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学者姓名：王健
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港口作为耗能和温室气体排放大户,研究其碳排放趋势对推进我国绿色生态港口建设至关重要.考虑到港口碳排放量波动具有多尺度特征,文章以中国主要集装箱港口为对象,构建了融合变分模态分解(VMD)-小波神经网络(WNN)-遗传算法(GA)-反向传播神经网络(BPNN)的多尺度组合预测模型.基于分解-分项预测-集成预测思想,采用VMD将碳排放量序列分解为多个模态分量;根据分量波动特征分为低、中、高频项和趋势项,分别优选预测方法实现分项预测;利用分项预测值完成集成预测并分析预测效果.实例应用表明,与现有预测模型相比,文章构建的多尺度组合预测模型能显著提高港口碳排放量预测精度,揭示港口碳排放量内在多尺度特征,有利于从能源技术、季节、突发事件等尺度制定针对性的碳减排策略.
Keyword ：
变分模态分解 变分模态分解 多尺度组合预测模型 多尺度组合预测模型 碳排放量预测 碳排放量预测 集装箱港口 集装箱港口
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| GB/T 7714 | 陈哲沣 , 王健 . 基于变分模态分解的中国主要集装箱港口碳排放组合预测 [J]. | 中国航海 , 2024 , 47 (4) : 138-145 . |
| MLA | 陈哲沣 等. "基于变分模态分解的中国主要集装箱港口碳排放组合预测" . | 中国航海 47 . 4 (2024) : 138-145 . |
| APA | 陈哲沣 , 王健 . 基于变分模态分解的中国主要集装箱港口碳排放组合预测 . | 中国航海 , 2024 , 47 (4) , 138-145 . |
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Digitization and greening, as two critical themes in the development of today's world, are also the main driving forces for high-quality and sustainable economic development. Based on the panel data of 30 provinces in China (except Tibet and Hong Kong, Macao, and Taiwan) from 2011-2019, this paper firstly measures the level of digital development of China's logistics industry by establishing a coupled and coordinated development model of China's logistics industry and digital industry, using two-way fixed effects model empirically China's logistics industry digital level on its carbon intensity, The mediating effect of logistics industry transformation and upgrading level on the impact of logistics digitalization level on its carbon emission intensity is further tested. The results show that: (1) the digitalization level of China's logistics industry shows an obvious "high in the east and low in the west"; (2) the influence of the digitalization level of the logistics industry on its carbon emission intensity is non-linear, with a significant inverse shape; (3) the transformation and upgrading level of the logistics industry has a positive mediating effect in the influence of the digitalization level of the logistics industry on its carbon emission intensity. The intermediary effect of positive regulation, in which the intermediary utility accounts for 66.7% of the total utility.
Keyword ：
carbon emission carbon emission digital economics digital economics industry amalgamation industry amalgamation logistics logistics
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| GB/T 7714 | Xie, Xinyu , Wang, Jian . The Influence and the Function of the Digitalization of Logistics on Carbon Emission in China [J]. | POLISH JOURNAL OF ENVIRONMENTAL STUDIES , 2023 , 32 (1) : 889-899 . |
| MLA | Xie, Xinyu 等. "The Influence and the Function of the Digitalization of Logistics on Carbon Emission in China" . | POLISH JOURNAL OF ENVIRONMENTAL STUDIES 32 . 1 (2023) : 889-899 . |
| APA | Xie, Xinyu , Wang, Jian . The Influence and the Function of the Digitalization of Logistics on Carbon Emission in China . | POLISH JOURNAL OF ENVIRONMENTAL STUDIES , 2023 , 32 (1) , 889-899 . |
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Demand forecasting of auto parts is an essential part of inventory control in the automotive supply chain. Due to non-stationarity, strong randomness, local mutation, and non-linearity in short-term auto parts demand data, and it is difficult to predict accurately. In this regard, this paper proposes a combination prediction model based on EEMD-CNN-BiLST-Mattention. First, the model uses the ensemble empirical mode decomposition method to decompose the original data into a series of eigenmode functions and a residual item to extract more feature information. And then uses the CNN-BiLST-Mattention model to analyze each mode separately. The components are predicted, and the prediction results are summed to obtain the final prediction result. The attention mechanism is introduced to automatically assign corresponding weights to the BiLSTM hidden layer states to distinguish the importance of different time load sequences, which can effectively reduce the loss of historical information and highlight the input of critical historical time points. Finally, the final auto parts demand prediction results are output through the fully connected layer. Then, we conduct an experimental analysis of the collected short-term demand data for auto parts. Finally, the experimental results show that the prediction model proposed in this paper has more minor errors, higher prediction accuracy, and the model prediction performance is better than the other nine comparison models, thus verifying the EEMD-CNN-BiLSTM-attention model for short-term parts demand forecasting effectiveness.
Keyword ：
auto parts auto parts BiLSTM BiLSTM Demand forecasting Demand forecasting EEMD EEMD short-term demand short-term demand
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| GB/T 7714 | Huang, Kai , Wang, Jian . Short-term auto parts demand forecasting based on EEMD-CNN-BiLSTM-Attention-combination model [J]. | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2023 , 45 (4) : 5449-5465 . |
| MLA | Huang, Kai 等. "Short-term auto parts demand forecasting based on EEMD-CNN-BiLSTM-Attention-combination model" . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 45 . 4 (2023) : 5449-5465 . |
| APA | Huang, Kai , Wang, Jian . Short-term auto parts demand forecasting based on EEMD-CNN-BiLSTM-Attention-combination model . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2023 , 45 (4) , 5449-5465 . |
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Digitization of the industrial sector is critical to combat climate change and reduce carbon emissions. The application of digital technology in traditional high-carbon emission industries can improve energy use efficiency and provide an essential technological path for carbon reduction. This study uses a coupled coordination degree model to measure the degree of integration between China's logistics industry and the digital economy. An environmental Kurtzwarts curve (EKC) panel and a non-linear mediation model was then constructed to analyse the impact of digitization of China's logistics industry on carbon emissions and its driving mechanisms using panel data from 30 regions in China from 2011 to 2020. The empirical results show that: (1) The level of integration between the logistics industry and digital economy led to an evident characteristic of 'high in the east and low in the west.' However, the regional differences are gradually decreasing over time. (2) The influence of the integration between the logistics industry and digital economy on carbon emission is in an inverted U-shape when the integration degree of the two exceeds the inflection point value of 0.76 to play the role of carbon At present, only in Guangdong, the degree of integration crosses the inflection point and plays an environment-friendly digital role. (3) As a crucial external environmental condition affecting carbon emissions, the degree of integration between logistics and digital economy will play an early role in carbon emission reduction under environmental regulation. (4) The degree of integration of the logistics industry and digital economy achieves carbon emission reduction through strengthening energy use efficiency and technological progress, in which the mediating effect of energy consumption intensity accounts for 23.05% of the total effect, ranging from 18.82% to 31.68%; the mediating effect of technological progress accounts for 13.25% of the total effect, ranging from 12.67% to 14.40%.
Keyword ：
carbon emission reduction carbon emission reduction energy efficiency energy efficiency industrial digitalization industrial digitalization logistics industry logistics industry technological progress technological progress
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| GB/T 7714 | Xie, Xinyu , Wang, Jian . Study on the impact of industrial digitization on carbon emissions: evidence from China's Logistics Industry [J]. | ENVIRONMENTAL RESEARCH COMMUNICATIONS , 2023 , 5 (10) . |
| MLA | Xie, Xinyu 等. "Study on the impact of industrial digitization on carbon emissions: evidence from China's Logistics Industry" . | ENVIRONMENTAL RESEARCH COMMUNICATIONS 5 . 10 (2023) . |
| APA | Xie, Xinyu , Wang, Jian . Study on the impact of industrial digitization on carbon emissions: evidence from China's Logistics Industry . | ENVIRONMENTAL RESEARCH COMMUNICATIONS , 2023 , 5 (10) . |
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The automobile industry is the pillar industry of the national economy. The good operation of the automobile supply chain is conducive to the sustainable development of the economy and social economy. In recent years, the popular research of automotive supply chain disruption risk management has been widely of concern by both business and academic practitioners. It is observed that most of the literature has focused only on a particular journal or field; there is a distinct lack of comprehensive bibliometric review of two decades, of research on automotive supply chain disruption risk management. This paper delivers a comprehensive bibliometric analysis that provides a better understanding not previously fully evaluated by earlier studies in the field of automotive supply chain disruption risk management. We used the 866 journal article during the period between 2000 and 2022 from the WOS database as sample data. Highlights research topics and trends, key features, developments, and potential research areas for future research. The research problems we solved are as follows: (1) Over time, how does the research in the field of automotive supply chain disruption risk management progress? (2) Which research areas and trends are getting the most attention in the field of automotive supply chain disruption risk management? (i) to recognize the scholarly production; (ii) the most productive authors; (iii) the most productive organization; (iv) the most cited articles; and (v) the most productive countries. (3) What is the research direction of automotive supply chain disruption risk management in the future? Also discusses the shortcomings of literature and bibliometric analysis. These findings provide a potential road map for researchers who intend to engage in research in this field.
Keyword ：
automotive supply chain disruption automotive supply chain disruption bibliometric analysis bibliometric analysis co-citation analysis co-citation analysis disruption risk disruption risk supply chain resilience supply chain resilience
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| GB/T 7714 | Huang, Kai , Wang, Jian , Zhang, Jinxin . Automotive Supply Chain Disruption Risk Management: A Visualization Analysis Based on Bibliometric [J]. | PROCESSES , 2023 , 11 (3) . |
| MLA | Huang, Kai 等. "Automotive Supply Chain Disruption Risk Management: A Visualization Analysis Based on Bibliometric" . | PROCESSES 11 . 3 (2023) . |
| APA | Huang, Kai , Wang, Jian , Zhang, Jinxin . Automotive Supply Chain Disruption Risk Management: A Visualization Analysis Based on Bibliometric . | PROCESSES , 2023 , 11 (3) . |
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物流业与数字经济融合发展带来的产业运行效率的提升以及生产方式的转变,是物流产业转型升级的重要驱动力.本文从产业融合角度入手,采用熵权-Topsis法测度我国30个省(区、市)物流业与数字经济发展水平.在此基础上,利用耦合协调模型测度物流业与数字经济融合水平,并通过dagum基尼系数、莫兰指数、LISA集聚图以及空间核密度分析揭示我国物流业与数字经济融合水平的区域差异、空间分布特征以及动态演进趋势.研究发现:(1)我国物流业与数字经济融合水平呈现出明显的"东高西低"特征,其中江苏和广东物流业与数字经济融合水平最高,达到优质协调级别.(2)我国物流业与数字经济融合水平的总体基尼系数、区域间差异呈波动下降趋势,区域内差异呈波动上升趋势.表明我国整体物流业与数字经济融合水平的差异在逐渐缩小,但是区域内却因为虹吸效应差异在进一步扩大.(3)我国物流业与数字经济融合水平已形成两大稳定的集聚区,分别是以上海为中心的"高-高"集聚区和以甘肃为中心的"低-低"集聚区.(4)在考虑到时间滞后和空间临近的条件下,物流业与数字经济融合水平呈现出截然不同的演进趋势.
Keyword ：
产业融合 产业融合 区域差异 区域差异 数字经济 数字经济 物流业与数字经济融合 物流业与数字经济融合 空间kernel密度 空间kernel密度
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| GB/T 7714 | 谢欣雨 , 王健 . 中国物流业与数字经济融合水平的区域差异及动态演进 [J]. | 调研世界 , 2023 , (2) : 33-46 . |
| MLA | 谢欣雨 等. "中国物流业与数字经济融合水平的区域差异及动态演进" . | 调研世界 2 (2023) : 33-46 . |
| APA | 谢欣雨 , 王健 . 中国物流业与数字经济融合水平的区域差异及动态演进 . | 调研世界 , 2023 , (2) , 33-46 . |
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本文选取汽车制造企业上市公司作为研究样本,并充分考虑供应链风险管理对供应链整合的影响,对供应链整合和汽车制造企业上市公司运营绩效的关系以及供应链风险管理在汽车制造企业上市公司供应链整合管理中所发挥的作用进行探讨.研究结果表明,供应链整合对企业运营绩效存在显著的负向影响.供应链风险管理对企业运营绩效存在显著的正向影响,而汽车制造企业上市公司的供应链风险管理对供应链整合与企业运营绩效之间的关系存在显著的调节效应.
Keyword ：
企业异质性 企业异质性 供应链整合 供应链整合 汽车制造业 汽车制造业 调节效应 调节效应 运营绩效 运营绩效 风险管理 风险管理
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| GB/T 7714 | 黄凯 , 王健 , 谢欣雨 . 供应链整合、风险管理有效性与运营绩效 ——以汽车制造企业上市公司为例 [J]. | 工业技术经济 , 2023 , 42 (1) : 38-44 . |
| MLA | 黄凯 等. "供应链整合、风险管理有效性与运营绩效 ——以汽车制造企业上市公司为例" . | 工业技术经济 42 . 1 (2023) : 38-44 . |
| APA | 黄凯 , 王健 , 谢欣雨 . 供应链整合、风险管理有效性与运营绩效 ——以汽车制造企业上市公司为例 . | 工业技术经济 , 2023 , 42 (1) , 38-44 . |
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Developing low-carbon logistics requires an understanding of the driving factors for carbon emissions. We employed the Comparative Study on Urban Transport and the Environment (CUTE) framework to identify the driving factors of China's logistics carbon emissions. Then, the Generalized Fisher Index Decomposition (GFID) model was adopted to decompose the effects of the driving factors. Finally, we tracked the spatial dynamics trajectory of each driving effect based on the gravity model. Our results showed that technical intensity and transport structure promoted carbon emissions, while technical efficiency and agglomeration curbed carbon emissions. The gravity centers of transport structure and technical efficiency converged with that of carbon emissions, whereas the gravity centers of technical intensity and industry agglomeration diverged from that of carbon emissions. The driving effects showed an obvious spatial heterogeneity, which indicated that carbon reduction policies should be formulated according to the local situation.
Keyword ：
carbon emissions carbon emissions CUTE framework CUTE framework driving factors driving factors GFID model GFID model Keywords Keywords logistics industry logistics industry
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| GB/T 7714 | Lin, Shuangjiao , Wang, Jian . Driving Factors of Carbon Emissions in China's Logistics Industry [J]. | POLISH JOURNAL OF ENVIRONMENTAL STUDIES , 2022 , 31 (1) : 163-177 . |
| MLA | Lin, Shuangjiao 等. "Driving Factors of Carbon Emissions in China's Logistics Industry" . | POLISH JOURNAL OF ENVIRONMENTAL STUDIES 31 . 1 (2022) : 163-177 . |
| APA | Lin, Shuangjiao , Wang, Jian . Driving Factors of Carbon Emissions in China's Logistics Industry . | POLISH JOURNAL OF ENVIRONMENTAL STUDIES , 2022 , 31 (1) , 163-177 . |
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This study uses the super efficiency DEA model to evaluate the logistics industry's total factor energy efficiency in various Chinese provinces, autonomous regions, and municipalities, and examines the relationship between environmental regulations and the total factor energy efficiency. The results show that environmental regulations can help improve the logistics industry's total factor energy efficiency. This study further analyzes the threshold effect of environmental pollution control investment level, quality of labor, and logistics industry development level in environmental regulations on the logistics industry's total factor energy efficiency. The results showed that the level of investment in environmental pollution control in environmental regulations has a significant single threshold effect on logistics industry's total factor energy efficiency. The quality of labor has a significant double threshold effect on environmental regulation's impact on the logistics industry's total factor energy efficiency. Logistics industry development level has a significant double threshold effect on the impact of environmental regulation on logistics industry's total factor energy efficiency.
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| GB/T 7714 | Huang, Kai , Wang, Jian . Research on the Impact of Environmental Regulation on Total Factor Energy Effect of Logistics Industry from the Perspective of Green Development [J]. | MATHEMATICAL PROBLEMS IN ENGINEERING , 2022 , 2022 . |
| MLA | Huang, Kai 等. "Research on the Impact of Environmental Regulation on Total Factor Energy Effect of Logistics Industry from the Perspective of Green Development" . | MATHEMATICAL PROBLEMS IN ENGINEERING 2022 (2022) . |
| APA | Huang, Kai , Wang, Jian . Research on the Impact of Environmental Regulation on Total Factor Energy Effect of Logistics Industry from the Perspective of Green Development . | MATHEMATICAL PROBLEMS IN ENGINEERING , 2022 , 2022 . |
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Technological innovation is an important way to reduce carbon emissions in the logistics industry. Using panel data of the logistics industry in China's 30 provinces from 2002 to 2019, a dynamic spatial Durbin model was constructed to explore the impact of logistics technological innovation on carbon emissions, and determine whether there is a rebound effect. Furthermore, the rebound effect of logistics technological innovation was calculated, and its spatial-temporal characteristics were analyzed. It was found that there is a "U"-shaped relationship between logistics technological innovation and carbon emissions at the national and regional levels, which means that the impact of logistics technological innovation on carbon emissions has a rebound effect. Moreover, the spatial spillover of technological innovation strengthens the rebound effect, while the optimiza-tion of the energy structure weakens it. Further calculation showed that the average spatial rebound effect is 60.61%, which is 22.66% higher than the average direct rebound effect. The spatial rebound effect has obvious regional heterogeneity, with the highest in the central region, followed by the eastern region, and the lowest in the western region. For temporal variation, at the national level, the spatial rebound effect was successively shown as a backfire effect, partial rebound effect, super energy-saving effect, and partial rebound effect. Simi-larly, three regions fluctuate circularly in these rebound effects with different forms. Thus, the rebound effect can be alleviated through energy structure optimization, energy price marketization, and a regional collaborative development mechanism.
Keyword ：
Carbon emissions Carbon emissions Energy structure Energy structure Logistics technological innovation Logistics technological innovation Rebound effect Rebound effect Spatial spillover Spatial spillover
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| GB/T 7714 | Liang, Hongyan , Lin, Shuangjiao , Wang, Jian . Impact of technological innovation on carbon emissions in China?s logistics industry: Based on the rebound effect [J]. | JOURNAL OF CLEANER PRODUCTION , 2022 , 377 . |
| MLA | Liang, Hongyan 等. "Impact of technological innovation on carbon emissions in China?s logistics industry: Based on the rebound effect" . | JOURNAL OF CLEANER PRODUCTION 377 (2022) . |
| APA | Liang, Hongyan , Lin, Shuangjiao , Wang, Jian . Impact of technological innovation on carbon emissions in China?s logistics industry: Based on the rebound effect . | JOURNAL OF CLEANER PRODUCTION , 2022 , 377 . |
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