Three-Dimensional Reconstruction of Indoor Scenes Based on Implicit Neural Representation.

Reconstructing 3D indoor scenes from 2D images has always been an important task in computer vision and graphics applications. For indoor scenes, traditional 3D reconstruction methods have problems such as missing surface details, poor reconstruction of large plane textures and uneven illumination areas, and many wrongly reconstructed floating debris noises in the reconstructed models. This paper proposes a 3D reconstruction method for indoor scenes that combines neural radiation field (NeRFs) and signed distance function (SDF) implicit expressions. The volume density of the NeRF is used to provide geometric information for the SDF field, and the learning of geometric shapes and surfaces is strengthened by adding an adaptive normal prior optimization learning process. It not only preserves the high-quality geometric information of the NeRF, but also uses the SDF to generate an explicit mesh with a smooth surface, significantly improving the reconstruction quality of large plane textures and uneven illumination areas in indoor scenes. At the same time, a new regularization term is designed to constrain the weight distribution, making it an ideal unimodal compact distribution, thereby alleviating the problem of uneven density distribution and achieving the effect of floating debris removal in the final model. Experiments show that the 3D reconstruction effect of this paper on ScanNet, Hypersim, and Replica datasets outperforms the state-of-the-art methods.

Neural implicit surface reconstruction of freehand 3D ultrasound volume with geometric constraints.

Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite improvements in smoothness, continuity, and resolution from deep learning approaches, research on surface reconstruction in freehand 3D US is still limited. This study introduces FUNSR, a self-supervised neural implicit surface reconstruction method to learn signed distance functions (SDFs) from US volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D queries sampled around volumetric point clouds to approximate the surface, guided by two novel geometric constraints: sign consistency constraint and on-surface constraint with adversarial learning. Our approach has been thoroughly evaluated across four datasets to demonstrate its adaptability to various anatomical structures, including a hip phantom dataset, two vascular datasets and one publicly available prostate dataset. We also show that smooth and continuous representations greatly enhance the visual appearance of US data. Furthermore, we highlight the potential of our method to improve segmentation performance, and its robustness to noise distribution and motion perturbation.

RobotSDF: Implicit Morphology Modeling for the Robotic Arm.

The expression of robot arm morphology is a critical foundation for achieving effective motion planning and collision avoidance in robotic systems. Traditional geometry-based approaches usually suffer from the contradiction between the high demand for computing resources for fine expression and the insufficient detail expression caused by the pursuit of efficiency. The signed distance function addresses these drawbacks due to its ability to handle complex and arbitrary shapes and lower computational requirements. However, conventional robotic morphology methods based on the signed distance function often face challenges when the robot moves dynamically, since robots with different postures are modeled as independent individuals but the postures of robots are infinite. In this paper, we introduce RobotSDF, an implicit morphology modeling approach that can express the robot shape of arbitrary posture precisely. Instead of depicting a whole model of the robot arm, RobotSDF models the robot morphology as integrated implicit joint models driven by joint configurations. In this approach, the dynamic shape change process of the robot is converted into the coordinate transformations of query points within each joint's coordinate system. Experimental results with the Elfin robot demonstrate that RobotSDF can accurately depict robot shapes across different postures up to the millimeter level, which exhibits 38.65% and 66.24% improvement over the Neural-JSDF and configuration space distance field algorithms, respectively, in representing robot morphology. We further verified the efficiency of RobotSDF through collision avoidance in both simulation and actual human-robot collaboration experiments.

CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation.

Deep neural networks have shown excellent performance in medical image segmentation, especially for cardiac images. Transformer-based models, though having advantages over convolutional neural networks due to the ability of long-range dependence learning, still have shortcomings such as having a large number of parameters and and high computational cost. Additionally, for better results, they are often pretrained on a larger data, thus requiring large memory size and increasing resource expenses. In this study, we propose a new lightweight but efficient model, namely CapNet, based on convolutions and mixing modules for cardiac segmentation from magnetic resonance images (MRI) that can be trained from scratch with a small amount of parameters. To handle varying sizes and shapes which often occur in cardiac systolic and diastolic phases, we propose attention modules for pooling, spatial, and channel information. We also propose a novel loss called the Tversky Shape Power Distance function based on the shape dissimilarity between labels and predictions that shows promising performances compared to other losses. Experiments on three public datasets including ACDC benchmark, Sunnybrook data, and MS-CMR challenge are conducted and compared with other state of the arts (SOTA). For binary segmentation, the proposed CapNet obtained the Dice similarity coefficient (DSC) of 94% and 95.93% for respectively the Endocardium and Epicardium regions with Sunnybrook dataset, 94.49% for Endocardium, and 96.82% for Epicardium with the ACDC data. Regarding the multiclass case, the average DSC by CapNet is 93.05% for the ACDC data; and the DSC scores for the MS-CMR are 94.59%, 92.22%, and 93.99% for respectively the bSSFP, T2-SPAIR, and LGE sequences of the MS-CMR. Moreover, the statistical significance analysis tests with p-value < 0.05 compared with transformer-based methods and some CNN-based approaches demonstrated that the CapNet, though having fewer training parameters, is statistically significant. The promising evaluation metrics show comparative results in both Dice and IoU indices compared to SOTA CNN-based and Transformer-based architectures.

Evaluation of regional comprehensive development efficiency under low-carbon policy: based on optimized DDF-GML combined with unsupervised clustering method.

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist-Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics.

Factor mismatch measurement of resource-based cities: Evidence from all-round optimization total factor productivity decomposition framework.

In-depth analysis of the factors affecting the transformation of resource-based cities can provide effective support for the transformation and development of resource-dependent regions. How to comprehensively identify the factors affecting the transformation of resource-based cities is a complex problem. This study starts from the total factor productivity model and focuses on the two core basic factors that affect the transformation process of cities reliant on resources. Economic benefits and energy efficiency, respectively, from the economic benefit analysis framework and energy efficiency analysis framework for reconstruction, the two frameworks are combined with the use of distorted prices of resource elements to solve the problem that the synergistic effect of economic benefits and energy efficiency can not be measured. In order to quantitatively analyze the factors that affect the development efficiency of cities reliant on resources under the single or synergistic effect of the comprehensive framework, this study optimizes the directional distance function from three perspectives: exogenous weight, direction vector endogeneity, and absolute distance transformation relative distance, thus achieving an accurate assessment transformation efficiency of cities reliant on resources. Considering the impact of the new coronavirus epidemic, this study only selected the data of resource-based cities from 2003 to 2018, and found through model calculation that the impact on the transformation of cities reliant on resources: (1) Labor mismatch is mainly achieved by affecting the structure about the production of resource-based enterprises and industrial human resources; (2) Capital mismatch is mainly realized by affecting the production of resource-based enterprises; (3) Energy mismatch is mainly achieved by affecting high energy consumption enterprises and low production technology level enterprises. Further research shows that the main objects of these factors are the four parts of production technology level, energy consumption, total factor productivity and industrial structure. Through these contents, they affect environmental efficiency and deeply affect the transformation process of resource-based cities.

Towards sustainable development goals: An analysis of environmental efficiency and the impacts of self-purification capacity across diverse income levels.

The increasing urgency of global environmental degradation, particularly across diverse economic development stages, underscores a critical need for nuanced understanding and targeted strategies to achieve Sustainable Development Goals. Our study examines environmental efficiency trends over 27 years in 163 countries, utilizing greenhouse gases and particulate matter 2.5 as indicators. We address the challenge by developing and applying a two-stage method that combines a hyperbolic distance function with a stochastic meta frontier approach to assess environmental meta-efficiency. The average meta efficiency of these countries is 0.464, which remains at a relatively low level. Our model indicates that the high-income country group needs to reduce greenhouse gas and pollutant emissions by 25% and increase non-fossil energy usage by 33% to improve environmental efficiency. This suggests these countries must transition towards more sustainable energy sources and practices. Moreover, recognizing that existing income grouping inadequately characterizes each country, we use k-means cluster analysis for regrouping, more accurately reflecting individual differences. The regrouping results show that some high-income countries are classified into inactive groups, implying serious environmental problems. Our findings advocate for collaborative and tailored strategies to address these disparities. We conclude that income levels cannot solely drive environmental efficiency but must also consider geographical and climatic factors, which are pivotal in shaping a country's environmental policies and efforts. This approach offers a clearer understanding of current inefficiencies and sets the stage for more informed policy-making that can better address the specific needs and capabilities of different countries.

Shadow prices of agrochemicals in the Chinese farming sector: A convex expectile regression approach.

The use of agrochemical inputs has significantly enhanced agricultural yields in China; however, their excessive utilization has also caused a range of environmental issues. This paper examines the costs associated with reducing agrochemicals by employing shadow prices, which represent the value of the marginal product of agrochemicals, to further develop cost-effective environmental policy measures for reducing their usage. To this end, the shadow prices of agrochemicals have been assessed by adopting a newly developed convex expectile regression approach and using statistical data from 31 provinces in China spanning from 2005 to 2020. Furthermore, the present study investigates the disparities between shadow prices and market prices for different agrochemicals across various regions in China. The findings suggest that the costs of reducing chemical fertilizers are higher than those of reducing pesticides and plastic films. Moreover, the results indicate that central China exhibits relatively high potential for decreasing agrochemical usage. Finally, these findings can inform the Chinese government's restructuring of producer support and environmental policy in a cost-effective way to mitigate agrochemicals use in the future. Additionally, the research method employed in this study holds potential for extension to other agrochemicals-dependent countries.

Evaluation of healthcare efficiency in China: a three-stage data envelopment analysis of directional slacks-based measure.

Background: A consensus on the changing pattern of healthcare efficiency in China is current absent. This study tried to identify temporal fluctuations in healthcare efficiency from 2012 to 2021, and conducted a comparative analysis on the performance of 31 regions in China using region-level balanced panel data. Methods: Employing three-stage data envelopment analysis (DEA) as the analytical framework, we measured healthcare efficiency and its changes using the directional slacks-based measure and global Malmquist-luenberger (GML) indexes. We also decomposed the sources of healthcare inefficiency and extended our analysis to changes in healthcare efficiency across different primary medical service levels and regional economic development tiers. Results: The average efficiency score of medical institutions (0.956) was slightly higher than that of hospitals (0.930). We found that the average GML indexes of medical institutions in China stood at 0.990, while the average technical change (TC) index was 0.995 and the average efficiency change (EC) index was 0.998 from 2012 to 2021. The GML indexes, TC indexes, and EC indexes of hospitals were 1.002, 1.009, and 0.994, respectively. The healthcare inefficiency for both inputs and desirable outputs in medical institutions was primarily attributed to the redundant numbers of institutions, outpatient visits slacks and inpatient surgery volume slacks, accounting for 50.040, 49.644, and 28.877%, respectively. The undesirable output inefficiency values of medical institutions concerning in-hospital mortality stood at 0.012, while the figure for hospital regarding the average length of stay (LOS) was 0.002. Additionally, healthcare efficiency in both medical institutions and hospitals exhibited an upward trend from 2012 to 2021, corresponding to an increase in the volume of primary medical services, primary medical staff, and the total gross domestic product (GDP). Conclusion: Total factor productivity (TFP) of medical services declined in China from 2012 to 2021. The excessive number of medical institutions and the slack of medical service volumes were the main sources of healthcare inefficiency. Regions prioritizing primary medical services and boasting higher GDP levels exhibited superior healthcare efficiency. These findings are expected to inform policymakers' efforts in building a value-based and efficient health service system in China.

An endoscopic chisel: intraoperative imaging carves 3D anatomical models.

PURPOSE: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging. However, surgery elicits anatomical changes not represented in the preoperative model, generating an inaccurate basis for navigation during surgery progression. METHODS: We propose a first vision-based approach to update the preoperative 3D anatomical model leveraging intraoperative endoscopic video for navigated sinus surgery where relative camera poses are known. We rely on comparisons of intraoperative monocular depth estimates and preoperative depth renders to identify modified regions. The new depths are integrated in these regions through volumetric fusion in a truncated signed distance function representation to generate an intraoperative 3D model that reflects tissue manipulation RESULTS: We quantitatively evaluate our approach by sequentially updating models for a five-step surgical progression in an ex vivo specimen. We compute the error between correspondences from the updated model and ground-truth intraoperative CT in the region of anatomical modification. The resulting models show a decrease in error during surgical progression as opposed to increasing when no update is employed. CONCLUSION: Our findings suggest that preoperative 3D anatomical models can be updated using intraoperative endoscopy video in navigated sinus surgery. Future work will investigate improvements to monocular depth estimation as well as removing the need for external navigation systems. The resulting ability to continuously update the patient model may provide surgeons with a more precise understanding of the current anatomical state and paves the way toward a digital twin paradigm for sinus surgery.

Measuring urban environmental performance in China: A Euclidean distance function approach.

Air pollution has been one of the biggest environmental challenges for cities in their pursuit of sustainability. The removal of air pollutants usually entails a cost that can negatively impact economic activities. Assessing urban environmental performance can offer valuable insights for cities to balance their economic production and environmental protection. This paper develops a novel approach to measuring environmental performance by using the Euclidean distance function. An appealing feature of this approach is its ability in endogenously allocating the optimization pathway to each emitter, thereby avoiding arbitrary estimation results and distorted managerial implications. We apply this approach to study the environmental performance of Chinese key environmental protection cities. We find the heterogeneity in performance estimates and endogenous optimization pathways. Prioritizing the reduction of a specific type of emissions while simultaneously increasing industrial output value seems to be the most appropriate objective for the majority of cities. Our study can serve as a basis for urban governments to pinpoint the underlying factors contributing to low urban environmental performance and establish diverse objectives for enhancing environmental performance.

Policy choices for China to reduce carbon emissions in coping with extreme weather: Applying a dynamic two-stage undesirable non-radial directional distance function.

China is the world's largest carbon emitter and also one of many countries most affected by extreme weather. Although its government has set carbon reduction targets, the public has not established a connection between carbon reduction and coping with extreme weather. This study aims to help establish the above connection and applies a dynamic two-stage undesirable non-radial directional distance function to evaluate energy performance in the first stage while establishing CO2 emissions as a link to evaluate coping with extreme weather performance in the second stage. From empirical results, the average efficiency of 30 provinces in China in coping with extreme weather from 2011 to 2020 is only 0.484, or far lower than the energy efficiency value of 0.709. Based on the differences in performance between the two stages and the changing trends in the room for improvement of CO2 emissions, this study proposes policy options to promote the participation of the entire society in the emission reduction process.

Extracellular Vesicle Protein Expression in Doped Bioactive Glasses: Further Insights Applying Anomaly Detection.

Proteomic analysis of extracellular vesicles presents several challenges due to the unique nature of these small membrane-bound structures. Alternative analyses could reveal outcomes hidden from standard statistics to explore and develop potential new biological hypotheses that may have been overlooked during the initial evaluation of the data. An analysis sequence focusing on deviating protein expressions from donors' primary cells was performed, leveraging machine-learning techniques to analyze small datasets, and it has been applied to evaluate extracellular vesicles' protein content gathered from mesenchymal stem cells cultured on bioactive glass discs doped or not with metal ions. The goal was to provide additional opportunities for detecting details between experimental conditions that are not entirely revealed with classic statistical inference, offering further insights regarding the experimental design and assisting the researchers in interpreting the outcomes. The methodology extracted a set of EV-related proteins whose differences between conditions could be partially explainable with statistics, suggesting the presence of other factors involved in the bioactive glasses' interactions with tissues. Outlier identification of extracellular vesicles' protein expression levels related to biomaterial preparation was instrumental in improving the interpretation of the experimental outcomes.

The role of climate-adaptive technological innovation in promoting agriculture carbon efficiency: impact and heterogeneity in economic development.

Achieving global climate change mitigation targets requires low-carbon production in agriculture. In such an endeavor, a new classification of climate-adaptive technology is defined to affect agriculture towards the low-carbon direction, but such an impact has seldom been empirically tested in the literature. In this paper, we investigate the impact of climate-adaptive technological innovation on agricultural carbon efficiency, a proxy for low-carbon agriculture. We use a stochastic directional distance function framework and a cross-country dataset covering 38 OECD countries. Additionally, we test the heterogeneous impact, considering that regional economic development is a crucial condition for deploying advanced technologies. The findings show that climate-adaptive technological innovation can promote carbon efficiency in agriculture, and this aggregate effect hides significant heterogeneity at different levels of economic development. The higher the economic development level is, the better climate-adaptive technological innovation contributes to improving agricultural carbon efficiency. Then, related policy implications are set forth.

Research on carbon emission quota allocation scheme under "Double Carbon" target: a case study of industrial sector in Henan Province.

To achieve China's "Double Carbon" target, overall carbon emissions should be effectively controlled, and carbon emission quota (CEQ) allocation is an important tool. This study develops carbon emission prediction, CEQ allocation, and scheme feasibility evaluation models based on the principles of fairness, efficiency, and economy. The purpose is to propose a suitable CEQ allocation scheme for the Industrial Sector in Henan Province (ISHP). The results show that (1) the allocation model combining the technique for order preference by similarity to ideal solution (TOPSIS) and the zero-sum gains DEA (ZSG-DEA) can trade off the fairness and efficiency principles. (2) The reallocation scheme has an environmental Gini coefficient of 0.393 (< 0.4), which maximizes efficiency while lowering the abatement costs by 126.268 billion yuan, making it an ideal scheme that considers multiple principles. (3) CEQ should be reduced in 7 subsectors of ISHP while increasing in 33 others. Carbon emissions from these 7 subsectors are high, and CEQ should be reduced in accordance with the fairness principle. Even if their abatement costs are high and CEQ rises according to the efficiency principle, the increase is much smaller than the decrease. The findings are useful for optimizing the CEQ allocation under the "Double Carbon" target.

A step toward inclusive green growth: can digital finance be the main engine?

Inclusive green growth (IGG) has become a worldwide consensus to achieve the target of sustainable development goals. Although the prominent role of digital finance (DF) against the pandemic has drawn considerable attention from policymakers, its plausible effect on IGG and underlying mechanisms have not been distinctly explored in academia. The aim of the study is to explore the causal effect of DF on IGG based on prefecture city-level data from 2011 to 2019 in China. To this end, we employed the non-radial direction distance function approach within the global production technology to evaluate the aggregate IGG performance and its three sub-dimensions. The empirical results demonstrate that DF exerts a significant promotional effect on urban IGG. This finding continues to survive in an extensive set of robustness checks using an alternative dependent variable, model specifications, instrumental variable, and difference-in-difference approaches to address the endogeneity concerns. Meanwhile, sub-dimensional regressions show that this positive effect is driven predominantly by the scale economy of DF, while the depth of usage and digitalization playing a minor role. Moreover, we uncover that DF enhances IGG by leveraging greater marginal product of labor rather than capital, improving environmental externalities, increasing fuller employment, and reducing rural-urban income inequality. However, we also reveal the dark side of DF on imbalanced regional development. The promotional effect of DF on IGG is only prominent for cities with better inherent comparative advantages, and we are thus likely to see a widening digital divide resulting from the "Matthew effect" on regional disparity without timely policy interventions.

Efficacy of Fiberoptic Laryngeal Surgery in Benign VocalCord Lesions in Comparision with Microlaryngeal Surgery.

The use of operating microscope and rigid telescope in laryngeal surgery represents one of the most exciting advances. Both systems provide good illumination, magnification and relative ease of operation. Microlaryngeal surgery (MLS) with suspension laryngoscope is currently considered the gold standard for surgical approach to the larynx for excision of benign lesions of vocal folds. The limitation of this technique is intubation, general anesthesia (GA) related and in difficult exposure of larynx. The development of ideal anesthetic system has not been successful in satisfying both the surgeon and the anesthesiologist. Fiberoptic Laryngeal Surgery (FLS) is convenient as it avoids GA, is an outpatient procedure with less morbidity and reduced costs. FLS is preferable for the patients who have medical contraindications for general GA. To compare the efficacy of MLS and FLS surgical procedures in terms of functional outcome. This is a comparative clinical study done on 42 patients who were diagnosed to have benign vocal fold pathology. Pre and post-operative speech evaluation was done for all the patients by the same examiner, which included GRABS scale, Maximum Phonation Time, voiced consonants, voiceless consonants, s/z ratio along with stroboscopy. In Group A, MLS was the treatment modality, done under GA, while in Group B, FLS was the treatment modality done under Local Anesthesia (LA). After one-month, post-operative evaluation of voice was done. When the Maximum Phonation Time, S/Z ratio and stroboscopic parameters (mucosal wave) were compared between the two study groups there appeared no statistically significant difference between the two groups. Our study shows that when benign vocal cord pathologies were treated by MLS or FLS the voice Quality improvement remains the same. FLS is the procedure of choice when MLS under general anesthesia is contraindicated.

Evaluating the resource recovery efficiency of garbage removal and disposal in Taiwan.

All countries recognize the importance of enhancing circular resource utilization and promoting zero waste in order to achieve a circular economy. This study focuses on 22 counties (cities) in Taiwan and analyses the relationship between changes in resource recovery efficiency and productivity over an 8-year period (2011-2018) using the super efficiency directional distance function and total factor productivity. The results indicate that the average total efficiency of resource recovery in the 22 counties (cities) is 1.3951, with 17 counties (cities) exceeding 1. The average gross productivity of resource recovery is 1.0417, and only four counties have a gross productivity of less than 1. Despite having more administrative resources, municipalities directly under the central government have a slightly lower average total efficiency of resource recovery (1.1464) than non-municipalities (1.4885). To improve efficiency, the government should allocate resources rationally and upgrade technology. The study's findings can serve as a reference for the 22 counties (cities) in Taiwan and provide guidance for municipal governments worldwide in developing policies related to resource recovery.

Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks.

In this paper, we present and compare four methods to enforce Dirichlet boundary conditions in Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (VPINNs). Such conditions are usually imposed by adding penalization terms in the loss function and properly choosing the corresponding scaling coefficients; however, in practice, this requires an expensive tuning phase. We show through several numerical tests that modifying the output of the neural network to exactly match the prescribed values leads to more efficient and accurate solvers. The best results are achieved by exactly enforcing the Dirichlet boundary conditions by means of an approximate distance function. We also show that variationally imposing the Dirichlet boundary conditions via Nitsche's method leads to suboptimal solvers.

Energy structure upgrade and carbon emission reduction: evidence from Asian economies.

The reduction of carbon emissions has become an important climate issue worldwide. However, the diversity of carbon trading systems and the differentiation policy may generate incomparable carbon abatement costs across regions and countries. Based on the nonparametric model, this paper investigates the shadow price of carbon emissions and energy structure in 38 Asian countries from 1991 to 2019. The main findings of this paper are as follows: (1) The annual average shadow price of carbon emissions experienced a fluctuating decline for Asian countries during the period 1991-2000, followed by a continuous rise and then a fluctuating decline. (2) Industrialization may lead to a decline in carbon shadow price, while urbanization may lead to a rise in the opportunity cost of carbon reduction. (3) The carbon shadow price in countries of Asia-Pacific Economic Cooperation (APEC) is lower than that in non-APEC countries. (4) The structure of energy consumption is negatively related to marginal abatement costs, while on the contrary, the coefficients of the level of human resources are significantly positive. We also derive corresponding policy measures to promote intra-regional emission reduction.