作者机构：
[Zhou, Yuanyuan; Zheng, Chengli] Cent China Normal Univ, Financial Engn Res Ctr, Sch Econ & Business Adm, Wuhan 430079, Peoples R China.;[Goh, Mark] Natl Univ Singapore, NUS Business Sch, Singapore, Singapore.;[Goh, Mark] Natl Univ Singapore, Logist Inst Asia Pacific, Singapore, Singapore.
通讯机构：
[Zheng, Chengli] C;Cent China Normal Univ, Financial Engn Res Ctr, Sch Econ & Business Adm, Wuhan 430079, Peoples R China.
关键词：
Large-scale group decision-making;Statistics;Five-number summary;Risk attitude;Interval-valued Pythagorean fuzzy number
摘要：
As loss in decision sample information occurs during large-scale group decision-making (LSGDM), this paper proposes a statistical estimation approach for handling Pythagorean fuzzy information under the risk attitude of decision-makers (DMs). The DMs are partitioned by risk attitudes (hesitancy degrees) into subgroups. A five-number summary for the subgroups from the incomplete decision information given by the DMs is obtained. The Cornish-Fisher expansion is then applied to estimate the mean, standard variance, and skewness of the decision sample information from the five-number summary. The confidence interval constructed by the skewness is used to obtain the interval-valued Pythagorean fuzzy number (IVPFN) evaluation information of the subgroups. An optimization model based on minimizing the conflicts between the subgroups and the overall group is used to derive the weights of the subgroups. A sorting function of the IVPFNs is used to rank the alternatives. A case study on green credit and a comparison analysis are applied to validate the proposed method. (C) 2021 Elsevier B.V. All rights reserved.
期刊：
Frontiers in Environmental Science,2022年10:894 ISSN：2296-665X
通讯作者：
Zhou, C.
作者机构：
[Wei, Wei; Xie, Weikun] Cent China Normal Univ, Sch Econ, Wuhan, Peoples R China.;[Zhou, Chengying] Nankai Univ, Sch Econ, Tianjin, Peoples R China.;[Wei, Wei; Xie, Weikun] Cent China Normal Univ, Business Adm, Wuhan, Peoples R China.
通讯机构：
[Zhou, C.] S;School of Economics, Nankai University, Tianjin, China
期刊：
Discrete Dynamics in Nature and Society,2022年2022 ISSN：1026-0226
通讯作者：
Kong, J.
作者机构：
[Yao, Riquan] State Grid Zhejiang Elect Power Co Ltd, Huzhou Power Supply Co, Huzhou, Peoples R China.;[Jin, Shaojun] State Grid Zhejiang Elect Power Co Ltd, Hangzhou, Peoples R China.;[Wei, Cong] Zhejiang Univ Finance & Econ, Sch Econ, Hangzhou, Peoples R China.;[Kong, Jiayang] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan, Peoples R China.;[Kong, Jiayang] Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.
通讯机构：
[Kong, J.] S;School of Economics and Business Administration, China
摘要：
The grey model, which is abbreviated as GM (1, 1), has been widely applied in the fields of decision and prediction, particularly in the prediction of time series with few observations, referred to as the poor information and small sample in the literature related to grey model. Previous studies focus on improving the accuracy of prediction but pay less attention to the robustness of the grey model to outliers, which often occur in practice due to an incorrect record by chance or an accidental failure in equipment. To fill that void, we develop a robust grey model, whose structural parameters are obtained from the least trim squares, to forecast Chinese electricity demand. Also, we use the last value in the first-order accumulative generating time series as the initial value, according to the new information priority criterion. We name the novel grey model, proposed in this paper, the novel robust grey model integrating the new information priority criterion, which could be abbreviated as NIPC-GM (1, 1). In addition, we introduce a novel approach, that is, the bootstrapping test, to investigate the robustness against outliers for the novel robust grey model and the classical grey model, respectively. Using the data on Chinese electricity demand from 2011 to 2021, we find that not only does the novel robust grey model integrating the new information priority criterion have a better robustness to outliers than the classical grey model, but it also has a higher accuracy of prediction than the classical grey model. Finally, we apply the novel robust grey model integrating the new information priority criterion to forecasting the future values in Chinese electricity demand during the period 2022 to 2025. We see that Chinese electricity demand would continue to rise in the next four years.
作者：
Wang, Cuicui;Tong, Qingmeng;Xia, Chunping;Shi, Miaomiao;Cai, Yi
期刊：
Journal of Environmental Planning and Management,2022年67(4):809-829 ISSN：0964-0568
作者机构：
[Wang, Cuicui] Yantai Univ, Sch Econ & Management, Yantai, Peoples R China.;[Tong, Qingmeng] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan, Peoples R China.;[Shi, Miaomiao; Xia, Chunping] Huazhong Agr Univ, Coll Econ & Management, Wuhan, Peoples R China.;[Cai, Yi] South China Agr Univ, Coll Econ & Management, Digital Countryside Res Inst, Guangzhou, Peoples R China.
关键词：
e-commerce;fruit farmers;China;green production;conditional mixed process
摘要：
Based on data for 812 Chinese farmers and a conditional mixed process (CMP) approach, this paper investigates the impact of farmers' e-commerce participation on their awareness of green production. Main results include: (1) e-commerce participation increases farmers' overall awareness of green production by 0.771 (i.e. 0.88 standard deviations); meanwhile such impact is more evident for old-generation, low-income and small-scale farmers; (2) risk awareness is most affected among three sub-dimensions; (3) three influencing channels are confirmed, which are improving information acquisition, strengthening connections with the food market, and alleviating information asymmetry. This paper concludes that e-commerce can play a significant role in promoting the green transition of farmers and agricultural production. As for implications, policymakers need to further promote e-commerce in agriculture while building a more solid food system, including green food certification and a full chain traceability system. However, farmers' heterogeneities should be considered when any intervention is proposed.
作者机构：
[Tu, Zhengge; Cao, Yu; Kong, Jiayang] Cent China Normal Univ, Sch Econ & Business Adm, Wuhan 430079, Peoples R China.;[Tu, Zhengge; Cao, Yu; Kong, Jiayang] Cent China Normal Univ, Res Ctr Low Carbon Econ & Environm Policies, Wuhan 430079, Peoples R China.;[Cao, Yu; Kong, Jiayang] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.
通讯机构：
[Jiayang Kong] S;School of Economics and Business Administration, Central China Normal University, Wuhan 430079, China<&wdkj&>School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China<&wdkj&>Research Center of Low-Carbon Economy and Environmental Policies, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
low-carbon city pilot projects;carbon emissions;program evaluation;difference-in-differences;instrument variables
摘要：
Here, we assessed the impact of low-carbon city pilot projects on carbon emissions across China through application of a series of econometric techniques to data on these three waves of low-carbon city construction. Our baseline results are obtained from a difference-in-differences estimator, comparing cities with and without introducing low-carbon city pilot projects, and show that low-carbon city pilot projects reduce carbon emissions by about 2 percentage points. We found a similar impact of low-carbon city pilot projects on carbon emissions when we controlled for the estimated propensity of a city to launch the low-carbon city pilot project based on a series of urban characteristics. We obtained comparable estimates when we instrumented whether a city would launch the low-carbon city pilot projects using regional waves of low-carbon city pilot projects. Our results also show that low-carbon city pilot projects have a larger impact on carbon emissions in northern, poorer, and less industrialized cities than those with the opposite characteristics. We found little evidence for the persistence of this impact on carbon emissions, implying that it is necessary to dynamically adjust the low-carbon city pilot projects for cities that have launched the project.
