Quantitative Evaluation Index Research Articles

Accurate object localization facilitates automatic esophagus segmentation in deep learning

BackgroundCurrently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task.MethodsA total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized.ResultsThe mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions.ConclusionThe 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.

Real-Time Quantitative Evaluation on the Longitudinal Slip Performance of Spherical Steel Bearings of Long-Span Bridges

Spherical steel bearings play an important role in the normal longitudinal expansion of the main girders of long-span bridges. With the increase in service time, the wear damage will deteriorate the longitudinal slip performance of spherical steel bearings. In this research, a method of real-time quantitative evaluation on longitudinal slip performance is proposed through monitoring data analysis, correlation analysis, damage evaluation analysis, and experiment data analysis. Monitoring data analysis shows that the temperature field has a good linear relationship with longitudinal displacement. Correlation analysis shows that this relationship is well described by a time-varying multiple linear regression model. Furthermore, bearing friction is used as an index for real-time quantitative evaluation, and a large value of bearing friction indicates serious damage. An evaluation model considering the influence of temperature field and bearing frictions is proposed. The time-varying values of bearing frictions are calculated through Kalman filtering analysis. Experimental results show that the maximum evaluation error of this method is less than 5%, verifying that the proposed method is feasible for real-time quantitative evaluation on the longitudinal slip performance of bridge bearings.

Research on denoising the temperature monitoring signal of a distributed optical fiber downhole cable

Aiming at the noise mixed in the downhole cable skin temperature signal measured by the BOTDR distributed fiber optic sensing temperature measurement system, a new improved wavelet threshold function is proposed, and denoising experiments are completed based on the MATLAB platform for the measured cable skin temperature signal. The denoising results of several threshold function methods are compared and analyzed from the aspects of graphical intuitive comparison as well as quantitative evaluation indicators. The results show that the improved threshold function denoising fiber optic temperature monitoring signal has a higher SNR and lower RMSE, there is no obvious distortion phenomenon, and the denoising performance is significantly improved compared with the traditional threshold function, which has a high application value.

Proposal for a Human–Machine Collaborative Transfer System Considering Caregivers’ Lower Back Pain and Cognitive Factors in the Elderly during Transfer Movements

With the aging society in Japan, the number of elderly people residing in elderly facilities is increasing. In previous study, we developed a transfer assistive device designed to aid the elderly in transferring from the bedroom to the bathroom. Additionally, the device assists the elderly with standing and sitting to facilitate independent toileting activities. We verified that, throughout the entire transfer movement, the lumbar burden on caregivers remained below 3400 N. In this study, based on quantitative evaluation indices of transfer movements, the relationship between the lumbar burden on caregivers and factors such as psychological anxiety or cognitive impairment in the elderly during the use of a transfer assistive device was elucidated through motion analysis. We developed a control algorithm for the human–machine collaborative transfer system with the aim of alleviating the strain on the caregiver’s lower back while ensuring the elderly can use the device with peace of mind. The practicality of the control algorithm was verified.

Evaluation method of modification effect of direct-to-plant SBS modifier on asphalt

To accurately evaluate the modification effect of direct-to-plant styrene–butadiene–styrene block copolymer (SBS) modifier on asphalt, a collection method for direct-to-plant SBS-modified asphalt was proposed in this paper, the qualitative and quantitative evaluation index system was established, and the feasibility of quantitative evaluation based on fluorescence microscopy images and visual inspection of the mixing effect was evaluated, the feasibility of quantitative evaluation indicators was also verified based on the asphalt indicators, rheological properties, and asphalt mixtures's pavement performances. The research results indicate that based on fluorescence microscopy images, by observing the distribution and swelling state of direct-to-plant SBS modifiers can qualitatively evaluate the modification effect of direct-to-plant SBS modifiers on asphalt. Based on the visual inspection of the mixing effect, observe the size and quantity of unmelted particles, determine the melting state of the direct-to-plant SBS modifier, and its modification effect on asphalt can be qualitatively evaluated. For the same direct-to-plant SBS-modified asphalt, the percentage of rotation viscosity between direct-to-plant SBS-modified asphalt and traditional SBS-modified asphalt at 135 ℃ (DAη(135℃)) and discrete coefficient of 135 ℃ rotational viscosity of direct-to-plant SBS-modified asphalt (CVη(135℃)) exhibit a linear relationship with direct-to-plant SBS-modified asphalt's penetration, softening point, and ductility, the smaller the CVη(135℃), the greater the DAη(135℃). The smaller the CVη(135℃) (the larger the DAη(135℃)), the larger the dynamic shear modulus (G*), the smaller the phase angle (δ), and the larger the rutting factor (G*/sinδ) at the same scanning temperature. The relationship between CVη(135℃) (DAη(135℃)) and direct-to-plant SBS modifier on the main technical indicators and rheological performance indicators of asphalt is relatively stable. There is a clear relationship between CVη(135℃) (DAη(135℃)) and the high-temperature stability, low-temperature crack resistance, and water stability of direct-to-plant SBS-modified asphalt mixture, all of which show that as CVη(135℃) decreases (DAη(135℃) increases), the pavement performances improve.

Experimental study on static and dynamic mechanical properties of composite buffer with entangled metallic wire materials and viscous fluid

This article proposes a composite buffer based on two characteristics: porous throttling and damping energy dissipation of entangled metal wire material (EMWM). A series of quasi-static tests and low-speed impact tests were carried out to analyze its stiffness and damping characteristics. The filling degrees of cushioning force curves and energy absorption rate were proposed as quantitative evaluation indicators for the composite buffer under low-speed impact. Test results showed that the stiffness and damping of the composite buffer layer increase with the increasing density of the EMWM damping layer under quasi-static conditions, but the static loss factor fluctuates. Under low-speed impact load, the composite buffer demonstrates stable force output, with an energy absorption rate above 85% and favourable filling degrees. This work provides a practical reference for its engineering application.

Performance Evaluation of L1-Norm-Based Blind Deconvolution after Noise Reduction with Non-Subsampled Contourlet Transform in Light Microscopy Images

Noise and blurring in light microscope images are representative factors that affect accurate identification of cellular and subcellular structures in biological research. In this study, a method for l1-norm-based blind deconvolution after noise reduction with non-subsampled contourlet transform (NSCT) was designed and applied to a light microscope image to analyze its feasibility. The designed NSCT-based algorithm first separated the low- and high-frequency components. Then, the restored microscope image and the deblurred and denoised images were compared and evaluated. In both the simulations and experiments, the average coefficient of variation (COV) value in the image using the proposed NSCT-based algorithm showed similar values compared to the denoised image; moreover, it significantly improved the results compared with that of the degraded image. In particular, we confirmed that the restored image in the experiment improved the COV by approximately 2.52 times compared with the deblurred image, and the NSCT-based proposed algorithm showed the best performance in both the peak signal-to-noise ratio and edge preservation index in the simulation. In conclusion, the proposed algorithm was successfully modeled, and the applicability of the proposed method in light microscope images was proved based on various quantitative evaluation indices.

Research on the data-driven inter-well fracture channeling identification method for shale gas reservoirs

The issue of inter-well fracture channeling in shale reservoirs is becoming increasingly prominent, significantly impacting the production of nearby wells. Therefore, it is crucial to accurately determine the location of fracture channeling in order to effectively design anti-channeling measures and optimize reservoir fracturing. In this paper, a data-driven fracture propagation model and fracture channeling identification method are established. In the model, the fracture morphology is fitted by the bottom-hole flowing pressure constraint. The bottom-hole flowing pressure (pwp) calculated by the construction pump pressure and the fluid wellbore flow is mainly considered as the real solution. The bottom-hole flowing pressure (pwf) calculated by the construction displacement and the fracture morphology is used as the constraint variable, and the fracture parameters are changed using the SPSA optimization algorithm to realize the dynamic fitting of the fracture morphology. In order to accurately describe the position of fracture channeling, the seepage radius of the fracture boundary is introduced to calculate the volume of fracture reconstruction. The volume coefficient of repeated reconstruction is used as the quantitative evaluation index of fracture channeling. This approach enables an accurate depiction of the position of fracture channeling. Finally, the model method is applied to the actual fracture channeling well. The study shows that the fracture length of the well inversion is greater than the well spacing, and there is a possibility of inter-well fracture channeling. The volume coefficient of repeated reconstruction is 8%, similar to the critical fracture channeling index. There are nine fracturing sections with fracture channeling, and the maximum fracture channeling coefficient is 14.2%. This paper successfully explains the reason for cross-well fracture channeling, and its conclusion aligns with the actual monitoring results. The proposed method in this paper effectively identifies the location of fracture channeling and offers guidance for optimizing channeling prevention in subsequent designs.

Human visual system-based perceptual Mura index for quantitative Mura evaluation

We propose a new quantitative Mura evaluation metric that refers to a human perceptual Mura index (HPMI) for a given captured panel image including a Mura artifact, which considers the perceptual differences of Mura features based on the human visual system (HVS). Conventional quantitative Mura evaluation metrics are highly dependent on the contrast feature of the Mura region, in which perceptual Mura level can vary depending on the perceptual characteristics with background gray levels (BGLs) in addition to the contrast. Although various studies have tried to solve the intrinsic weakness of a contrast-based metric caused by insufficient treatment of perceptual Mura features, there is still room for reflecting the variations of human perception caused by BGLs and Mura types with HVS properties. To solve this problem, we provide two solutions to evaluate the Mura level that can reflect the perception characteristics of human eyes. First, we establish the individual evaluation metrics depending on the BGLs by formulating the relationship between the human inspection and Mura level based on the perceptive features in the Mura region. Second, we apply adaptive HVS-based preprocessing to the contrast map of the Mura image, which represents the different ratios of variation in the Mura region and background region depending on the Mura types. Consequently, the correlation between subjective ranking by multiple human inspectors and objective ranking by the proposed HPMI increases considerably, up to 0.559 at the low BGL, compared with that of benchmark methods. Furthermore, by applying HVS-based preprocessing, the correlation for subjective ranking is improved up to 0.77 in line Mura.

Rock Mass Structure Classification of Caves Based on the 3D Rock Block Index

In large-scale water conservancy and hydropower projects, complex rock structures are considered to be the main factor controlling the stability of hydraulic structures. The classification of rock mass structure plays an important role in the safety of all kinds of large buildings, especially underground engineering buildings. As a quantitative classification index of rock mass, the rock block index is very common in the classification of borehole and dam foundation rock mass structures. However, there are few studies on the classification of underground engineering rock masses. Moreover, their classification criteria have disadvantages in spatial dimension. Therefore, this paper takes the long exploratory cave CPD1 in the water transmission and power generation system of the Qingtian pumped storage power station in Zhejiang Province as the research object and launches a study on the structural classification of the rock mass of a flat cave based on the 3D rock block index. According to the group distribution of joints, the sections are statistically homogeneous. Additionally, the Monte Carlo method is used to carry out random simulations to generate a three-dimensional joint network model. The virtual survey lines are arranged along the center of the shape of the three different orthogonal planes of the 3D joint network model to represent the boreholes, and the RBI values of the virtual survey lines on each orthogonal plane are counted to classify the rock mass structure of the flat cave in a refined manner using the rock block index of the rock mass in 3D. The above method realizes the application of the 3D rock block index in underground engineering and overcomes the limitations of traditional rock mass classification methods in terms of classification index and dimension. The results show that: (1) Three-dimensional joint network simulations built on statistical and probabilistic foundations can visualize the structure of the rock mass and more accurately reflect the structural characteristics of the actual rock mass. (2) Based on the 3D rock block index, the rock mass structure of the long-tunnel CPD1 is classified, from that of a continuous structure to a blocky structure, corresponding to the integrity of the rock mass from complete to relatively complete. The classification results are consistent with the evaluation results of horizontal tunnel seismic wave geophysical exploration. (3) Based on the 3D joint network model, it is reasonable and feasible to use the 3D rock block index as a quantitative evaluation index to determine the structure type of flat cave rock masses. The above method is helpful and significant in the classification of underground engineering rock mass structures.

AHP-based quantitative evaluation of radiation safety in nuclear technology utilization units

In order to protect human health from the hazards of nuclear radiation, it is necessary to standardize the management of irradiation equipment. According to the principle of “man-machine-management-environment” intrinsic safety, analytic hierarchy process (AHP) and cloud model method were adopted. A quantitative safety evaluation index system was established from four aspects: safety management, safety environment, irradiation equipment and experimental team. Based on the actual situation of an experimental institution, the safety grade of this experimental institution was systematically analyzed. The safety grade of this experimental organization was excellent, but it still needs to improve certain aspects. Corresponding management recommendations were put forward for nuclear technology utilization units of different safety levels.

Quantitative Evaluation of Domestic Cultural and Tourism Integration Policies Based on PMC Index Modeling

Abstract This paper constructs a text mining and quantitative evaluation model for cultural and tourism integration policies based on LDA and PMC index models. We find research samples of cultural and tourism integration policies through relevant websites and databases and use the LDA model to mine high-frequency words in the policies and make a high-frequency glossary. The quantitative evaluation index system of cultural and tourism policies is constructed according to the glossary and the definitions of policy evaluation by authoritative scholars, and then the scores of each policy are calculated based on the PMC formula, and the PCM surface diagram is drawn for quantitative evaluation. The results show that 57.14% of the 63 policies belong to the excellent level, and most of the policies have high scores in terms of policy timeliness, subject, tool, field, evaluation, and function, ranging from 0.5 to 0.8. Thirteen policies were poorly rated in terms of policy nature, with only 0.333-05, and there is much room for improvement. Based on the results of the study, an optimization concept focusing on optimizing the nature of policies is proposed to improve the problems existing in the current policy formulation of cultural tourism integration.

Swin Transformer for simultaneous denoising and interpolation of seismic data

Seismic data are often characterized by low quality due to noise contamination or missing traces. Convolutional neural networks are popular in dealing with denoising and interpolation. However, fixed-size convolution kernels have limited feature extraction range, and popular networks aim at either denoising or interpolating. In this paper, we propose a Swin Transformer convolutional residual network (SCRN) for simultaneous denoising and interpolation. We step-by-step show on synthetic datasets that SCRN can effectively denoise, interpolate, or denoise and interpolate simultaneously. Following this, the trained simultaneous denoising and interpolation model is directly used to handle multi-tasks on the field datasets. We assess the reconstruction effect of SCRN based on synthetic and field datasets. Comparison methods include both supervised ones (DnCNN, Unet and RIDNet) and unsupervised ones (DDUL, MultiResUNet and DL-POCS). Experimental results show that SCRN outperforms the counterparts in terms of (1) quantitative evaluation indices, (2) event continuity and weak signal retention, (3) generalization seismic data ability to suppress various noise levels and interpolate different sampling rates, and (4) preservation of first-arriving edge signals. These results indicate that SCRN can effectively reconstruct seismic data.

Topology Comparison and Sensitivity Analysis of Fuel Cell Hybrid Systems for Electric Vehicles

The hybrid system containing the fuel cell and energy storage sources contributes to realizing hydrogen energy in road traffic applications. It is meaningful to evaluate the different forms of system topology. In this work, the quantitative comparison of topologies and sensitivity analysis of system parameters are conducted. Firstly, the output characteristics of the fuel cell, battery, and ultracapacitor are modeled by experimental data. Secondly, the dimensions of four hybrid topologies are determined by an iterative constraint method. An energy management strategy based on dynamic programming is conducted for all topologies. Then the overall economic cost is used as an indicator for quantitative evaluation, considering the initial acquisition, hydrogen consumption, and component aging. The base-case results illustrate that the fuel/cell battery semi-active topology is the least costly one. Finally, seven parameters are analyzed to determine the system cost’s key factors. The relationship between costs and driving conditions is further studied. The rankings of the normalized parameter analysis results inspire us on how to further reduce the cost of fuel cell hybrid vehicles.

Research on white feather broiler health monitoring method based on sound detection and transfer learning

Livestock-vocalization-based health monitoring methods have the advantage of being contactless and non-invasive. In this study, the authors proposed a white feather broiler health monitoring method based on sound detection and transfer learning. The health of white feather broilers in the breeding area was determined by judging the frequency of coughs in the white feather broiler audios, and a quantitative evaluation index, cough rate, was newly proposed. Thus, the white feather broiler health monitoring problem is transformed into the multi-classification problem in machine learning, and training a reliable classification model becomes critical. First, large numbers of labelled white feather broiler audios were collected. Through signal filtering, sub-frame processing, pulse extraction and endpoint detection, the white feather broiler audios were transformed into labelled frame signals. Next, sixty sound features in multiple domains were extracted, and the labelled frame signals were converted into labelled data, thus created the data set. Then, multiple non-linear classification models were trained, the best performing classification model was performed parameter optimization, and a reliable classification model was obtained. Finally, the classification model was used to identify the sound types that appear sequentially in the unfamiliar white feather broiler audio. Based on this, the cough rate of the audio was calculated, and the health of the white feather broilers in the source breeding area was determined. In practice, there are differences in the vocalizations of white feather broilers at different days-old, and training classification models to identify white feather broiler audios of each day-old would result in a large resource loss. To address this problem, the authors considered setting white feather broilers of every three days-old as a collective, and attempted to train applicable classification models, respectively. Specifically, one of the collectives was selected, large number of labelled white feather broiler audios were collected, the master data set was created, and the conventional classification model was trained. It is based on the parameter-optimized random forest, which achieved a classification accuracy of 91.25%. For the other collectives, a small number of labelled white feather broiler audios were collected, and slave data sets were created, respectively. The improved TrAdaBoost multi-classification algorithm was used to filter instances from the master data set to each slave data set, the transfer data sets were created, and the transfer classification models were trained, respectively. They achieved a classification accuracy of approximately 83%. In addition, the definition of identification accuracy was newly proposed. Test results show that, the average classification accuracy achieved by the conventional classification model was 88.93%, and the average classification accuracy achieved by the transfer classification models was approximately 82%. This indicates the strong generalization ability of the classification models. The identification accuracies achieved by several classification models were all 100%. It demonstrates the feasibility and practicality of the method proposed in this paper.

Assessment of structural robustness based on accident chain risk theory: Example of base-isolated buildings

Robustness is considered to be an important function of building structures, in addition to satisfying safety, applicability, and durability, and has received increasing attention by designers and scholars in recent years. However, the reason for the initial damage is often ignored in the current structural robustness analysis, and the chain process from structural damage to complete failure is often not described, which is unconducive to decision-making and optimization. In addition, the research on certain new or complex structural systems such as base-isolated structures has rarely been reported.Therefore, a framework for assessing the robustness of base-isolated buildings is proposed in the present work, which is grounded in analysis through accident chain risk theory. First, the damage accident chain of the base-isolated building is established, and the initial damage and the indirect consequences caused by the initial damage defined. Second, a chain risk assessment framework for the damage accident based on chain risk theory is proposed, which is employed to calculate the direct and indirect risks caused by disaster in terms of a building. Third, a quantitative evaluation index of the robustness is proposed by integrating the damage accident chain of the building structure, which is measured by the sensitivity of the indirect risk. Finally, numerical studies of one prototype base-isolated building are performed using this framework. The results of this study will broaden the notion of structurally robust optimization design for multi-hazard risks based on complex networks.

Study on Multi-Pollutant Test and Performance Index Determination of Wet Electrostatic Precipitator

A wet electrostatic precipitator (WESP) is typically installed downstream of wet flue gas desulfurization (WFGD) to remove fine particles and sulfuric acid mists from flue gases in coal-fired power plants. The emission reduction characteristics of multiple pollutants and the energy consumption data of 214 sets of WESPs (94 sets of metal plate WESPs, 111 sets of conductive Fiber Reinforced Plastic WESPs, and 9 sets of flexible plate WESPs) were tested and analyzed, and the results showed that: WESPs had a high removal efficiency on PM, PM2.5, SO3, droplets and Hg, and mostly concentrated in ≥75%, ≥70%, ≥60%, ≥70% and ≥40%, respectively. The outlet pollutant concentrations were mostly concentrated in ≤5 mg/m3, ≤3 mg/m3, ≤5 mg/m3, ≤15 mg/m3 and ≤5 μg/m3, respectively. Specific power consumption and specific water consumption were concentrated in the range of 0.5~2.5 × 10−4 kWh/m3 and ≤10 × 10−6 t/m3. The correlation analysis of multiple pollutant’s removal performance was studied and the quantitative evaluation index requirements of high efficiency WESPs were determined in this paper. The high efficiency indexes of WESPs, such as PM emission concentration, SO3 emission concentration, PM removal efficiency, SO3 removal efficiency, pressure drop, air leakage rate and specific power consumption, were ≤2.50 mg/m3, ≤2.50 mg/m3, ≥90%, ≥85%, ≤200 Pa, ≤0.5% and ≤1.3 × 10−4 kWh/m3, respectively. The high efficiency indexes of specific water consumption for metal plate WESPs and FRP WESPs were ≤2.50 and ≤0.66 × 10−6 t/m3, respectively. This study can provide valuable reference for the following energy conservation and efficiency improvement of ultra-low emission units.

Research on seismic resilience evaluation index of mid-rise CFS structures

This paper focuses on the deficiencies of Chinese standard GB/T 38591–2020, and combines the mechanical characteristics, post-earthquake damage characteristics, and repair methods of mid-rise cold-formed steel (CFS) structures to define the damage grade of structural components and revise various index parameters in the “Standard”. Based on the calculation results of 120 sets of 6-story CFS composite shear wall structures, the multi-parameter seismic resilience evaluation index and grading criteria applicable to mid-rise CFS structures are fitted, and the index values (R values) for dividing the 5 resilience grades are 0.5, 1.0, 1.25, and 1.7, respectively. By considering different building usage functions and seismic effects, the 5-story CFS structure shaking table test models and the irregular eccentric CFS structure numerical model are used to jointly verify the reasonableness and universality of the seismic resilience evaluation index and grade classification criteria. The multi-parameter resilience evaluation index system lays the foundation for the study of seismic resilience of mid-rise CFS buildings and provides new ideas for establishing quantitative resilience evaluation indexes for different building systems.

Research on the role of psychological education in college Civic Education under the background of Internet

Abstract Psychological education in colleges and universities can inject new vitality into Civic Education. In this paper, the ridge estimation of regression parameters is based on the linear regression model, the canonical form of ridge regression is constructed, and the selection method of ridge parameters is proposed by combining the ridge trace plot and the Mcdorard-Garaneau method. The prediction accuracy of the ridge regression is optimized by using the idea of partial least squares for the shortcomings of the general ridge regression. After completing the improvement of ridge regression, the quantitative evaluation indexes of psychological education and Civic Education in colleges and universities were established, and the influence of psychological education on Civic Education was studied through regression analysis. The correlation coefficient R of faculty and hardware construction of psychological education on the content richness of Civic Education reached 0.725, and the standardized regression coefficient T of the former was −0.357, while the latter was −4.257, and the former was more significant than the latter. Meanwhile, the regression coefficients of organizational management and institutional setting, faculty construction and work organization and effectiveness on the level of college students’ psychological education were 0.54, 0.26 and 0.24, respectively, with significant joint effects. This study realizes the quantitative analysis of psychological education influencing Civic Education, which helps to promote the integration and development of psychological education and Civic Education.

Quantitative Evaluation of Carbon Reduction Policy Based on the Background of Global Climate Change

High-quality carbon reduction policies play a crucial role in tackling global climate change and reducing carbon dioxide emissions. China, as the world’s largest emitter of carbon dioxide, has committed to peaking its carbon emission by 2030. This study focuses on the evaluation of 12 carbon reduction policies implemented by the Chinese government. A quantitative evaluation index system for carbon reduction policies was designed. Next, the policy modeling consistency (PMC) index method was utilized to assess the quality of these policies. The findings are as follows: Firstly, the average PMC index value of the 12 carbon reduction policies is 6.75, indicating a good performance overall. Secondly, the carbon reduction policies established by the Chinese government are generally effective; among the twelve policies analyzed, one policy received a perfect evaluation grade, four policies were graded as excellent, six policies were graded as good, and one policy received a bad evaluation grade. Thirdly, the indicators Z3, Z4, Z5, and Z9 in the evaluation index system for carbon reduction policies scored relatively low, suggesting that there are some deficiencies in terms of policy timeliness, issuing institutions, policy tools, and policy evaluation within the 12 policies. Fourthly, this study presents a PMC curved surface diagram to visually illustrate the strengths and weaknesses of the carbon reduction policies. Finally, based on the research findings, recommendations are provided to enhance the quality of carbon reduction policies.