Chinas Provincial(中国省级)研究综述
Chinas Provincial 中国省级 - Furthermore, we validate the relationship between the extent of natural resource endowment, the quality of institutions and economic growth using China’s provincial data. [1] The results show that the urbanization level of China’s provincial regions is uneven. [2] The Moran scatter plot indicates that China’s provincial TFEE has not only spatial dependence characteristics but also differences in spatial correlation. [3] The empirical analysis of China’s provincial power sectors based on the constructed models came to the following results. [4] (2) Compared with partner countries, China’s provincial logistics development level presented a greater impact on bilateral trade. [5] Therefore, using China’s provincial-level data, this study investigates the impact of market reform (MR) on energy efficiency (EE). [6] Therefore, the empirical results suggest that China’s provincial governments, which have gained significant fiscal power through increased fiscal decentralization, should take the problem of income inequality more seriously, because fiscal decentralization and income equality may both have the potential to benefit public health. [7] The results show that: (1) there is a spatial agglomeration effect and a positive spatial dependence relationship in China’s provincial per capita FNF (FNFP), which verifies that the relationship between China’s FNF and economy is in the early stage of EKC hypothesis curve. [8] To clarify whether there is a spatial correlation problem among China’s provincial energy consumption, economic growth, and ecological environment, this paper adopts the panel data of 31 provinces in China from 2008 to 2019 and uses a spatial data analysis method. [9] This study examined the relationship between the air quality index (AQI), COVID-19, and the oil market using China’s provincial data for the first four months of the pandemic (1 January–22 April 2020). [10] Different from previous studies that focus on the provincial governors and party secretaries, this paper makes the first attempt to examine whether the career turnover of China’s provincial environmental agency heads (PEAHs) is affected by the environmental performance within their jurisdictions. [11] The research model was then empirically tested and validated through quantitative research using China’s provincial panel data from 2004 to 2018. [12] The results show that: (1) China’s provincial carbon productivity presents an increasing trend in 2001–2017, but the differences in carbon productivity among provinces are widening. [13] Therefore, this study examines the impact of digital financial inclusion and human capital on China’s provincial economic growth. [14] The empirical evidence based on China’s provincial dataset over the period 2000–2016 implies that energy-saving R&D has not played a positive role in influencing energy demand. [15] In this paper, we introduce resource misallocation in the process of discussing the impact of environmental regulation on TFP, taking China’s provincial industrial panel data from 1997 to 2017 as a sample, and the spatial econometric method is employed to investigate whether environmental regulation has a resource reallocation effect and affects TFP. [16] This study developed an integrated framework to explore China’s provincial household ECW nexus as well as their drivers from the years 2000 through 2016. [17] This paper uses China’s provincial panel data from 1997 to 2015 to construct the Malmquist- Luenberger productivity indicators to measure the level of green biased technology progress, and measures the change in industrial structure based on indicators of low-carbon transformation, optimization and rationalization of industrial structure, and empirically tests the impact of green biased technology progress and industrial structure adjustment on China’s provincial carbon emission intensity. [18] Methods China’s provincial panel data from 2007 to 2018, collected by the National Bureau of Statistics of China, were used to establish a panel regression model in order to investigate the impact of the upgrading of the industrial structure on the income and expenditure of the urban employee basic medical insurance fund. [19] This paper establishes a linear regression model and a random effect model, and uses China’s provincial panel data from 2000 to 2013. [20] To verify the theory, this paper takes the charging pile of new energy vehicles as an example, using regression analysis of China’s provincial panel data from 2016 to 2020 to draw a significant positive conclusion, which is aimed to better meet the new infrastructure era and realize a new round of great development. [21] Based on China’s provincial data from 2005 to 2015, this article uses the Dagum Gini coefficient to decompose the spatial non-equilibrium of per capita transportation carbon emissions from static viewpoint. [22] This article attempts to study the “club phenomenon” of the uneven development of China’s regional economy from the perspective of knowledge spillover, using the Spatial Dubin Model (SDM) to process China’s provincial data from 1991 to 2015. [23] Using data envelopment analysis and China’s provincial data for 2017, this study analysed the input–output efficiency of the water-energy-food nexus by considering production-based intensity, consumption-based intensity, and the quantity index system. [24] In order to explore the relationship between these three factors, this paper constructs a nonlinear threshold regression model based on China’s provincial panel data from 2009 to 2018, and empirically analyzes the threshold effect of FDI on regional innovation capability with the intensity of intellectual property protection as the threshold variable. [25] The corresponding network slack-based model (SBM) is proposed to analyse the performance of China’s provincial industry sector. [26] This paper explores the spatial distribution and convergence of China’s provincial carbon intensity during 2000–2017 and its influencing factors employing spatial panel techniques. [27] The initial model and the two-stage model are applied to optimize the staple crop spatial distribution in China’s provincial administrative districts. [28] Based on China’s provincial panel data from 1990 to 2017 and the improved Lucas, Nelson & Phelps model, the Spatial Dubin Model is used to test the spatial effects of higher education and human capital quality. [29] Based on China’s provincial panel data from 2008 to 2019, using the intermediary adjustment model and the spatial Dubin model to analyze and test the industrial mechanism, spatial mechanism, and spatial effect attenuation boundary of the GBD effect on CEEOCI. [30] Therefore, the empirical results suggest that China’s provincial governments, which have gained significant fiscal power through increased fiscal decentralization, should take the problem of income inequality more seriously, because fiscal decentralization and income equality may both have the potential to benefit public health. [31] This study first attempts to use the parameterized quadratic directional distance function (DDF) approach to calculate China’s provincial carbon abatement cost and carbon reduction potential (CRP) under different scenarios from 2000 to 2017. [32] Using China’s provincial data from 2005 to 2018, we detected the threshold effect of financial structure on capital allocation efficiency. [33] Based on the solid theoretical foundations and literature review, we aim to investigate the spatial mechanisms of regional innovation mobility at China’s provincial scale. [34] This paper explores the spatial agglomeration and spatial dependence of China’s provincial energy consumption scale, structure, and efficiency on economic growth from 2000 to 2017 through the spatial econometric analysis method. [35] This article examines the effects of governance continuity on public administration efficiency with China’s provincial level data. [36] On the basis of the DIFI and China’s Provincial Panel data, this study aims to test the poverty reduction effect of digital inclusive finance in three dimensions of income, education, and healthcare and further look at the transmission mechanism of digital inclusive finance in poverty alleviation. [37] The overall total factor productivity of China’s provincial non-life insurance industry is on the rise, mainly due to the improvement of pure technical efficiency and scale efficiency, while technological progress has an inhibiting effect on the contrary. [38]此外，我们使用中国省级数据验证了自然资源禀赋程度、制度质量和经济增长之间的关系。 [1] 结果表明，中国省域城市化水平参差不齐。 [2] Moran 散点图表明，中国省级 TFEE 不仅具有空间依赖特征，而且在空间相关性上也存在差异。 [3] 基于所构建模型的中国省域电力部门的实证分析得出以下结果。 [4] （2）与伙伴国相比，中国省级物流发展水平对双边贸易的影响更大。 [5] 因此，本研究使用中国省级数据，调查市场改革（MR）对能源效率（EE）的影响。 [6] 因此，实证结果表明，通过加强财政分权获得显着财政权力的中国省级政府应该更加重视收入不平等问题，因为财政分权和收入平等都可能有利于公共卫生。 [7] 结果表明：（1）中国省级人均FNF（FNFP）存在空间集聚效应和正空间依赖关系，验证了中国FNF与经济的关系处于EKC假设曲线的早期阶段。 [8] 为明确中国各省能源消费、经济增长与生态环境之间是否存在空间相关性问题，本文采用2008-2019年中国31个省份的面板数据，采用空间数据分析方法。 [9] 本研究使用大流行前四个月（2020 年 1 月 1 日至 4 月 22 日）的中国省级数据，研究了空气质量指数 (AQI)、COVID-19 和石油市场之间的关系。 [10] 有别于以往对省长、省委书记的研究，本文首次尝试考察中国省级环保局局长（PEAHs）的职业更替是否受到辖区内环保绩效的影响。 [11] 然后，使用中国 2004 年至 2018 年的省级面板数据，通过定量研究对研究模型进行了实证检验和验证。 [12] 结果表明：（1）2001-2017年中国各省碳生产力呈现上升趋势，但各省碳生产力差异正在拉大。 [13] 因此，本研究考察了数字普惠金融和人力资本对中国省级经济增长的影响。 [14] 基于 2000-2016 年中国省级数据集的经验证据表明，节能研发在影响能源需求方面并未发挥积极作用。 [15] 本文在探讨环境规制对全要素生产率影响的过程中，引入资源错配，以1997-2017年中国省级工业面板数据为样本，运用空间计量经济学方法考察环境规制是否具有资源性。再分配效应并影响 TFP。 [16] 本研究建立了一个综合框架，以探索 2000 年至 2016 年中国省级家庭 ECW 关系及其驱动因素。 [17] 本文利用中国1997-2015年省级面板数据构建Malmquist-Luenberger生产力指标来衡量绿色偏向技术进步的水平，并以产业低碳转型、优化和合理化指标衡量产业结构的变化。结构，并实证检验了绿色偏向技术进步和产业结构调整对中国省级碳排放强度的影响。 [18] 方法利用国家统计局收集的2007-2018年中国省级面板数据，建立面板回归模型，考察产业结构升级对城镇职工收入和支出的影响。基本医疗保险基金。 [19] 本文建立了线性回归模型和随机效应模型，并使用了2000-2013年的中国省级面板数据。 [20] 为验证理论，本文以新能源汽车充电桩为例，对2016-2020年中国省级面板数据进行回归分析，得出显着正向结论，旨在更好地适应新基建时代，实现新一轮大发展。 [21] 本文基于2005-2015年中国省级数据，采用达古姆基尼系数从静态角度分解人均交通碳排放的空间非均衡。 [22] 本文试图从知识溢出的角度研究中国区域经济发展不平衡的“俱乐部现象”，采用空间杜宾模型（Spatial Dubin Model，SDM）处理中国1991-2015年的省级数据。 [23] 本研究利用数据包络分析和2017年中国省级数据，结合生产强度、消费强度和数量指标体系，分析了水-能源-食品关系的投入产出效率。 [24] 为探究这三个因素之间的关系，本文基于2009-2018年中国省级面板数据构建非线性阈值回归模型，实证分析FDI对区域创新能力的阈值效应，知识产权保护强度为：阈值变量。 [25] 提出了相应的基于网络松弛的模型（SBM）来分析中国省级工业部门的表现。 [26] 本文利用空间面板技术探讨了 2000-2017 年中国省级碳强度的空间分布和收敛性及其影响因素。 [27] 应用初始模型和两阶段模型优化我国省级行政区主粮作物空间分布。 [28] 基于1990-2017年中国省级面板数据和改进的Lucas, Nelson & Phelps模型，空间杜宾模型用于检验高等教育和人力资本质量的空间效应。 [29] 基于2008-2019年中国省级面板数据，运用中介调整模型和空间杜宾模型，分析检验GBD效应对CEEOCI的产业机制、空间机制、空间效应衰减边界。 [30] 因此，实证结果表明，通过加强财政分权获得显着财政权力的中国省级政府应该更加重视收入不平等问题，因为财政分权和收入平等都可能有利于公共卫生。 [31] 本研究首次尝试使用参数化二次方向距离函数（DDF）方法计算2000-2017年不同情景下中国省级碳减排成本和碳减排潜力（CRP）。 [32] 利用中国2005-2018年的省级数据，我们检测了金融结构对资本配置效率的门槛效应。 [33] 基于扎实的理论基础和文献回顾，我们旨在研究中国省级区域创新流动的空间机制。 [34] 本文通过空间计量分析方法探讨了2000-2017年中国各省能源消费规模、结构和效率对经济增长的空间集聚和空间依赖关系。 [35] 本文利用中国省级数据检验了治理连续性对公共行政效率的影响。 [36] 本研究基于DIFI和中国省级面板数据，从收入、教育、医疗三个维度检验数字普惠金融的扶贫效果，进一步探讨数字普惠金融在扶贫中的传导机制。 [37] 中国省级非寿险行业整体全要素生产率呈上升趋势，主要得益于纯技术效率和规模效率的提高，而技术进步则相反。 [38]