Carbon Emission Measurement 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 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.

A method for measuring carbon emissions from power plants using a CNN-LSTM-Attention model with Bayesian optimization

Accurate measurement of CO2 emissions from coal-fired power plants is crucial for achieving China's “carbon neutrality” goal; however, traditional CO2 emission measurement methods have several limitations, and emerging prediction models rarely utilize real-time monitoring data from power plants at the micro level. To address these challenges, this study presents a novel hybrid convolutional neural network with long short-term memory (CNN-LSTM)-Attention model, leveraging real-time monitoring data to enhance the accuracy of CO2 emission predictions. The proposed model employs a hybrid architecture that leverages the strength of CNN, LSTM, and Attention mechanism. To address the challenges of multiple hyperparameters and difficult selection, the Bayesian optimization algorithm is used to optimize the hyperparameters ensuring optimal performance. Using historical operational data from a 660 MW generator set in Hubei province, the proposed model was evaluated and validated horizontally by using three evaluation metrics. Additionally, the vertical evaluation and validation of the proposed model were done using the operational data from other months. The results indicate that the proposed model achieved the highest performance across every evaluation index for predicting CO2 emissions from coalfired power plants, and the prediction accuracy of the CO2 emission in the future months is also guaranteed.

Carbon emission measurement and evaluation of large public buildings in the materialization stage

The greenhouse gas emission during its materialization stage is characterized by time-concentrated and large emissions. This study first divided the materialization stage into three segments according to the life cycle theory: building materials production, building materials transportation, and construction. A carbon emission measurement model for the materialization stage of large public buildings was established by integrating the carbon emission coefficient method, and the emission reduction rate (ERR) was proposed as an indicator for quantitatively assessing carbon emissions during the materialization stage of large public buildings. After the initial indicator system was framed, the Analytic Network Process and Fuzzy Comprehensive Evaluation methods were used to establish the evaluation model for large public buildings at the materialization stage. Last, the model was verified at a convention and exhibition center in Xiamen, China, and it shows that the production stage of building materials has a large potential for emission reduction.

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.

A Study of Carbon Emissions during the Operational Period of an Integrated Expressway Construction Station

An integrated construction station for an expressway is characterized by complex carbon emission sources and high carbon emission intensity. Conducting carbon emissions accounting makes possible a comprehensive understanding of these characteristics, enabling targeted and guided carbon reduction efforts, which is crucial for advancing the low-carbon development of expressway construction. This paper, based on an in-depth analysis of the carbon emission structure during the operational period of an integrated expressway construction station identifies, as calculation boundaries, eight categories: residential areas, station transportation, mixing stations, precast beams, steel bar yards, artificial carbon emissions, chemical reactions during construction, and construction conditions. The study adopts a “bottom-up” approach to carbon emission measurement and constructs a carbon emission model for the production and operational period of the integrated construction station, based on the carbon emission factor method. Using the SG-2 section of the Fengqiu to Xiuwu stretch of the Changxiu Expressway as an engineering case, carbon emissions accounting for each component of the integrated construction station’s operational period was conducted, and the results were compared with the station’s monitoring system. High-precision characterization and calculation of total carbon emissions, as well as emissions from each process and piece of equipment during the operational period, were achieved. The results indicate that: (1) the relative error between the overall calculation results and actual monitoring is 3.6%, verifying the model’s accuracy; (2) the monthly carbon emissions of the integrated construction station during the operational period reached 72.15 tons; (3) there is a significant difference in carbon emissions among the different processes, with the highest emissions coming from transportation and residential areas, accounting for 43.4% and 23.7%, respectively. Therefore, electrification of transportation equipment could significantly reduce the overall carbon emissions of the integrated construction station.

Dynamic Calculation Method for Zonal Carbon Emissions in Power Systems Based on the Theory of Production Simulation and Carbon Emission Flow Theory

Power systems are the main source of carbon emissions. Currently, coordinated operation strategies of the source–grid–load–storage model considering carbon emissions is primarily expanded from the generation side. For practical power systems, where multiple types of generating units coexist at a single node, it is difficult to develop unit combination strategies that simultaneously consider carbon emission factors and power flow constraints. Therefore, a new power flow calculation method based on connectivity matrix theory was proposed, aiming to address the issues of existing approaches that are too coarse and unable to accurately represent the operating states of multiple units under each node. Furthermore, a new method for dynamic calculation of regional carbon emission based on connectivity matrix and carbon emissions flow was introduced to improve the accuracy of carbon emission measurements. Firstly, a simulation model for a coordinated optimization operation based on the minimum system cost for the source–grid–load was established and an optimal flow calculation method using a connectivity matrix was introduced. Second, a dynamic carbon emission calculation method, considering electricity sources, was developed by combining the results of the optimal power flow calculation with carbon emission flow theory. Finally, the effectiveness of the approach in this article was verified by the IEEE 14-bus system example and a provincial power grid, ensuring strict adherence to the conservation principle of carbon emissions between the supply and demand sides and satisfying power flow constraints.

Analysis of the Measurement of Transportation Carbon Emissions and the Emission Reduction Path in the Yangtze River Economic Belt under the Background of “Dual Carbon” Goals

Carbon emissions from the Yangtze River Economic Belt are an important element of China’s carbon emission endeavor, and a study of its emission reduction pathway can provide a reference for the country’s overall management of carbon emission reduction. From the perspective of energy consumption, this paper uses the carbon emission factor method to estimate the carbon emissions of the transportation industry in the Yangtze River Economic Belt during 2006–2020, based on the extended STIRPAT model, considering the influence of seven factors, i.e., population size, urbanization rate, GDP per capita, transportation added value, energy structure, energy intensity, and transportation intensity, on carbon emissions. Based on these factors, a scenario analysis, combined with a forecasting model, is used to predict the peak carbon performance of the transportation industry under different development scenarios. The results show that the overall carbon emissions of transportation in the YEB from 2006 to 2020 show a fluctuating upward trend, and the downstream carbon emissions are significantly higher than those in other regions. The main factors influencing carbon emissions from transportation in different upstream, midstream, and downstream regions vary, with both population and economic factors contributing to carbon emissions, while technical factors affect them differently. There are significant differences in the peak carbon performance of transportation under different development scenarios, and the government should take effective measures to work towards achieving the goals of the low-carbon or enhanced low-carbon scenarios.

The threshold effect and driving path of the rapid transit system on tourism eco-efficiency in China

Rapid transit is important for the green development of China’s tourism industry because it profoundly affects the optimal structural layout and rational allocation of elements that impact regional tourism. Based on provincial panel data from 2009 to 2020, the entropy weight method, tourism carbon emission measurement model, Super efficiency slacks-based measure model and threshold regression model were used to determine the impact of the rapid transit system on tourism eco-efficiency in China. The findings showed that rapid transit system has increased steadily, while tourism eco-efficiency has fluctuated significantly, and the relative development characteristics of rapid transit system and tourism eco-efficiency gradually changed from “lagging rapid transit system” to “lagging tourism eco-efficiency.” In addition, the rapid transit system and the highway and civil aviation have a nonlinear relationship with tourism eco-efficiency, while the high-speed railway has not passed the threshold effect test, and at the regional level, the impacts of rapid transit system have heterogeneous characteristics. As the deepening construction of rapid transit system, its threshold effect on tourism eco-efficiency will change from positive “V” to positive “U” type, promoting the more efficient development of environmentally friendly tourism.

The calculation and distribution of CAV carbon emissions on urban transportation systems: A comparative analysis of renewable and non-renewable energy sources

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources.

Effectiveness of spatial measurement model based on SDM-STIRPAT in measuring carbon emissions from transportation facilities

To gain a deeper understanding of the carbon emission mechanism from transportation facilities, all system elements affecting carbon emissions from regional transportation facilities are identified and analyzed according to panel data from 30 regions in China. A spatial econometric model for carbon emissions from transportation facilities is constructed using the Spatial Dolbin model from 2004 to 2022 as the research period. From the results, the carbon dioxide emissions from transportation facilities added from 318 million tons in 2004 to 752 million tons in 2022, with an average annual growth rate of 4.9%. The global spatial auto-correlation coefficient was significant at the 5%, with an obvious spatial correlation between carbon dioxide emissions within a geographical range. In addition, through stability testing, the model showed high stability in both spatial lag testing and spatial error testing, demonstrating strong ability to interpret data. The research shows that the carbon emission is affected by independent variables, including population, economy, technology, and transportation, and exhibit significant spatial distribution characteristics in different regions and years, providing a basis for policy formulation and carbon emission management.

Research on Coupling Coordination of Agricultural Carbon Emission Efficiency and Food Security in Hebei Province, China

The delineation and measurement of carbon emissions in agricultural production systems constitute a complex issue involving multiple factors. Previous research in this area has been limited in terms of comprehensive carbon emission assessment throughout the agricultural production process and systematic measurement. This study focuses on both dynamic and static aspects, systematically analyzing the agricultural carbon emissions and emission efficiency in Hebei Province from 2000 to 2020. It comprehensively explores the influencing factors of carbon emissions and delves into the relationship between agricultural carbon emission efficiency and food security. The experimental results revealed the following: (1) From 2000 to 2020, the agricultural carbon emissions in Hebei Province exhibited a fluctuating downward trend, with a spatial distribution pattern where they were high in the south and low in the north. And the carbon emissions caused by chemical fertilizers and plowed land accounted for 42.6% of the total. (2) The efficiency of agricultural carbon emissions in the static dimension fluctuated at a rate of 0.0265, whereas the ML index fluctuated less in the dynamic dimension, and the agricultural industrial structure had the most significant impact. (3) The coupling coordination degree of food security and agricultural carbon emission efficiency increases with time, and “coordination” gradually dominates in spatial change. The conclusions of this study are of great significance in stabilizing grain production and achieving low-carbon production in the Hebei Province.

Carbon Emission Measurement of Prefabricated Buildings Based on LCA and Research on Energy Saving and Emission Reduction Strategy of Renewable Energy

In the face of diverse quantification methods for building carbon emissions, this article delves into the Life Cycle Assessment (LCA) approach to comprehensively measure the carbon emissions of prefabricated buildings throughout their lifespan. It meticulously identifies the carbon emission sources in prefabricated buildings and analyzes the measurement models relevant to their emissions in both physical and chemical stages. Prefabricated buildings hold profound implications for transforming the construction industry and advancing its sustainable development path. Examining the energy-saving characteristics and emission reduction potential of prefabricated buildings from a life-cycle perspective, this article analyzes the carbon emission measurement model during the prefabricated building transformation stage. This comprehensive analysis of the building atomization path and influential carbon emission factors lays a theoretical foundation for transitioning prefabricated buildings towards energy-saving and emission reduction strategies. Using the LCA method, carbon emissions were calculated, revealing a positive correlation with building size. Notably, through the prefabricated construction method detailed in this article, carbon emissions were significantly reduced by 30% compared to traditional construction methods.

Monitoring high-carbon industry enterprise emission in carbon market: A multi-trusted approach using externally available big data

Carbon markets are widely recognized as effective strategies for regulating carbon emissions from the enterprises of the high-carbon industry. Accurate monitoring of emissions in these enterprises is crucial for ensuring fair and trusted carbon trading. However, traditional monitoring methods have encountered various challenges including poor accuracy, low frequency, significant hysteresis and weak credibility. Addressing these concerns, this paper introduces a novel, multi-trusted, high-frequency monitoring approach for the measurement of both direct and indirect carbon emissions of enterprises, utilizing externally available multi-source big data, vertical federated long short-term memory network with a self-attention mechanism (VF-LSTMSA) and additive homomorphic encryption. The efficacy of this model is thoroughly assessed via case studies on six hourly datasets from the power and steel sectors. Our principal findings include: (1) In the current scenario, the proposed VF-LSTMSA model demonstrates superior federated efficiency, improving with the inclusion of more federated participants. (2) The VF-LSTMSA model surpasses other prevalent AI models, achieving a Mean Absolute Percentage Error (MAPE) of less than 9% across power and steel datasets. (3) The advanced high-frequency carbon emission monitoring method exhibits errors of +0.28%, +1.29%, and −0.41% in the current scenario monitoring task, suggesting that the model effectively leverages multi-source externally available big data to precisely monitor the target enterprise's carbon emissions while safeguarding multi-party data security. These findings suggest that the proposed monitoring method can accurately track corporate hourly carbon emissions, significantly improving the carbon trading and verification processes for enterprises

Carbon Emission Measurement Method of Regional Power System Based on LSTM-Attention Model

With the acceleration of the green and low-carbon transformation of the power system, it is very important to calculate and analyze the carbon emissions of the urban power system. In order to effectively grasp the carbon emission distribution of power system and reduce the carbon emission of power system, this paper proposes a systematic carbon emission measurement method for regional power system. Firstly, the quantitative analysis model of driving factors for regional power system carbon emissions is constructed, and the direction and measures of low-carbon transformation and green collaborative development of regional power system are proposed. Secondly, energy consumption scenarios under different constraints are established to support the collaborative control path of CO2. It provides key data and a theoretical basis for the low-carbon development of the power industry. Finally, a systematic carbon emission measurement method based on LSTM-Attention is proposed. Finally, the effectiveness of the proposed method is verified by an example analysis, and the effective path of carbon emission reduction in the future is analyzed.

Rapid carbon emission measurement during the building operation phase based on PSO–SVM: electric big data perspective

With the rapid development of urbanization in China, urban energy consumption increases rapidly, leading to energy shortages and environmental pollution, of which building operational energy consumption carbon emissions (BECCE) account for a large proportion. It has a vital impact on global warming and urban green and sustainable development. Chengdu city in Sichuan Province is taken as the research area in this paper. First, basic information and power data on four types of single buildings, including large-sized buildings, small- and medium-sized buildings, government agencies, and residential buildings, are collected. Second, the characteristics of the four types of buildings are extracted, and the calculation model of BECCE (“electricity-carbon” model) based on particle swarm optimization algorithm–support vector machine (PSO–SVM) is constructed, and the model is trained and verified using the method of five-fold cross-validation. Then, according to the mean absolute error (MAE), root mean square error (RMSE), and R2 evaluation indicators, the constructed “electricity-carbon” model is compared and evaluated. Finally, the generalization ability of the “electricity-carbon” model is verified. The research results show that (1) the “electricity-carbon” model constructed in this paper has a high accuracy rate, and the fitting ability of the PSO–SVM model is significantly better than that of the support vector regression (SVR) model; (2) in the testing stage, the fitting situation of large buildings is the best, and MAE, RMSE, and R2 are 858.7, 1108.6, and 0.91, respectively; and (3) the spatial distribution map of regional BECCE can be quickly obtained using the “electricity-carbon” model constructed in this paper. The “electricity-carbon” model constructed in this paper can provide a scientific reference for building emission reduction.

Study on real-time estimation of carbon emissions based on the non-intrusive power load monitoring

Monitoring carbon emissions, as well as evaluating and controlling the level of carbon emissions, are important prerequisites for combating climate change and promoting sustainable development. In this paper, real-time estimation of carbon emissions based on non-intrusive electricity load monitoring is investigated, existing machine learning approaches and related issues of load identification and marginal carbon emission factors are elucidated, and existing carbon emission monitoring approaches are analyzed and summarized. The methods of real-time carbon emission measurement are summarized, and their development is anticipated. Most of the existing methods for measuring and analyzing carbon emissions in power systems are unable to meet the development needs of real-time carbon emission calculation and have the disadvantages of higher installation and maintenance costs and poorer economy of monitoring equipment. Therefore, in this paper, a real-time carbon emission monitoring scheme based on deep learning is used to estimate carbon emissions by dividing them into direct carbon emissions and indirect carbon emissions.

Toward sustainable consumption: Exploring the role of environmental innovation and international trade on consumption‐based carbon emissions in OECD countries

AbstractEnvironmental degradation is a severe problem around the globe, which includes a range of worries such as weather extremes, biodiversity loss and heatwaves. The scientific community has widely recognized the urgent need to address these challenges and transition towards sustainable sustainability. One key aspect of sustainability is the concept of eco‐innovation, which involves the production processes that minimize ecological impact of productive activities. Businesses across the world are including eco innovation in their strategies, which have great impact on environmental performance. This study explores the linkage between eco innovation and environmental performance achieved by OECD nations over the period of 1990–2021. We investigate the impact that international trade, renewable energy consumption, and economic activity have on environmental performance. This study focuses on consumption‐based approach for measuring carbon emissions. Previous studies have focused on production‐based approach to measure carbon emissions. Using advanced econometric approaches, we find that eco innovation, exports and renewable energy consumption abate CO2 emissions, while imports and economic activities increase CO2 emissions.

Exploring the Impact of Digital Inclusive Finance on Agricultural Carbon Emissions: Evidence from the Mediation Effect of Capital Deepening

Carbon emissions from agriculture should not be underestimated. With the aim for carbon peaking and carbon neutralization and the help of digital inclusive finance, the effective reduction in carbon emissions in agriculture and animal husbandry production is crucial to achieving China’s carbon emission reduction goals. We used the balanced panel data of 31 provinces in China from 2011 to 2021 to study this issue. We empirically tested the impact, mechanism, and heterogeneity of digital inclusive finance on agricultural carbon emissions based on the systematic measurement of agricultural carbon emissions. The results revealed that (1) the development of digital inclusive finance has a significant inhibitory effect on agricultural carbon emissions, and it is an important path to reduce agricultural carbon emissions. (2) Through the intermediary effects’ analysis, it was found that capital deepening is an important transmission mechanism for the promotion of agricultural carbon emission reduction through digital inclusive finance. (3) Further analysis using the quantile regression model reveals that the impact of digital inclusive finance on agricultural carbon emissions is significantly negative at different quantiles. (4) Through the spatial Durbin model, digital inclusive finance has a space carbon enhancement effect. Finally, we put forward suggestions to promote the development of low-carbon agriculture by paying attention to the technical effect of digital inclusive finance, strengthening the connection and cooperation between various regions and promoting the carbon emission reduction role of capital deepening.

Spatial differences, dynamic evolution and influencing factors of China's construction industry carbon emission efficiency

Improving the construction industry carbon emission efficiency (CICEE) is crucial for achieving sustainable development. To promote low-carbon development in the construction industry, it is essential to measure carbon emission efficiency (CEE) and analyze spatial differences, dynamic evolution, and influencing factors. This study measures CICEE in 30 provinces in China from 2005 to 2019 and evaluates CEE using the minimum distance to a strong efficient frontier (MinDS) model with undesirable outputs. Subsequently, the Dagum Gini coefficient and its decomposition, as well as spatial autocorrelation analysis, are used to explore the sources of spatial differences and the spatial clustering pattern of CEE. The dynamic trend of CEE is analyzed through kernel density estimation, traditional and spatial Markov chains. Finally, geographical detectors are used to detect the explanatory factors and their interactions on spatial differences in CEE. The results of this study show that the CICEE presents an increasing and then decreasing trend, with the highest CEE in the eastern region, followed by the central and northeastern regions, and the lowest in the western region. Additionally, the eastern region exhibits the highest intra-regional differences and the highest inter-regional differences with the western region. Meanwhile, CEE shows a positive spatial correlation, with high-high (H-H) clustering in the eastern region and low-low (L-L) clustering in the western and northeastern regions. Polarization has been evident throughout the entire country and its four regions in recent years. It is challenging to achieve the CEE transfer through rapid advancement, and the efficiency of neighboring provinces will influence the potential transfer of the local province. Finally, factors such as enterprise scale, economic development level, degree of openness to the outside world, innovation level, industrial structure, and energy consumption structure all affect the spatial differences in CEE, with the interaction effect being higher than the single factor. This study presents a novel computational model to measure CICEE, analyzes the structural factors contributing to the spatial differences in CICEE, and provides theoretical support for the synergistic improvement of CEE across different regions. Combining with spatial autocorrelation analysis, the spatial distribution characteristics of CICEE are analyzed from the static level. This study provides a comprehensive examination of the evolution trend of CICEE, focusing on its dynamic evolution characteristics and the long-term transfer dimension. Additionally, geographical detector technology is introduced for the first time to analyze the influencing factors of spatial differences in CICEE. providing scientific evidence for the sustainable and coordinated development of different regions in China's construction industry. Furthermore, this study also contributes to the development of varied strategies for improving CICEE in China.