon emissions from operating vehicles can aid in formulating targeted policies and promoting emission reduction. To analyze the factors influencing urban traffic carbon emissions, we calculated emissions using trajectory data from operating vehicles in Shenzhen. We then used gradient boosting regression tree methods, specifically RF, XGBoost, and LightGBM models, to analyze the impact of the built environment on vehicle emissions. We used the XGBoost model for detailed factor analysis by comparing the models. The results indicate that bus stops, intersections, housing density, metro stops, and land use mix are the top five factors influencing emissions. When road density is 0-15 km/km2, the distance from the city center is 0-6 km, and the population exceeds 2000/km2, the built environment significantly reduces vehicle emissions.

Spatiotemporal Evolution Characteristics and Influencing Factors of Industrial Carbon Emissions in the Yellow River Basin

Scientific assessment of industrial carbon emissions in the Yellow River Basin and identification of its influencing factors are of great importance for promoting green transformation, ecological protection, and high-quality development of the Yellow River Basin. Considering nine provinces in the Yellow River Basin as the research objects； using relevant data on industrial development and energy consumption in the Yellow River Basin from 2000 to 2019； and with the help of IPCC carbon emission measurement, spatial autocorrelation, and LMDI decomposition, the spatial and temporal evolution characteristics and influencing factors of carbon emissions from industries and industrial sectors in the Yellow River Basin were analyzed. Reasonable suggestions were put forward for reducing the carbon emissions from industries in the Yellow River Basin. The results showed that： ① From 2000 to 2019, industrial carbon emissions in the Yellow River Basin showed a fluctuating growth trend, with a decreasing growth rate. The spatial pattern changed from "low in the upstream and high in the middle and downstream" to "high and low value distribution," and the spatial difference gradually expanded. ② The high carbon industry was the most important source of industrial carbon emissions in the Yellow River Basin, accounting for 96.35% of the carbon emissions between the industries with a continuous growth trend, which was a significant difference. The middle and low carbon industry carbon emissions and the total proportion was low, showing different fluctuations； nine provinces and nine industrial industries had significant spatial variability. ③ Energy structure intensity, economic scale, and population scale promoted the increase in industrial carbon emissions in the Yellow River Basin and energy consumption intensity had an inhibitory effect on the increase in carbon emissions. The economic scale effect was positive and significant, which offset the negative effect of energy consumption intensity. Spatial variability was observed in the contribution value of the influence effect of the factors affecting the carbon emissions of the industry in nine provinces.

Carbon Peak Control Strategies and Pathway Selection in Dalian City: A Hybrid Approach with STIRPAT and GA-BP Neural Networks

Mitigating the rate of global warming is imperative to preserve the natural environment upon which humanity relies for survival; greenhouse gas emissions serve as the principal driver of climate change, rendering the promotion of urban carbon peaking and carbon neutrality a crucial initiative for effectively addressing climate change and attaining sustainable development. This study addresses the inherent uncertainties and complexities associated with carbon dioxide emission accounting by undertaking a scenario prediction analysis of peak carbon emissions in Dalian, utilizing the STIRPAT model in conjunction with a GA-BP neural network model optimized through a genetic algorithm. An analysis of the mechanisms underlying the influencing factors of carbon emissions, along with the identification of the carbon emission peak, is conducted based on carbon emission accounting derived from nighttime lighting data. The GA-BP prediction model exhibits significant advantages in addressing the nonlinear and non-stationary characteristics of carbon emissions, attributable to its robust mapping capabilities and probabilistic analysis proficiency. The findings reveal that energy intensity, tertiary industry value, resident population, and GDP are positively correlated with carbon emissions in Dalian, ranked in order of importance. In contrast, population density significantly reduces emissions. The GA-BP model predicts carbon emissions with 99.33% accuracy, confirming its excellent predictive capability. The recommended strategy for Dalian to achieve its carbon peak at the earliest is to adopt a low-carbon scenario, with a forecasted peak of 191.79 million tons by 2033.

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

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

A Comprehensive Quantitative Lifecycle Cost and Environmental Impact Analysis Model for Computing Infrastructure

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

Spatial-temporal dynamics and influencing factors of city level carbon emission of mainland China

Urban areas are major sources of carbon emissions, making it crucial to understand their emission characteristics for effective carbon reduction and sustainable development. Using carbon emission data from mainland China (2001–2021), we analyzed the spatio-temporal dynamics and future trends of city-level emissions and explored influencing factors using machine learning methods. Results indicate significant fluctuations in carbon emissions, with over 40 % of mainland Chinese cities experiencing a doubling in total emissions. Geospatially, cities in South Coast (SC), East Coast (EC), Northeast (NE), and NorthCoast (NC) show stronger intensity and increasing trends in carbon emissions compared to other regions, with over 80 % of cities in these regions experiencing high or higher increases. Additionally, a continued rise in carbon emissions was detected in most Chinese cities, with an average Hurst index of 0.64, indicating persistent trends. Using the XGBoost method, factors such as population density, built-up area, urban green coverage rate, and GDP were found to strongly correlate with urban carbon emissions, exhibiting significant spatial heterogeneity. This research uncovers the characteristics and influencing factors of urban-scale carbon emissions, offering valuable insights for policymakers to tailor carbon reduction strategies to the specific needs and conditions of various urban areas.

Decoupling effect and influencing factors of carbon emissions in China: Based on production, consumption, and income responsibilities

Carbon emissions at the provincial level are mostly calculated based on production responsibility, which ignores the carbon emission accounting perspectives of consumption and income responsibility. However, scientific accounting of carbon emissions from multiple perspectives is crucial to equitably distribute the responsibilities for reducing emissions. This study utilises an input‒output model for multi-regional carbon emissions to calculate carbon emissions from production-, consumption- and income-based perspectives for exploring the variability in the responsibilities for reducing emissions at the provincial level. The Tapio decoupling coefficient is used to analyse the relationship between carbon emissions and economic growth in key provinces, and the Logarithmic Mean Divisia Index (LMDI) model is used to determine the factors promoting or inhibiting the decoupling of carbon emissions under different responsibility perspectives. The results show that, firstly, the national carbon emissions illustrate the following trend: production-based emissions > income-based emissions > consumption-based emissions. Secondly, provinces in strong decoupling states are distributed in a northeast–southwest direction. Carbon emissions and decoupling situations differ among varying responsibility perspectives due to interprovincial transfers of carbon emissions. Thus, a compensation mechanism should be established to promote a fair and orderly peaking in each province. Lastly, carbon emission intensity is a factor promoting decoupling in all three perspectives. In the meantime, output scale, economic development and factor input are the major inhibiting factors for the decoupling of production-, consumption- and income-based emissions, respectively.

Carbon Peak Prediction in China Based on Bagging-integrated GA-BiLSTM Model under Provincial Perspective

Many studies have revealed potential carbon peaking pathways in China. However, to minimize the uncertainty inherent in forecasting studies, more accurate forecasting models as well as more segmented forecasting subjects are always necessary. Based on panel data of 30 Chinese provinces from 2000 to 2020, this study applies the STIRPAT model and ridge regression to examine the key influencing factors of carbon emissions in each province in China, further constructs an innovative Bagging-integrated GA-BiLSTM model for forecasting, and finally explores China's peak carbon pathways in conjunction with scenario analysis. The results show that: (1) increasing urbanization and population growth are the main reasons driving the increase of carbon emissions in most provinces; (2) there is significant variability in the carbon peaking dynamics of China's regions, which can be classified into three categories: autonomous earlier peaking, policy-guided peaking, and difficulty in peaking; (3) For most provinces, the green scenario is more effective than the development scenario in reducing the peak carbon level rather than advancing the peak time; (4) under the development and the green scenario, China's overall carbon peak will be reached in 2032 and 2028, with the peak carbon levels of 11.88 billion tons and 11.42 billion tons, respectively.

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

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

Analysis of spatial and temporal evolution and driving factors of carbon emission in Shandong Province: based on the perspective of land use

Land use/cover change is the second major contributor to carbon emissions, following energy emissions. Studying provincial land-use carbon emissions is crucial for achieving the “double carbon” goal. This study selects 16 prefecture-level cities in Shandong Province as the research object. It analyzes the spatial and temporal distribution pattern of carbon emissions in Shandong Province based on land-use data and energy consumption. In terms of net carbon emissions, this study utilizes the standard deviation ellipse and kernel density estimation to analyze net carbon emissions change from the municipal and regional perspectives. In terms of carbon ecological carrying capacity, not only the carbon ecological carrying capacity of forest and grassland was considered, but also the carbon ecological carrying capacity of crops in Shandong Province, which is a large grain province. Using the geographic detector to explore the drivers. Research findings indicate that carbon sources and sinks show a clear spatial and temporal distribution pattern, with the center of gravity of net carbon emissions extending to the northeast. Areas with high carbon ecological carrying capacity have high forest coverage, grassland coverage, and crop yields. Regarding driving factors, the urbanization rate, economic aggregate, and technological progress demonstrate significant explanatory power through single and interaction tests, suggesting that these factors are critical drivers of land-use carbon emissions within Shandong Province. Based on the spatiotemporal pattern analysis of land-use carbon emissions in Shandong Province, each city's growth rate and spatial distribution characteristics can be clarified, providing a scientific basis for the local government to formulate regional and differentiated emission-reduction policies. In addition, by exploring the driving factors of land-use carbon emissions in Shandong Province, the influence level of factors on carbon emissions can be revealed to provide references for formulating regional sustainable development strategies.

What is the role of environmental stress on public health? Asymmetric evidence on carbon emissions, ecological footprint, and load capacity factor

The aim of this paper is to analyze the effects of carbon emission, ecological footprint, which takes into account the demand side of the environment, and load capacity factor, which takes into account both the supply and demand sides of the environment, on health expenditures with conventional and quantile methods. According to the conventional co-integration approach, there is no relationship between the environment and health expenditures. The other side, the findings obtained from the quantile co-integration method, which can give robust results in the presence of tailed distributions and possible endogeneity problems and consider the asymmetric structure in the data set, show the existence of a long-term relationship between the variables. According to the coefficient estimates, while carbon emission and ecological footprint increase health expenditures, the load capacity factor decreases.

Factors Influencing Carbon Emission and Low-Carbon Development Levels in Shandong Province: Method Analysis Based on Improved Random Forest Partial Least Squares Structural Equation Model and Entropy Weight Method

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively.

A Study on Carbon Emission Reduction in the Entire Process of Retrofitting High-Rise Office Buildings Based on the Extraction of Typical Models

The building sector is one of the largest contributors to carbon emissions globally, with high-rise office buildings being a major source due to their energy-intensive operations. This study aims to address the critical issue of carbon emission reductions through the retrofitting of existing high-rise office buildings, focusing on the entire life cycle of these buildings, including the embodied, operational, and demolition phases. Existing research has primarily concentrated on energy consumption and carbon emissions during the operational phase, neglecting the carbon impact of the retrofitting process itself. This research seeks to fill that gap by quantifying the carbon reduction benefits of retrofitting across all life-cycle stages. Using data from 100 high-rise office buildings in Hangzhou’s Gongshu District, five typical models were extracted based on their construction eras and architectural features. Retrofitting strategies tailored to these models were developed, and the carbon reduction benefits were calculated using the carbon emission factor method. The primary findings indicated that the shape and orientation of buildings are crucial factors influencing the carbon reduction benefits of retrofitting. Buildings oriented east–west tend to exhibit greater carbon reductions after retrofitting. During the embodied and demolition phases, retrofitting emissions remain similar for models constructed in the same era due to consistent material inputs. However, emissions vary for models from different eras, primarily due to differences in envelope materials and subsequent material consumption. High-rise office buildings constructed between 2007 and 2021 demonstrate higher overall retrofit carbon reduction rates compared to those built before 2007, despite the latter achieving greater reductions during the operational phase. The shorter remaining lifespans of pre-2007 buildings diminish their life-cycle carbon reduction advantages. Notably, complex-shaped buildings from the same era do not necessarily exhibit lower overall retrofit carbon reduction rates compared with rectangular or L-shaped buildings, with comparable reductions per unit area. This suggests that complex-shaped buildings should not be disregarded for retrofitting based solely on shape considerations. Furthermore, the remaining lifespan of a building significantly impacts its post-retrofitting carbon reduction benefits; longer lifespans result in greater benefits, and vice versa. In practical engineering applications, structural reinforcement measures can be implemented prior to retrofitting to extend a building’s structural lifespan, ultimately enhancing its carbon reduction benefits.

Accounting for carbon emissions in social water cycle system in nine provinces along the yellow river and analysis of influencing factors.

Water resources is an essential factor to ensure the sustainable development of the society, but along with the utilization and treatment of water resources, a large amount of carbon emissions will be generated. The study of carbon emissions in social water cycle system is of great significance in promoting the achievement of carbon peaking and carbon neutrality. This study calculated the carbon emissions generated in social water cycle system in nine provinces along the Yellow River, used the Tapio decoupling model to analyze the decoupling relationship between water and carbon emissions, and constructed the STIRPAT expanded model to analyze the main influencing factors of carbon emissions. (1) The total carbon emissions of the nine provinces showed an increasing trend over time, with a growth rate of 25.13%. (2) The carbon emission intensity of water use (1.60kg/m3) and drainage (1.45kg/m3) system is higher, the carbon emission intensity of water supply (0.30kg/m3) and water withdrawal (0.56kg/m3) system is lower. (3) The relationship between water resources utilization and carbon emissions along the Yellow River is generally in a state of negative decoupling and coupling. (4) Energy structure and population growth are the main factors affecting carbon emissions in social water cycle system, while water supply quantity and water use system are secondary factors. Water use system is the main body of carbon emissions in social water cycle system, and as the water consumption increases, the carbon emissions will continue to increase. In order to reduce carbon emissions and mitigate climate change, carbon emission factors should be incorporated into water resources management.

Dynamic simulation of street-level carbon emissions in megacities: A case study of Wuhan City, China (2015–2030)

Dynamic simulation of carbon emissions (CE) in megacities is crucial for regional carbon reduction management, however, limited simulation accuracy hinders its application in carbon reduction policies. An integrated modeling framework was developed based on high-resolution multi-source data to analyze the street-level carbon emissions in Wuhan from 2015 to 2030. First, we conducted principal components analysis on the 5 driving factors of carbon emissions (including point of interests, electricity consumption, gross domestic product, population, and nighttime light) to delineate single carbon emission character region (CECR). Then, a machine learning method was used to simulating CE and explaining the contributions of different CECRs. Multi-scenarios CE accountings also were conducted with CECR simulations and an improved cellular automata model. Results shows that: (1) CE in Wuhan showed strong aggregation during the historical period. The five CECRs formed a near-concentric circle. The changes of CECR reflected the enhancement of human activity. (2) CE gradually shifted from the central urban areas to the surrounding regions during the scenario period, showing significant spatial spillover effects. Administrative districts with lower CE density exhibited greater scenario variation in total carbon emissions, indicating a higher potential for carbon reductions. (3) The total CE of Wuhan (148.11 Mt) in 2030 is projected to increase by 42.6 % compared to 2015 in the baseline scenario, representing 105 % of the low scenario and 91 % of the high scenario. The growth rate of total CE in Wuhan significantly slows down (<1 %) under all scenarios. The high-resolution dynamic simulation of CE will provide an important scientific basis for low-carbon city management in China.

Analysis of the non-equilibrium and evolutionary driving forces of carbon emissions in China's construction industry

Clarifying the historical characteristics and influencing factors of carbon emissions in the construction industry is essential for formulating targeted emission reduction policies. The study utilized Dagum Gini coefficient and geographical detector to measure and analyze the regional disequilibrium characteristics and key driving factors of carbon emissions from the construction industry in China. The results indicate that regional disparities are the primary drivers of spatial imbalances in carbon emissions from China's construction industry. Indirect and interactive carbon emission driving forces were the main drivers of the spatial imbalance of carbon emissions. The output value of the regional economy and industrial structure exerted a significance influence. Notably construction output value, industrial structure and energy efficiency emerged as the main interaction factors. From these findings, four main conclusions are drawn: Controlling the carbon emission of building materials significantly alleviates the spatial imbalance of carbon emission in China's construction industry. The principle of regional coordinated carbon emission reduction in the construction industry should be followed. The regional economic level and industrial structure differences significantly affect the non-equilibrium of construction industry's carbon emissions. Accelerate the development of energy infrastructure can help to improve the construction industry's carbon emission balance. For the first time, Dagum Gini coefficient was combined with geographical detector analysis to study the disequilibrium of carbon emissions in the construction industry. This approach identified new multi-factor interaction driving forces, contributing to a deeper understanding of carbon emissions in the construction sector.

Carbon emission measurement and regional decomposition analysis of China’s beef cattle farming industry

IntroductionWarming caused by greenhouse gas (GHG) emissions has become a global environmental issue of widespread concern, and China, as a responsible power, has the pressing task of reducing carbon emissions. China is one of the world’s major beef producers and consumers, and at the same time, beef cattle, as a large livestock, is the largest source of GHG emissions in the livestock industry.MethodsThis study considered the panel data of 31 provinces in China from 2008 to 2022. The kernel density estimation and Dagum Gini coefficient were used to analyze the spatiotemporal dynamic evolution patterns and influencing factors of carbon emissions from China’s beef cattle farming industry.Results(1) The carbon emission trajectory of beef cattle production follows a distinctive “ascend-descend-ascend” three-phase pattern. By 2022, the sector’s cumulative carbon emissions had burgeoned by 37.62% relative to 2008, reflecting an average annual escalation of 2.31%. Despite the overall upward trend in carbon emissions, significant regional differences were observed. The Central Plains region has witnessed a consistent decline, in stark contrast to the Southwest and Northeast regions, which have emerged as hotspots for heightened carbon emissions and intensified emission densities within China’s beef cattle production landscape, underscoring the intensifying significance of carbon mitigation measures. (2) The kernel density curve shows an overall rightward shift with a specific gradient effect on carbon emissions. In addition, the range of the right drag of the curve in 2022 was significantly reduced, which laterally reflects the narrowing of the difference between the provinces with the highest and lowest carbon emissions from beef cattle farming. The principal source of variance in the overall carbon emissions from beef cattle production is the disparities between regions, which accounts for an average annual contribution rate of 52.52%. Conversely, the within-region contribution rates have remained relatively stable, while those for the intensity of transvariation have witnessed a substantial rise, with annual averages of 18.31 and 28.96%, respectively. (3) Regarding the factors influencing carbon emissions reduction, environmental regulations and production efficiency significantly drive carbon emissions reduction in beef cattle farming.DiscussionRelevant government departments should actively guide farmers toward green production, establish perfect policies and regulations for low-carbon beef cattle farming, and promote low-carbon beef cattle farming models based on local conditions.