基于最新统计分类标准的数字经济发展水平测度

随着信息技术的发展,数字经济已经成为经济增长的"新引擎"。但由于缺乏权威的产业统计分类标准,学者们一直面临"数字经济研究缺乏数字依据"的尴尬境地。文章基于国家统计局公布并实施的《数字经济及其核心产业统计分类(2021)》中的分类标准,对各省份统计年鉴的数据进行重新整理,利用熵权法构建数字经济发展指数,测度了我国30个省份的数字经济发展水平,分析了各省份数字经济发展的差异以及时空特征。研究发现,2009—2019年我国数字经济产业发展迅猛,各项子产业都取得了长足的进步。相比较而言,数字要素驱动业发展速度略低于其他三个子产业;数字经济发展存在着明显的区域不平衡。东中部地区的数字经济发展状况明显优于西部地区,南方优于北方,而且区域不平衡有持续扩大趋势。     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

科技创新、产业复杂度与经济增长

科技创新能够通过产业复杂度的变化对经济增长产生影响.以1995—2019年世界银行和哈佛大学Atlas数据库的科技创新为研究样本,运用中介效应检验方法,考察科技创新如何通过纺织类、农业类、钻石类、矿产类、金属类、化工类、汽车类、设备类和电子类的产业复杂度变化来影响国民生产总值,从而揭示了科技创新如何通过产业复杂度的变化影响经济增长的机理.研究结果表明:从总体上看,科技创新影响经济增长的过程中,产业复杂度的中介效应量为90.83％,具有显著性,其中农业类、钻石类以及矿产类相关系数为负,其他类别相关系数为正,电子类作用程度最高.区分经济体类型的研究结果显示:产业复杂度的中介效应存在异质性,发达经济体、亚洲经济体、技术密集型经济体存在相对优势,而且科技创新促进经济增长开启了"机会窗口期",后发经济体可以实现"弯道超车".这一研究结论有助于丰富科技创新领域的相关成果,能够为引导产业转型升级方向、提高国家经济增长能力提供理论依据和经验借鉴.     

Has Opposition to Immigration Increased in the United States after the Economic Crisis? An Experimental Approach

We employ two population‐level experiments to accurately measure opposition to immigration before and after the economic crisis of 2008. Our design explicitly addresses social desirability bias, which is the tendency to give responses that are seen favorably by others and can lead to substantial underreporting of opposition to immigration. We find that overt opposition to immigration, expressed as support for a closed border, increases slightly after the crisis. However, once we account for social desirability bias, no significant increase remains. We conclude that the observed increase in anti‐immigration sentiment in the post‐crisis United States is attributable to greater expression of opposition rather than any underlying change in attitudes.     

磷酸酯/Fe3+ 型油基冻胶压裂液性能研究

针对磷酸酯/Al3+ 交联的油基压裂液交联速度慢，交联剂溶解性慢、稳定性差和价格高等缺点，首次对磷酸酯/Fe3+ 交联的油基压裂液体系进行了研究。通过对铁交联剂体系的优化，筛选一种磷酸酯/Fe3+ 交联的新型交联剂体系，并对磷酸酯/Fe3+ 油基冻胶压裂液性能进行了评价。结果表明：采用30%Fe2(SO4)3+15% 二乙醇胺+55% 水制备的新型交联剂体系，直接与磷酸酯混合即可形成油基冻胶，其性能不受放置时间的影响，交联速度快，成胶性能好，10 min 就可达到最大黏度；压裂液的抗温抗剪切性能高，与常规铝交联剂体系比较，压裂液成胶速度提高了20 倍，压裂液的抗温能力由原来100 ℃提高到135 ℃，并且压裂液的破胶性和滤失性等性能均能达到压裂施工的要求。     

Environmental Justice in the Context of Scientifi Uncertainty: On the Safety of Genetically Modifi Organisms

应对风险特别是科学不确定性所导致的风险，实现和维护环境正义是当今时代的 一个前沿核心议题。转基因生物安全为代表的科学不确定性背景下的环境正义，需要 建立在相应的科学研究基础之上，但在本质上乃是一种社会构建。对转基因生物技术 及其产品等现代科技的研发、应用和推广等活动，应当维护其市场自由，与此同时， 为了公共安全以及社会弱势群体的最大利益，还应当对相关科技活动进行必要的政府 干预和法律规制。     

A new long-term survival model with dispersion induced by discrete frailty

Lifetime Data Analysis - Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there...