Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover e...Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.展开更多
It is clearly stated in the 19th people's congress that we should make the environmental protection as our national policy. Therefore, it is of great importance to study this issue. This article is going to consid...It is clearly stated in the 19th people's congress that we should make the environmental protection as our national policy. Therefore, it is of great importance to study this issue. This article is going to consider 30 provinces of China as the cross-section, and utilize the data sample from 2006 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of FDI. By using these data, this article creates a comprehensive environmental pollution index with the help of entropy. The result indicates that the effect of FDI on environment has a non-linear and spatial spillover characteristic. Before reaching the critical value, FDI has a negative effect on environment; however, with the accumulation of FDI, it will create a significant positive effect on the environment.展开更多
In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues...In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.展开更多
This article considers 30 provinces of China as the cross-section subjects, and utilizes the data sample from 2009 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect o...This article considers 30 provinces of China as the cross-section subjects, and utilizes the data sample from 2009 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of environmental regulation on employment. The result indicates that environmental regulation has negative effect on employment with the consideration of spatial spillover effect, and this adverse effect is not significant mathematically. With the enhance of environmental regulation, the negative impact on employment will decrease accordingly, even may eventually promote job growth, which means there may be a non-linear relationship between them. Specifically, the direct effect of environmental regulation on employment indicates that it is beneficial for job growth whereas the indirect effect illustrate that it is detrimental for employment.展开更多
可持续发展目标(Sustainable Development Goals,SDGs)的实现往往会因为生态保护或人类福祉之间可能存在的权衡关系而受到阻碍。将生态系统服务(Ecosystem services,ESs)纳入到可持续发展目标的决策中能够避免各方利益的冲突,促进SDGs...可持续发展目标(Sustainable Development Goals,SDGs)的实现往往会因为生态保护或人类福祉之间可能存在的权衡关系而受到阻碍。将生态系统服务(Ecosystem services,ESs)纳入到可持续发展目标的决策中能够避免各方利益的冲突,促进SDGs的实现。然而,在生态环境脆弱的山区,ESs对SDGs的贡献分析仍然不足。以川西地区为研究区,对2000—2020年11个可持续发展目标进行了量化,利用生态系统服务和权衡的综合评估(Integrated Valuation of Ecosystem Services and Tradeoffs,InVEST)模型和定量指标法估算了碳固存、土壤保持和食物生产三种重要生态系统服务,并使用空间面板数据模型研究ESs对SDGs的影响及其空间溢出效应。结果表明:(1)可持续发展目标水平整体提升,但SDG1(无贫穷)和SDG3(良好健康和人类福祉)表现较差,分值低于5分。从空间上看,与环境相关的SDGs在川西北高原表现更好,与社会经济相关的SDGs在川西东部和川西南山地地区表现更好。(2)川西地区碳固存和食物生产服务呈现线性增长趋势,土壤保持服务呈现波动增长趋势,分别增长了0.23×10~8t、8.93×10~5t和1.23×10~8t。(3)与土壤保持和食物生产相比,碳固存对SDGs表现出更强烈的直接影响和空间溢出效应。其中县域碳固存对本县域和邻近县域的SDG11(可持续城市和社区)和SDG1具有显著的促进作用,对SDG2(零饥饿)呈现显著负向影响。研究结果可为区域联合管理提供科学依据,推动可持续发展目标的实现。展开更多
文摘Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.
基金supported by the Hubei Province Educational Division Social Science Research Project(Grant No.15G051)
文摘It is clearly stated in the 19th people's congress that we should make the environmental protection as our national policy. Therefore, it is of great importance to study this issue. This article is going to consider 30 provinces of China as the cross-section, and utilize the data sample from 2006 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of FDI. By using these data, this article creates a comprehensive environmental pollution index with the help of entropy. The result indicates that the effect of FDI on environment has a non-linear and spatial spillover characteristic. Before reaching the critical value, FDI has a negative effect on environment; however, with the accumulation of FDI, it will create a significant positive effect on the environment.
基金Supported by the National Natural Science Foundation of China(71131008(Key Project)and 71271179)
文摘In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.
基金supported by the Hubei Province Educational Division Social Science Research Project (Grant No. 15G051)
文摘This article considers 30 provinces of China as the cross-section subjects, and utilizes the data sample from 2009 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of environmental regulation on employment. The result indicates that environmental regulation has negative effect on employment with the consideration of spatial spillover effect, and this adverse effect is not significant mathematically. With the enhance of environmental regulation, the negative impact on employment will decrease accordingly, even may eventually promote job growth, which means there may be a non-linear relationship between them. Specifically, the direct effect of environmental regulation on employment indicates that it is beneficial for job growth whereas the indirect effect illustrate that it is detrimental for employment.
文摘可持续发展目标(Sustainable Development Goals,SDGs)的实现往往会因为生态保护或人类福祉之间可能存在的权衡关系而受到阻碍。将生态系统服务(Ecosystem services,ESs)纳入到可持续发展目标的决策中能够避免各方利益的冲突,促进SDGs的实现。然而,在生态环境脆弱的山区,ESs对SDGs的贡献分析仍然不足。以川西地区为研究区,对2000—2020年11个可持续发展目标进行了量化,利用生态系统服务和权衡的综合评估(Integrated Valuation of Ecosystem Services and Tradeoffs,InVEST)模型和定量指标法估算了碳固存、土壤保持和食物生产三种重要生态系统服务,并使用空间面板数据模型研究ESs对SDGs的影响及其空间溢出效应。结果表明:(1)可持续发展目标水平整体提升,但SDG1(无贫穷)和SDG3(良好健康和人类福祉)表现较差,分值低于5分。从空间上看,与环境相关的SDGs在川西北高原表现更好,与社会经济相关的SDGs在川西东部和川西南山地地区表现更好。(2)川西地区碳固存和食物生产服务呈现线性增长趋势,土壤保持服务呈现波动增长趋势,分别增长了0.23×10~8t、8.93×10~5t和1.23×10~8t。(3)与土壤保持和食物生产相比,碳固存对SDGs表现出更强烈的直接影响和空间溢出效应。其中县域碳固存对本县域和邻近县域的SDG11(可持续城市和社区)和SDG1具有显著的促进作用,对SDG2(零饥饿)呈现显著负向影响。研究结果可为区域联合管理提供科学依据,推动可持续发展目标的实现。
