Chinese Provincial(中国省级)研究综述
Chinese Provincial 中国省级 - We test this executive constraint theory using panel data on Chinese provincial-level court leaders who served from 2003 to 2012. [1] This paper uses the panel data of Chinese provincial-level administrative units in 2007−2017 and adopts the panel regression model and panel quantile regression model to empirically analyze the relationship between debt burden level and average propensity to consume (APC). [2] This paper studies the pollution industry transfer in China from the perspective of industry mobility, and proves that the transfer of pollution industries under environmental regulations is the comprehensive effect of “Pollution Haven” and “Innovation Compensation” by using Chinese provincial-level data from 2000 to 2015. [3] This paper conducts an empirical test of political budget cycles using Chinese provincial-level data from 1990 to 2006. [4] In this paper, we tried to show that the theory of planned behavior provides a useful conceptual framework for SI when facing a pandemic risk, and a regression method with Chinese provincial (Guangdong Province) data was applied to investigate how attitude (ATT), subjective norms (SN), and perceived behavioral control (PBC) influence SI when facing a pandemic emergency. [5]我们使用 2003 年至 2012 年任职的中国省级法院领导的面板数据来检验这一行政约束理论。 [1] 本文利用中国省级行政单位2007-2017年的面板数据，采用面板回归模型和面板分位数回归模型，实证分析债务负担水平与平均消费倾向（APC）的关系。 [2] 本文从产业流动性视角研究中国污染产业转移，利用2000年中国省级数据证明环境规制下的污染产业转移是“污染避风港”和“创新补偿”的综合效应。到 2015 年。 [3] 本文使用 1990 年至 2006 年的中国省级数据对政治预算周期进行了实证检验。 [4] 在本文中，我们试图证明计划行为理论为面对大流行风险时的 SI 提供了一个有用的概念框架，并应用中国省（广东省）数据的回归方法来研究态度（ATT）、主观规范 (SN) 和感知行为控制 (PBC) 在面临大流行紧急情况时会影响 SI。 [5]
green total factor 绿色全因子
Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China’s green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. [1] Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China's green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. [2]本研究使用 2006-2017 年中国省级面板数据，使用动态空间杜宾模型、中介效应模型和动态阈值面板模型，调查互联网是否提高了中国的绿色全要素能源效率 (GTFEE)。 [1] 本研究利用 2006-2017 年中国省级面板数据，使用动态空间杜宾模型、中介效应模型和动态阈值面板模型，调查互联网是否提高了中国的绿色全要素能源效率 (GTFEE)。 [2]
31 Chinese Provincial
Methods The main tasks of our study were to estimate the direct medical costs of syphilis in China at subnational level, and to characterize the distribution of the direct medical cost of syphilis in 31 Chinese provincial districts in relation to GDP. [1] In this paper, the sustainability of the 31 Chinese provincial-level administrative regions (provinces for short) was investigated using a composite sustainability indicator (CSI). [2] Death and meteorological data of 31 Chinese provincial capital cities during 2008–2013 was analyzed in this study. [3] More specifically, it uses panel data on 31 Chinese provincial/first level administrative units, collected over a decade, from 2003 to 2015, to analyze how socioeconomic status in the general public and the political and policy structures have shaped civic environmentalism. [4] By distinguishing non-residential areas and introducing detailed residential building information, we proposed a novel HVR estimation model, thus realizing the estimation of HVR in 31 Chinese provincial cities with different development levels (Tier 1–Tier 3). [5]方法 我们研究的主要任务是估算中国地方一级的梅毒直接医疗费用，并描述中国 31 个省区的梅毒直接医疗费用相对于 GDP 的分布情况。 [1] 本文采用综合可持续性指标（CSI）对中国31个省级行政区域（简称省）的可持续性进行了调查。 [2] 本研究分析了 2008-2013 年中国 31 个省会城市的死亡和气象数据。 [3] 更具体地说，它使用从 2003 年到 2015 年十多年来收集的 31 个中国省级/一级行政单位的面板数据，分析公众的社会经济地位以及政治和政策结构如何塑造公民环保主义。 [4] 通过区分非居住区并引入详细的住宅建筑信息，我们提出了一种新颖的 HVR 估计模型，从而实现了中国 31 个不同发展水平（Tier 1-Tier 3）省级城市的 HVR 估计。 [5]
30 Chinese Provincial 30 中国省级
Based on panel data from 30 Chinese provincial administrations during 2001-2016, this study uses the Global Malmquist-Luenberger (GML) index to measure provincial green productivity and employs a spatial econometric model to examine the impact of environmental regulation on green productivity. [1] Based on a unique data set of reputational blacklists and redlists in 30 Chinese provincial-level administrative divisions (ADs), we show the diversity, flexibility, and comprehensiveness of the SCS listing infrastructure. [2] ,This study proposes a topic−sentiment analysis using a mixed social media analytics framework to analyse the microblogs collected from the Sina Weibo accounts of 30 Chinese provincial police departments. [3] This study analyzed six life-cycle stages and calculated the life-cycle CO2 emissions of the construction sector in 30 Chinese provincial jurisdictions to understand the disparity among them. [4] This study analyzed six life-cycle stages and calculated the life-cycle CO2 emissions of the construction sector in 30 Chinese provincial jurisdictions to understand the disparity among them. [5]本研究基于 2001-2016 年中国 30 个省级行政部门的面板数据，采用全球 Malmquist-Luenberger (GML) 指数衡量省级绿色生产力，并采用空间计量模型检验环境规制对绿色生产力的影响。 [1] 我们基于中国 30 个省级行政区 (AD) 的声誉黑名单和红名单的独特数据集，展示了 SCS 上市基础设施的多样性、灵活性和全面性。 [2] , 本研究提出使用混合社交媒体分析框架进行主题-情感分析，以分析从中国 30 个省级公安部门的新浪微博账户中收集的微博。 [3] 本研究分析了 6 个生命周期阶段，并计算了中国 30 个省辖区建筑行业的生命周期二氧化碳排放量，以了解它们之间的差异。 [4] 本研究分析了 6 个生命周期阶段，并计算了中国 30 个省辖区建筑行业的生命周期二氧化碳排放量，以了解它们之间的差异。 [5]
Using Chinese Provincial 使用中国省
Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China’s green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. [1] Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China's green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. [2] Using Chinese provincial panel data during the period 2005–2018, and applying simultaneous equation modelling techniques to control for endogeneity, we find that China’s OFDI presents a significant positive impact on domestic employment, and the BRI moderates this impact positively. [3] Using Chinese provincial-level data from 2005 to 2017, we quantify the effects of bank presence on rural income. [4]本研究使用 2006-2017 年中国省级面板数据，使用动态空间杜宾模型、中介效应模型和动态阈值面板模型，调查互联网是否提高了中国的绿色全要素能源效率 (GTFEE)。 [1] 本研究利用 2006-2017 年中国省级面板数据，使用动态空间杜宾模型、中介效应模型和动态阈值面板模型，调查互联网是否提高了中国的绿色全要素能源效率 (GTFEE)。 [2] nan [3] nan [4]
Use Chinese Provincial 使用中国省
To explore FDI’s impact on China’s economic growth, technological innovation, and environmental pollution, we use Chinese provincial panel data for 2004–2016 to construct a dynamic panel simultaneous-equation model. [1] Design/methodology/approachThis study uses Chinese provincial panel data from 1998 to 2015 and utilizes GMM method to estimate the stimulating effect of RMB appreciation on technical transactions through trade competition. [2] Based on the meta-frontier Malmquist-Luenberger (MML) productivity index, this paper uses Chinese provincial panel data (2003–2015) to measure the green total factor productivity (TFP) of China's manufacturing. [3]为了探究 FDI 对中国经济增长、技术创新和环境污染的影响，我们使用 2004-2016 年中国省级面板数据构建了动态面板联立方程模型。 [1] 设计/方法/途径本研究使用1998-2015年中国省级面板数据，利用GMM方法估计人民币升值对贸易竞争对技术交易的刺激作用。 [2] 基于元前沿Malmquist-Luenberger（MML）生产率指数，本文采用中国省级面板数据（2003-2015）测度中国制造业绿色全要素生产率（TFP）。 [3]
34 Chinese Provincial 34 中国省级
The dataset contained live streaming content related to 147 countries and 34 Chinese provincial administrative regions between 2010 and 2021. [1] Methods An online cross-sectional survey was conducted among 19,515 pregnant women from all 34 Chinese provincial-level administrative regions from February 25 to March 10, 2020. [2]该数据集包含 2010 年至 2021 年间与 147 个国家和 34 个中国省级行政区域相关的直播内容。 [1] 方法 2020 年 2 月 25 日至 3 月 10 日，对来自中国 34 个省级行政区的 19515 名孕妇进行在线横断面调查。 [2]
chinese provincial panel 中国省级专家组
Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China’s green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. [1] To explore FDI’s impact on China’s economic growth, technological innovation, and environmental pollution, we use Chinese provincial panel data for 2004–2016 to construct a dynamic panel simultaneous-equation model. [2] Based on Chinese provincial panel data from 2003 to 2017, this paper tested the effect of different agglomeration modes on green technology innovation (green process innovation and green product innovation) under environmental regulation. [3] Using Chinese provincial panel data for the period 2006–2017, this study investigates whether the internet has improved China's green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model, mediation effect model and dynamic threshold panel model. [4] Using the 1996-2016 Chinese provincial panel data to measure and analyze the human capital structure and total factor productivity, and empirically examines the impact of human capital structure on high-quality economic growth. [5] Using Chinese provincial panel data during the period 2005–2018, and applying simultaneous equation modelling techniques to control for endogeneity, we find that China’s OFDI presents a significant positive impact on domestic employment, and the BRI moderates this impact positively. [6] Based on the Chinese provincial panel data collected from 2000 to 2014, the spatial correlation characteristics of PM2. [7] This study empirically analysed the linear and nonlinear relationships between industrial structure adjustment and energy intensity, using a Chinese provincial panel dataset for 1997–2016. [8] This article summarized the mechanism of the impact of real estate tax on city output path, on the basis of selecting the Chinese provincial panel data from 2005 to 2019, by using the PD - GMM method. [9] We adopted econometric models with the effect of every dimension on green growth and empirically analyzed with the generalized method of moments, based on Chinese provincial panel data from the years 2000–2016. [10] This paper constructs green TFP (GTFP) based on the SBM–Global–Luenberger index, using Chinese provincial panel data from 2003 to 2017. [11] Design/methodology/approachThis study uses Chinese provincial panel data from 1998 to 2015 and utilizes GMM method to estimate the stimulating effect of RMB appreciation on technical transactions through trade competition. [12] This paper applies an autoregressive distributed lag–pooled mean group(ARDL–PMG) model to evaluate the relationship between agricultural production factor prices, food consumption prices, and agricultural carbon emissions, using Chinese provincial panel data from 1994 to 2018. [13] Using the Chinese provincial panel data, empirical analysis shows that the hypothesis is well confirmed. [14] Based on Chinese provincial panel data, the empirical analysis was established using data envelopment analysis (DEA), Malmquist index, Theil index decomposition method and Grey correlation analysis method. [15] Based on the meta-frontier Malmquist-Luenberger (MML) productivity index, this paper uses Chinese provincial panel data (2003–2015) to measure the green total factor productivity (TFP) of China's manufacturing. [16] This paper explores the influence of the urbanrural social security imbalance on the consumption inequality with the dynamic panel data model, using the Chinese provincial panel data from 2000 to 2016. [17] Based on Chinese provincial panel data over the period 2007–2016, a threshold model analysis found that the impact of social security on productivity has a “double threshold” on human capital. [18] Based on the theoretical mechanism of the aforementioned variables, this study uses the Chinese provincial panel data from 2001 to 2016. [19] Firstly, an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was established to study the impact of technological innovation on carbon dioxide (CO2) emissions based on Chinese provincial panel data from 1997 to 2015. [20] ,The Chinese provincial panel data from 2006 to 2016 were analyzed with the use of Arellano–Bond linear dynamic panel data estimations. [21] Considering the possible nonlinear and spatial effects of tourism on carbon emissions, this paper employed spatial econometric models combined with quadratic terms of explanatory variables to explore the nexus between them using Chinese provincial panel data from 2003 to 2016. [22]本研究使用 2006-2017 年中国省级面板数据，使用动态空间杜宾模型、中介效应模型和动态阈值面板模型，调查互联网是否提高了中国的绿色全要素能源效率 (GTFEE)。 [1] 为了探究 FDI 对中国经济增长、技术创新和环境污染的影响，我们使用 2004-2016 年中国省级面板数据构建了动态面板联立方程模型。 [2] 基于2003-2017年中国省级面板数据，本文检验了环境规制下不同集聚模式对绿色技术创新（绿色工艺创新和绿色产品创新）的影响。 [3] 本研究利用 2006-2017 年中国省级面板数据，使用动态空间杜宾模型、中介效应模型和动态阈值面板模型，调查互联网是否提高了中国的绿色全要素能源效率 (GTFEE)。 [4] nan [5] nan [6] 基于2000-2014年收集的中国省级面板数据，PM2.5的空间相关特征。 [7] 本研究使用 1997-2016 年的中国省级面板数据集，实证分析了产业结构调整与能源强度之间的线性和非线性关系。 [8] 本文在选取2005-2019年中国省级面板数据的基础上,采用PD-GMM方法,总结了房地产税对城市产出路径的影响机制。 [9] 我们基于 2000-2016 年的中国省级面板数据，采用了各维度对绿色增长影响的计量经济学模型，并采用广义矩量法进行了实证分析。 [10] 本文基于 SBM-Global-Luenberger 指数，使用 2003 年至 2017 年的中国省级面板数据构建绿色 TFP（GTFP）。 [11] 设计/方法/途径本研究使用1998-2015年中国省级面板数据，利用GMM方法估计人民币升值对贸易竞争对技术交易的刺激作用。 [12] 本文采用自回归分布滞后池均值组（ARDL-PMG）模型，利用中国1994年至2018年的省级面板数据，评估农业生产要素价格、食品消费价格与农业碳排放之间的关系。 [13] 使用中国省级面板数据，实证分析表明该假设得到了很好的证实。 [14] 以中国省级面板数据为基础，采用数据包络分析（DEA）、Malmquist指数、泰尔指数分解法和灰色相关分析法建立实证分析。 [15] 基于元前沿Malmquist-Luenberger（MML）生产率指数，本文采用中国省级面板数据（2003-2015）测度中国制造业绿色全要素生产率（TFP）。 [16] 本文利用2000-2016年的中国省级面板数据，利用动态面板数据模型探讨城乡社会保障失衡对消费不平等的影响。 [17] 基于2007-2016年中国省级面板数据，阈值模型分析发现，社会保障对生产力的影响对人力资本具有“双重阈值”。 [18] 基于上述变量的理论机制，本研究使用了2001-2016年的中国省级面板数据。 [19] 首先，基于1997-2015年中国省级面板数据，建立了一个扩展的人口、富裕和技术回归随机影响（STIRPAT）模型，研究技术创新对二氧化碳（CO2）排放的影响。 [20] ,采用 Arellano-Bond 线性动态面板数据估计对 2006 年至 2016 年中国省级面板数据进行分析。 [21] 考虑到旅游对碳排放可能存在的非线性和空间效应，本文采用空间计量模型结合解释变量的二次项，利用中国2003-2016年的省级面板数据，探讨二者之间的关系。 [22]
chinese provincial datum 中国省级基准
Hence, this study examines the effects of household consumption on carbon emissions using the Chinese provincial data from 1995 to 2017. [1] This paper studies the impact of financial structure on energy conservation and emission reduction by using Chinese provincial data from 2000 to 2017. [2] This paper uses spatial panel methods and Chinese provincial data from 2003 to 2017 to study the spatial spillovers of financial openness on economic growth. [3] Based on Chinese provincial data from 2006 to 2017, this paper uses the non-radial directional distance function (NDDF) to measure energy and carbon emission performance and then constructs a two-way fixed-effect model. [4] Moreover, according to Chinese provincial data, the joint term for FDI with REC and TI is negatively associated with carbon emissions. [5] Based on Chinese provincial data from 2006 to 2014, this study analyzes the problem from the aspect of the public's own living environment and investigates the interaction between public participation behavior and policy. [6] We utilize Chinese provincial data from 2002 to 2016 and apply the spatial panel data techniques to explicitly consider spatial correlations and spillover effects between observations. [7] Based on Chinese provincial data over 2000-2015 and panel data models, this paper regards the CO2 emissions as climate change and explores the response of renewable energy technological innovation to intensive CO2 emissions. [8] The paper studies the impact of urbanization on residential energy consumption using annual Chinese provincial data from 1996 to 2014. [9] Using the Chinese provincial data from 2005–2015, this article analyzes the convergence characteristics of per capita transportation carbon emissions in China. [10] The author established a theoretical framework to analyze the influence of trade openness on the level of human capital and adopted the system GMM to test it, using Chinese provincial data over the period 1995–2015. [11] Our framework was applied to Chinese provincial data from 2000 to 2016. [12]因此，本研究使用 1995 年至 2017 年的中国省级数据检验了家庭消费对碳排放的影响。 [1] 本文利用2000-2017年的中国省级数据研究了金融结构对节能减排的影响。 [2] 本文利用空间面板方法和中国2003-2017年的省级数据研究金融开放对经济增长的空间溢出效应。 [3] 本文基于2006-2017年中国省级数据，采用非径向定向距离函数（NDDF）衡量能源和碳排放绩效，然后构建双向固定效应模型。 [4] 此外，根据中国省级数据，与 REC 和 TI 的 FDI 联合项与碳排放负相关。 [5] 本研究基于2006-2014年中国省级数据，从公众自身的生存环境角度分析问题，考察公众参与行为与政策的相互作用。 [6] 我们利用 2002 年至 2016 年的中国省级数据，并应用空间面板数据技术来明确考虑观测之间的空间相关性和溢出效应。 [7] 本文基于2000-2015年中国省级数据和面板数据模型，将CO2排放视为气候变化，探讨可再生能源技术创新对CO2密集排放的响应。 [8] 本文使用 1996 年至 2014 年中国各省年度数据研究城市化对住宅能源消耗的影响。 [9] 本文利用2005-2015年的中国省级数据，分析了中国人均交通碳排放的收敛特征。 [10] 作者建立了一个理论框架来分析贸易开放对人力资本水平的影响，并采用GMM系统对其进行了检验，并使用了1995-2015年的中国省级数据。 [11] 我们的框架适用于 2000 年至 2016 年的中国省级数据。 [12]
chinese provincial city
,The findings provide a methodology for indexing cities, with 15 Chinese provincial cities as examples. [1] Finally, experiments on Xi’an (a Chinese provincial city) show that urban youth can be characterized excellently according to the comparison with the actual situation of the experimental samples. [2] By distinguishing non-residential areas and introducing detailed residential building information, we proposed a novel HVR estimation model, thus realizing the estimation of HVR in 31 Chinese provincial cities with different development levels (Tier 1–Tier 3). [3],研究结果提供了一种城市指数化的方法，以中国 15 个省级城市为例。 [1] 最后，在西安（中国省级城市）的实验表明，通过与实验样本的实际情况进行比较，可以很好地表征城市青年。 [2] 通过区分非居住区并引入详细的住宅建筑信息，我们提出了一种新颖的 HVR 估计模型，从而实现了中国 31 个不同发展水平（Tier 1-Tier 3）省级城市的 HVR 估计。 [3]
chinese provincial capital
Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. [1] Death and meteorological data of 31 Chinese provincial capital cities during 2008–2013 was analyzed in this study. [2]选择了成都、重庆、昆明、西宁和南宁等丝绸之路经济带沿线的九个中国省会城市，以 2001 年至 2010 年的数据对所提出的方法进行了测试。 [1] 本研究分析了 2008-2013 年中国 31 个省会城市的死亡和气象数据。 [2]
chinese provincial jurisdiction
This study analyzed six life-cycle stages and calculated the life-cycle CO2 emissions of the construction sector in 30 Chinese provincial jurisdictions to understand the disparity among them. [1] This study analyzed six life-cycle stages and calculated the life-cycle CO2 emissions of the construction sector in 30 Chinese provincial jurisdictions to understand the disparity among them. [2]本研究分析了 6 个生命周期阶段，并计算了中国 30 个省辖区建筑行业的生命周期二氧化碳排放量，以了解它们之间的差异。 [1] 本研究分析了 6 个生命周期阶段，并计算了中国 30 个省辖区建筑行业的生命周期二氧化碳排放量，以了解它们之间的差异。 [2]
chinese provincial carbon 中国省碳
Then, we apply this approach to evaluate the relative economic and energy conservation performances of 15 allocation schemes by reallocating the 2015 Chinese provincial carbon emission quotas, each of which is constructed via a combination of equity, grandfathering, efficiency and ability to pay principles. [1] This paper aims to analyze the impacts of emission taxes, investments in the energy sector, expenditure on research and development, technological innovation, and tertiary sector development on the Chinese provincial carbon dioxide emission figures between 1995 and 2019. [2]然后，我们应用这种方法通过重新分配 2015 年中国省级碳排放配额来评估 15 个分配方案的相对经济和节能绩效，每个配额都是通过公平、祖父、效率和支付能力原则的组合来构建的。 [1] 本文旨在分析排放税、能源部门投资、研发支出、技术创新和第三产业发展对 1995 年至 2019 年中国各省二氧化碳排放数据的影响。 [2]