近年来,随着我国科学技术的发展,金融业也发生了显著的变化。在多元化金融领域,以金融科技为首的多元化金融体系逐渐占据主导地位。金融科技的出现对提升金融服务效率起到了非常重要的作用。然而,金融科技与金融科技风险密切相关。本文...近年来,随着我国科学技术的发展,金融业也发生了显著的变化。在多元化金融领域,以金融科技为首的多元化金融体系逐渐占据主导地位。金融科技的出现对提升金融服务效率起到了非常重要的作用。然而,金融科技与金融科技风险密切相关。本文运用AHP + 模糊综合评价模型,寻求50位金融专家对金融业风险进行综合量化,探究当前我国金融业风险的主导因素,从而进行可预测的干预。研究发现,技术风险、道德风险和法律风险,权重为76%,模糊评价指标为“高”,是影响金融科技风险的主要因素,而传统金融风险占多数但仅占24%。虽然权重占比不大,但仍不容忽视。本文旨在对模糊的金融业风险进行量化,探讨金融科技风险和传统金融风险在当前金融业中的主导地位,得出面对中国金融业未来的发展,需要更加重视介入金融科技带来的风险,以优化资源配置,但传统金融风险也不容忽视。In recent years, with the development of science and technology in China, the financial industry has also undergone significant changes. In the diversified financial field, the diversified financial system headed by financial technology gradually occupies a dominant position. The cash of financial technology has played a very important role in improving the efficiency of financial services. However, fintech goes hand in hand with fintech risks. This paper uses AHP + fuzzy comprehensive evaluation model, seeks 50 financial experts to comprehensively quantify the risk of financial industry, and explores the leading factors of China’s financial industry risks at present, so as to make predictable intervention. It is found that technical risk, moral risk and legal risk, with a weight of 76% and a fuzzy evaluation index of “high”, are the main factors affecting financial technology risks, while traditional financial risks account for the majority but only account for 24%. Although the weight ratio is not large, it still cannot be ignored. The purpose of this paper is to quantify the vague financial industry risks, explore the dominance of financial technology risks and traditional financial risks in the current financial industry, and conclude that in the face of the future development of China’s financial industry, it is necessary to pay more attention to intervening in the risks brought by financial technology, so as to optimize resource allocation, but traditional financial risks cannot be ignored.展开更多
In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all usef...In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.展开更多
研究采用2000~2021年中国30个省份能源耗费数据,基于ARIMA模型和BP神经网络模型,测算并预测了2000~2035年中国30个省份碳排放总量,采用ArcGIS和标准椭圆差对时空演变特征进行了可视化分析,进一步利用LMDI模型对影响碳排放的驱动因素进...研究采用2000~2021年中国30个省份能源耗费数据,基于ARIMA模型和BP神经网络模型,测算并预测了2000~2035年中国30个省份碳排放总量,采用ArcGIS和标准椭圆差对时空演变特征进行了可视化分析,进一步利用LMDI模型对影响碳排放的驱动因素进行了分解。研究结果表明:(1) 2000~2035年,我国碳排放总量逐年递增,但碳排放增长率逐渐降低;碳排放结构为“第二产业 > 居民生活 > 第三产业 > 第一产业”,第二产业和居民生活碳的增长速度较快,第一产业和第三产业变化趋势较小;(2) 我国各省碳排放的空间分布呈现典型的“东部 > 中部 > 西部”,“北部 > 南部”的分布格局,碳排放中心有向西北移动的趋势;(3) 数字经济、产业结构高级化以及新质生产力发展水平较高的地区碳排放相对较少,具有显著的组别差异效应;(4) 能源消费强度效应是驱动碳排放不断增长主要因素,人均GDP和能源消费结构效应是抑制碳排放的主要因素,产业结构和人口规模效应的影响相对较小。基于研究结论,从能源结构、产业结构、新质生产力和数字经济等方面提出了政策建议。Based on the energy consumption data of 30 provinces in China from 2000 to 2021, the total carbon emissions of 30 provinces in China from 2000 to 2035 were measured and predicted based on the ARIMA model and BP neural network model. The results show that: (1) From 2000 to 2035, China’s total carbon emissions will increase year by year, but the growth rate of carbon emissions will gradually decrease;The carbon emission structure is “secondary industry > residents’ daily life > tertiary industry > primary industry”, the secondary industry and residents’ living carbon growth rate is relatively fast, and the change trend of the primary industry and the tertiary industry is small. (2) The spatial distribution of carbon emissions in various provinces in China presents a typical distribution pattern of “eastern > central > western” and “northern > south”, and the carbon emission center has a tendency to move to the northwest. (3) The carbon emissions of the regions with higher levels of digital economy, industrial structure and new productivity are relatively small, which has a significant group difference effect. (4) The energy consumption intensity effect is the main factor driving the continuous growth of carbon emissions, the per capita GDP and energy consumption structure effect are the main factors inhibiting carbon emissions, and the impact of industrial structure and population scale effect is relatively small. Based on the research conclusions, policy suggestions are put forward from the aspects of energy structure, industrial structure, new quality productivity and digital economy.展开更多
文摘近年来,随着我国科学技术的发展,金融业也发生了显著的变化。在多元化金融领域,以金融科技为首的多元化金融体系逐渐占据主导地位。金融科技的出现对提升金融服务效率起到了非常重要的作用。然而,金融科技与金融科技风险密切相关。本文运用AHP + 模糊综合评价模型,寻求50位金融专家对金融业风险进行综合量化,探究当前我国金融业风险的主导因素,从而进行可预测的干预。研究发现,技术风险、道德风险和法律风险,权重为76%,模糊评价指标为“高”,是影响金融科技风险的主要因素,而传统金融风险占多数但仅占24%。虽然权重占比不大,但仍不容忽视。本文旨在对模糊的金融业风险进行量化,探讨金融科技风险和传统金融风险在当前金融业中的主导地位,得出面对中国金融业未来的发展,需要更加重视介入金融科技带来的风险,以优化资源配置,但传统金融风险也不容忽视。In recent years, with the development of science and technology in China, the financial industry has also undergone significant changes. In the diversified financial field, the diversified financial system headed by financial technology gradually occupies a dominant position. The cash of financial technology has played a very important role in improving the efficiency of financial services. However, fintech goes hand in hand with fintech risks. This paper uses AHP + fuzzy comprehensive evaluation model, seeks 50 financial experts to comprehensively quantify the risk of financial industry, and explores the leading factors of China’s financial industry risks at present, so as to make predictable intervention. It is found that technical risk, moral risk and legal risk, with a weight of 76% and a fuzzy evaluation index of “high”, are the main factors affecting financial technology risks, while traditional financial risks account for the majority but only account for 24%. Although the weight ratio is not large, it still cannot be ignored. The purpose of this paper is to quantify the vague financial industry risks, explore the dominance of financial technology risks and traditional financial risks in the current financial industry, and conclude that in the face of the future development of China’s financial industry, it is necessary to pay more attention to intervening in the risks brought by financial technology, so as to optimize resource allocation, but traditional financial risks cannot be ignored.
基金Outstanding Youth Foundation of Hunan Provincial Department of Education(Grant No.22B0911)。
文摘In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.
文摘研究采用2000~2021年中国30个省份能源耗费数据,基于ARIMA模型和BP神经网络模型,测算并预测了2000~2035年中国30个省份碳排放总量,采用ArcGIS和标准椭圆差对时空演变特征进行了可视化分析,进一步利用LMDI模型对影响碳排放的驱动因素进行了分解。研究结果表明:(1) 2000~2035年,我国碳排放总量逐年递增,但碳排放增长率逐渐降低;碳排放结构为“第二产业 > 居民生活 > 第三产业 > 第一产业”,第二产业和居民生活碳的增长速度较快,第一产业和第三产业变化趋势较小;(2) 我国各省碳排放的空间分布呈现典型的“东部 > 中部 > 西部”,“北部 > 南部”的分布格局,碳排放中心有向西北移动的趋势;(3) 数字经济、产业结构高级化以及新质生产力发展水平较高的地区碳排放相对较少,具有显著的组别差异效应;(4) 能源消费强度效应是驱动碳排放不断增长主要因素,人均GDP和能源消费结构效应是抑制碳排放的主要因素,产业结构和人口规模效应的影响相对较小。基于研究结论,从能源结构、产业结构、新质生产力和数字经济等方面提出了政策建议。Based on the energy consumption data of 30 provinces in China from 2000 to 2021, the total carbon emissions of 30 provinces in China from 2000 to 2035 were measured and predicted based on the ARIMA model and BP neural network model. The results show that: (1) From 2000 to 2035, China’s total carbon emissions will increase year by year, but the growth rate of carbon emissions will gradually decrease;The carbon emission structure is “secondary industry > residents’ daily life > tertiary industry > primary industry”, the secondary industry and residents’ living carbon growth rate is relatively fast, and the change trend of the primary industry and the tertiary industry is small. (2) The spatial distribution of carbon emissions in various provinces in China presents a typical distribution pattern of “eastern > central > western” and “northern > south”, and the carbon emission center has a tendency to move to the northwest. (3) The carbon emissions of the regions with higher levels of digital economy, industrial structure and new productivity are relatively small, which has a significant group difference effect. (4) The energy consumption intensity effect is the main factor driving the continuous growth of carbon emissions, the per capita GDP and energy consumption structure effect are the main factors inhibiting carbon emissions, and the impact of industrial structure and population scale effect is relatively small. Based on the research conclusions, policy suggestions are put forward from the aspects of energy structure, industrial structure, new quality productivity and digital economy.
