Porter effect vs cost effect: The impact of China's low carbon city pilot on carbon emissions and economic performance.

Building a low-carbon economy can help cities effectively mitigate climate risks, but it is challenging for developing countries. Using a difference-in-difference and event study model, we investigate the joint impact of China's Low-Carbon City Pilot (LCCP) on carbon emissions and economic performance. Our findings show that the LCCP significantly reduces carbon emissions and increases gross revenues, employee count, and fixed assets without compromising the net profit of manufacturing firms. The LCCP has a cumulative effect, with the positive joint impact increasing gradually over time. A heterogeneity analysis shows that the later pilot cities have not achieved better carbon emissions and economic performance than the early pilot cities. The reason for the positive joint effect of LCCP is that the Porter effect outweighs the cost effect. These findings contribute to knowledge about how developing countries can develop a low-carbon economy.

Predicting the loss to follow-up (LTFU) of HIV/AIDS patients in China using a recency-frequency (RF) model.

BACKGROUND: We constructed a recency-frequency (RF) model for predicting the loss to follow-up (LTFU) in HIV/AIDS patients in China. METHODS: Data on HIV/AIDS outpatients in the research unit from 1 August 2009 to 30 September 2020 and from 1 October to 31 December 2020 were exported as the observation and prediction datasets, respectively. The classic recency-frequency-monetary (RFM) model was expanded into RFm, RF, RFL and RFmL models. In the observation dataset, the best predictive model was obtained using k-means clustering and C5.0 verification. Then, two rounds of k-means modelling were performed on the best model: data with R ≤ 6 months were retained, randomly divided into a training set (70%) and a testing set (30%) and used to perform the second round of modelling to subdivide the churn and non-churn patients. Next, an ANN algorithm was used to predict LTFU, and the confusion matrix with prediction datasets was constructed. RESULTS: The observation and prediction datasets included 16 949 and 10 748 samples, respectively. The RF model with three clusters and a quality of 0.82 was the best predictive model. From the observation set, 13 799 samples were retained, and the model accuracy was 100% on the training and testing sets. These 13 799 samples were subdivided into 1563 samples of churn patients and 12 216 samples of non-churn patients. The accuracy of ANN prediction was 99.89%. The accuracy and precision of the confusion matrix were 85.41% and 99.76%, respectively. CONCLUSION: The RF model is effective in predicting the LTFU of HIV/AIDS patients in China and preventing its occurrence.

Probabilistic carbon price prediction with quantile temporal convolutional network considering uncertain factors.

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict.

Does air pollution inhibit manufacturing productivity in Yangtze River Delta, China? Moderating effects of temperature.

China has been experiencing serious and recurrent incidences of air pollution in recent years. The frequency and timespans of such incidences are uncertain because of variable urban weather conditions, especially temperature, that inhibit the productivity of manufacturing companies. Matching data about listed manufacturing companies in China's Yangtze River Delta urban cluster from 2003 to 2018 with data on urban air pollution and weather, we explored the effects of air pollution on corporate productivity and the moderating role of temperature. We found that air pollution significantly inhibited the productivity of these companies, which decreased by about 0.1% for 1% increase in the concentration of PM2.5. Regarding industry heterogeneity, high-energy-consumption and low-technology manufacturing industries were more sensitive to the negative effects of air pollution. Furthermore, we concluded that low temperatures played an important role in causing significant increases in the negative effects of air pollution. In the fall and winter (October to January), the lower the temperatures resulted in stronger inhibitory effects of air pollution on corporate productivity. When the average daily temperature is 0°C-3°C, the moderating effects of temperature are even more obvious. To minimize the inhibitory effects of air pollution on productivity, governments and companies should implement positive adaptions to simultaneously confront air pollution and temperature change.

China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?

The price jump behavior may bring tremendous challenges on risk management and asset pricing. This paper uses the BN-S test, the wavelet coherence method, and applies high-frequency data to explore whether and to what extent the COVID-19 pandemic impacts China's energy stock market jumps and its characteristics. The empirical results uncover the significant and heterogeneous interactions between the COVID-19 pandemic and China's energy stock market jumps across market specifications, investment horizons, and China/global pandemic tolls at different time scales. First, the oil stock market jumps were the most correlated with the pandemic, especially during the peak and re-deterioration phases. The pandemic played a positive and leading role in the short term (1-4 days length period) and long term (over 32 days length period). Second, the coal stock market jumps have similar characteristics to those of oil, but mainly show a negative correlation with the pandemic. Third, renewable energy stock market jumps were the least correlated, mainly showing a positive correlation in the short term and a negative correlation in the long term. In addition, the interaction characteristics of systemic co-jumps in different China's energy stock markets are also significant.

Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model.

BACKGROUND: Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables. METHODS: We cleaned 257,305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16,440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables. RESULTS: The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10,614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable. CONCLUSIONS: This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.

Carbon emission reduction characteristics for China's manufacturing firms: Implications for formulating carbon policies.

The rapid development of China's manufacturing industry since China's accession to WTO in 2001 has dramatically increased China's carbon emissions. To inform the carbon policy development of China's manufacturing industry, this study constructed a DEA-GS (data envelopment analysis and grid search) model from a cost perspective to understand the their emission reduction characteristics. Using a large sample of manufacturing firms from 2008 to 2011, the carbon pricing and reduction potential of China's manufacturing firms was explored by analyzing the firms' marginal abatement costs. The results showed that: (a) with increasing marginal abatement costs, the growth rates of both cumulative emission reduction activities and emission reduction of these firms gradually slowed down. When the marginal abatement cost exceeds 200 Yuan/ton, neither the number of reduction activities nor the amount of reduced emissions increase. (b) The impact of marginal abatement costs on the numbers of reduction activities and firms in each sub-sector is heterogeneous. (c) The emission reduction behaviors of manufacturting firms, determined by carbon pricing, are mostly concentrated in developed areas or around large cities. In contrast, areas with substantial emission reductions are more scattered. The results suggest that The emission reduction characteristics of sub-sectors should be fully considered when formulating carbon policies for China's manufacturing industry. The carbon price for the China's manufacturing industry should not exceed 200 Yuan/ton. Furthermore, the carbon policy of China's manufacturing industry should have broader coverage, rather than merely covering developed areas.

An improved production-theoretical approach to decomposing carbon dioxide emissions.

Production-theoretical decomposition analysis (PDA), built on production theory and data envelopment analysis, has been widely used to quantify the factors that drive CO2 emission changes to support policy analysis and making. Existing PDA methods are usually linked to Shephard distance function and Malmquist productivity index. However, decomposition results associated with these methods may be biased and incomplete. The challenges with these methods mainly stem from the problems associated with underestimating disaggregated efficiencies and the infeasibility of linear programming. This paper proposes a modified PDA approach based on a non-radial directional distance function and global Malmquist-Luenberger productivity index. This new approach addresses the problems associated with conventional PDA methods. To show the usefulness of the proposed approach, we apply it to study CO2 emissions in China and use the bootstrap method to test the statistical significance of the estimated results.

Industrial SO2 emissions treatment in China: A temporal-spatial whole process decomposition analysis.

Effectively treating industrial SO2 emissions depends on the synergy of different factors from the industrial SO2 generation source to the end of treatment. Applying a whole process treatment perspective, this paper decomposes industrial SO2 emissions into six specific driving factors in three whole process treatment dimensions (i.e. source prevention, process control, and end-of-pipe treatment), and economic scale. A temporal index decomposition analysis (Temporal-IDA), attribution analysis (AA), and spatial index decomposition analysis (Spatial-IDA) methods are then applied to quantify each dimension's treatment effect and its spatial differences. The empirical study across 30 regions in China using data from 2005 to 2015 shows that: (1) The end-of-pipe treatment is the dominant dimension for decreasing industrial SO2 emissions, followed by process control. The contribution of source prevention to reduce industrial SO2 emissions has begun to appear, however, there remains room for further improvement; (2) End-of-pipe treatment strength and energy intensity are key factors in reducing industrial SO2 emissions; Inner Mongolia, Henan, and Shandong are the main contributors; (3) The treatment emphasis are different among regions; as such, there are different treatment effects across the three dimensions of the whole process treatment. Regions can be classified into four categories: the Leading type, Process-dependent type, End-dependent type, and Lagging type. Based on the empirical results, this paper identifies the policy implications of promoting whole process treatment on China's industrial SO2 emissions.

Would an increasing block carbon tax be better? A comparative study within the Stackelberg Game framework.

Carbon tax is a policy for controlling carbon emissions, and is widely used worldwide. However, a non-differentiated carbon tax increases financial burden on manufacturers and inhibits their willingness to produce. Herein, a novel carbon tax policy involving an increasing block carbon tax is proposed, and the policy's possible implementation effects are analyzed. First, based on the Stackelberg game, the study constructs a social welfare model considering carbon emissions. Then, the study discusses the theoretical characteristics of the proposed carbon tax policy. After that, the differences and similarities between a flat carbon tax and an increasing block carbon tax are analyzed using a numerical simulation. The results indicate that: (1) compared to flat carbon tax, an increasing block carbon tax has the same controlling effect on carbon emissions. Both forms of taxes can restrict total carbon emissions within the desired range. (2) An increasing block carbon tax policy can significantly reduce tax burdens for manufacturers, and encourages low-carbon production. (3) An increasing block carbon tax can flexibly adjust the relationship between government's carbon tax revenue and manufacturer's tax burden. Finally, some policy implications for the proposed strategy are revealed.

Effects of a generalized dual-credit system on green technology investments and pricing decisions in a supply chain.

China recently implemented a corporate average fuel consumption regulation and new-energy vehicle credit program (dual-credit system) to prompt the transition to new-energy vehicles. This study generalizes the dual-credit system (energy credit and green credit) and investigates its effects on the green technology investments (GTI) and pricing decisions in a two-echelon supply chain consisting of three possible scenarios, Case O (conventional product only), Case B (both conventional and green products), and Case G (green product only). The obtained results show that the GTI made by manufacturers follow high threshold and low threshold. The generalized dual-credit system increases both thresholds and promotes the transition from Case O to Case B and Case B to Case G. The transition is sensitive to standard energy consumption per-unit (SECP), green credit quota (GCQ), and price of green credit (PGC). The generalized dual-credit system benefits the manufacturers who exceed the low threshold, vice versa, especially for whose conventional product with lower initial energy consumption per unit. The generalized dual-credit system contributes to GTI and environment effects in all cases. But, the impacts on GTI, environment effects, and profit differ in sensitivity to SECP, GCQ, and PGC in different cases. Numerical simulation is given and all the proofs are shown in appendix.

SHP2 and UGP2 are Biomarkers for Progression and Poor Prognosis of Gallbladder Cancer.

Biomarkers for the diagnosis, prognosis, and targeting therapy of gallbladder cancers are not clinically available. This study demonstrated that the percentage of cases with positive SHP2 and UGP2 expression significantly correlated with the percentage of cases with positive vimentin, ß-catenin, MMP2, MMP9, and Ki-67 expression, large tumor size, high TNM stage, lymph node metastasis, and survival in patients with adenocarcinomas and squamous cell/adenosquamous carcinomas. Positive SHP2 and UGP2 expression are independent poor-prognostic factors in both types of tumors. Our study suggested that positive SHP2 and UGP2 expression correlated with clinicopathological and biological behaviors, and poor-prognosis of gallbladder cancer.

The ataxia telangiectasia mutated kinase pathway regulates IL-23 expression by human dendritic cells.

Little is known of the regulation of IL-23 secretion in dendritic cells (DC) despite its importance for human Th17 responses. In this study, we show for first time, to our knowledge, that the ataxia telangiectasia mutated (ATM) pathway, involved in DNA damage sensing, acts as an IL-23 repressor. Inhibition of ATM with the highly selective antagonist KU55933 markedly increased IL-23 secretion in human monocyte-derived DC and freshly isolated myeloid DC. In contrast, inhibiting the closely related mammalian target of rapamycin had no effect on IL-23. Priming naive CD4(+) T cells with ATM-inhibited DC increased Th17 responses over and above those obtained with mature DC. Although ATM blockade increased the abundance of p19, p35, and p40 mRNA, IL-12p70 secretion was unaffected. To further examine a role for ATM in IL-23 regulation, we exposed DC to low doses of ionizing radiation. Exposure of DC to x-rays resulted in ATM phosphorylation and a corresponding depression of IL-23. Importantly, ATM inhibition with KU55933 prevented radiation-induced ATM phosphorylation and abrogated the capacity of x-rays to suppress IL-23. To explore how ATM repressed IL-23, we examined a role for endoplasmic reticulum stress responses by measuring generation of the spliced form of X-box protein-1, a key endoplasmic reticulum stress transcription factor. Inhibition of ATM increased the abundance of X-box protein-1 mRNA, and this was followed 3 h later by increased peak p19 transcription and IL-23 release. In summary, ATM activation or inhibition, respectively, inhibited or augmented IL-23 release. This novel role of the ATM pathway represents a new therapeutic target in autoimmunity and vaccine development.

Expression and Clinical Pathological Significance of miR-200a in Concurrent Cholangiocarcinoma Associated with Hepatolithiasis.

BACKGROUND Approximately 2-10% of the patients with hepatolithiasis may develop cholangiocarcinoma (CCA). Despite recent advances in the treatment of cancers, the 5-year survival rate for CCA patients currently remains poor, primarily due to early local invasion and distant metastasis of the cancer. This study aimed to investigate miR-200a expression in combined hepatolithiasis and CCA as well as its correlation with the clinical features of CCA. MATERIAL AND METHODS miR-200a expression in combined hepatolithiasis and CCA was detected by real-time reverse transcription PCR (qRT-PCR). Its correlation with the clinicopathology of CCA was analyzed by t-tests. The effect of miR-200a on the proliferation CCA cells was determined by MTT assay. The effect of miR-200a on the invasive ability of CCA cells was assessed by Boyden chamber test. RESULTS The expression level of MiR-200a in patients with combined hepatolithiasis and CCA was significantly decreased compared with patients with only hepatolithiasis (P<0.01). Furthermore, miR-200a expression in hepatic duct cancer RBE cells was substantially reduced compared with hepatolithiasis group (P<0.01). Correlation analysis showed that abnormal expression of miR-200a was only associated with the differentiation degree and metastasis of CCA. MiR-200a transfection significantly inhibited the proliferation and invasion of REB cells (P<0.01). CONCLUSIONS MiR-200a may suppress the proliferative and invasive ability of REB cells. The reduced miR-200a expression might be correlated with the development and progression of CCA.

Novel function for the p38-MK2 signaling pathway in circulating CD1c+ (BDCA-1+) myeloid dendritic cells from healthy donors and advanced cancer patients; inhibition of p38 enhances IL-12 whilst suppressing IL-10.

There is growing interest in myeloid (my) dendritic cells (DC) as an alternative to monocyte-derived DC (moDC) for immunotherapy. However, in contrast to moDC, little is known regarding the effect of malignancy on the function, abundance or use of intracellular signaling pathways in myDC. Understanding the molecular detail of circulating myDC is therefore important for future use in advanced cancer. Advanced cancer patients had similar numbers of circulating myDC to cancer-free patients and healthy individuals, and secreted similar levels of IL-1ß, IL-6, IL-10, IL-12 and IL-23. However, myDC from some patients failed to secrete the Th1-cytokine IL-12. Surprisingly, inhibiting p38 (p38i) signaling (using BIRB0796 or SB203580) markedly increased IL-12 secretion by myDC. This is in complete contrast to what is established for moDC where inhibiting p38 ablates IL-12. Interestingly, this was specific to IL-12, since IL-10 was suppressed by p38i in both DC types. The opposing effect of p38i on IL-12 was evident at the transcriptional level and in both DC types was mediated through the p38-MK2 pathway but did not involve differential phosphorylation of the distal Rsk kinase. Importantly, where patient myDC did not secrete IL-12 (or after treatment with suppressive melanoma lysate), p38i restored IL-12 to normal levels. In contrast to p38, inhibiting the other MAPK pathways had similar consequences in both DC types. We show for the first time the differential use of a major intracellular signaling pathway by myDC. Importantly, there are sufficient circulating myDC in advanced cancer patients to consider development of adoptive immunotherapy.

Laparoscopic left lateral sectionectomy--the learning curve and the role of left hepatic inflow occlusion.

BACKGROUND/AIMS: To investigate the learning curve of laparoscopic left lateral sectionectomy (LLLS), and the role of left hepatic inflow occlusion (LHIO) in LLLS. METHODOLOGY: 41 consecutive cases of solid lesion in left lateral section underwent LLLS between August 2011 and August 2012. LHIO was conducted in 21 cases. RESULTS: The overall median operative time and blood loss were 160 min (range, 80-290 min) and 80 mL (range, 20-00 mL), respectively. The median operative time and blood loss were significantly reduced in later 21 cases compared with early 20 cases (110 min vs. 195 min, 40 mL vs. 265 mL, respectively). Compared with the group of cases that did not undergo LHIO, the median operative time and blood loss were significantly reduced (130 min vs. 185 min, 50 mL vs. 290 mL, respectively) in the group of cases that underwent LHIO, while the elevation of transaminase at postoperative day 2 and day 5 were of no significant difference between these two groups. CONCLUSIONS: A smooth learning curve can be achieved for LLLS, LHIO was associated with reduced operative time and blood loss without increasing the risk of postoperative liver function impairment.

Modified technique of laparoendoscopic rendezvous ERCP during laparoscopic cholecystectomy for concomitant gallstone and common bile ductal stone.

Laparoendoscopic rendezvous ERCP and laparoscopic cholecystectomy (LC+ERCP/LERV) is considered an optimal approach for concomitant gallstone and common bile duct stone, but this procedure could be failed due to some technical challenges. We describe a modified technique which was adopted in 32 consecutive cases and yielded good results. A Dormia basket is inserted through cystic duct to enter duodenum and grasp the guide wire which is introduced with sphincterotome through endoscope. After pulling the basket catheter and guide wire into bile duct, the selective bile duct cannulation could be achieved by advancing sphinterotome over guide wire. An atraumatic clamp is also used to temporarily occlude proximal jejunum preventing diffuse bowel distention by air insufflation. The procedure was successfully performed in 31 cases(96.8%) , the mean operative time and endoscopic time were 82.6±19.6 min and 26.5±5.99min, respectively. This modified technique is safe, feasible and associated with short operative time.

Integrated machine learning methods with oversampling technique for regional suitability prediction of waste-to-energy incineration projects.

China's tiered strategy to enhance county-level waste incineration for energy aligns with the sustainable development goals (SDGs), emphasizing the need for comprehensive assessments of waste-to-energy (WtE) plant suitability. Traditional assessment methodologies face challenges, particularly in suggesting innovative site alternatives, adapting to new data sets, and their dependence on strict assumptions. This study introduced enhancements in three pivotal dimensions. Methodologically, it leverages data-driven machine learning (ML) approaches to capture the complex relationships essential for site selection, reducing dependency on strict assumptions. In terms of predictive performance, the integration of oversampling with stacked ensemble models enhances the diversity and generalizability of ML models. The area under curve (AUC) scores from four ML models, enhanced by the oversampled dataset, demonstrated significant improvements compared to the original dataset. The stacking model excelled, achieving a score of 92%. It also led in overall Precision and Recall, reaching 85.2% and 85.08% respectively. Nevertheless, a noticeable discrepancy existed in Precision and Recall for positive classes. The stacking model topped Precision scores at 83.1%, followed by eXtreme Gradient Boosting (XGBoost) (82.61%). In terms of Recall, XGBoost recorded the lowest at 85.07%, while the other three classifiers all marked 88.06%. From an industry applicability standpoint, the stacking model provides innovative location alternatives and demonstrates adaptability in Hunan province, offering a reusable tool for WtE location. In conclusion, this study not only enhances the methodological aspects of WtE site selection but also provides practical and adaptable solutions, contributing positively to sustainable waste management practices.

Big data and artificial intelligence application in energy field: a bibliometric analysis.

This paper uses bibliometrics to characterize the knowledge systems of big data, artificial intelligence (AI), and energy based on the Science Citation Index Extension (SCI-E) and Social Science Citation Index (SSCI) of the Web of Science from 2001 to 2020. Results show that China is the country with the highest number of publications (1115), accounting for 29% of the total; however, the most influential country in the field is the USA, with an h-index of 75. The Chinese Academy of Sciences publishes the largest number of papers (104) and plays a vital role in the collaboration network. The study also reveals that the IEEE Access is the most productive journal (195) in terms of the number of publications, and engineering is the most popular discipline (1526). The key theoretical foundation includes deep learning (293), big data (105), energy consumption (79), and reinforcement learning (40). The application of big data and AI in the field of energy focuses on smart grid, energy consumption, and renewable energy. Early research frontiers involve optimization and prediction of energy-related problems using the genetic algorithm and neural networks. Since 2013, energy big data have gained prominence. At present, machine learning, deep learning, and fog computing are frequently combined with energy saving. In the future, big data and AI will be utilized to promote the application of renewable energy and energy-saving renovation of buildings. These findings can help researchers understand the developmental trends and correctly grasp the research direction and method of the emerging interdisciplinary field.

Review of calibration strategies for discrete element model in quasi-static elastic deformation.

This study first reviewed theories of the mechanical response of structures under loading, and the discrete element method provides a route for studying mechanical response including elastic deformation and structure failure. However, the direct acquisition of the microscopic parameters from the governing equations of the discrete element method via experiments encounters challenges. One possible strategy to obtain these microscopic parameters is parameter calibration that are widely used by researchers. Secondly, the governing equations and failure criterion of the discrete element method are summarized, and the microscopic parameters that would be calibrated are pinpointed. Next, the principles of classical calibration methods of discrete element method are explicated in detail, alongside the validation and discussion of their properties. Lastly, this study examined the applicability of calibrated parameters and points out that the size ratio, porosity, maximum radius, and minimum radius of particles should be identical in both the geometric calibration model and that for applications.