Land urbanization and urban CO2 emissions: Empirical evidence from Chinese prefecture-level cities

Changes in land use and the resulting human practices in the land urbanization process would lead to variations in the function, intensity, and efficiency of CO2 emissions and greatly influence urban CO2 emissions. Therefore, using Chinese prefecture-level data for a time period ranging from 2003 to 2017, we systematically examine the mechanism of how land urbanization influences CO2 emissions based on land-use intensity regulation, land-use structure optimization, and land-use efficiency improvements. First, the benchmark results show that land urbanization's influence on urban CO2 emissions is significantly positive. This indicates that the consumption effect caused by land urbanization exceeds the agglomeration effect. Furthermore, the results of the nonlinear analysis using the spatial adaptive semi-parametric and semi-parametric spatial dynamic panel models show that the association between land urbanization and carbon emissions demonstrates an inverted U-shaped curve. Simultaneously, land urbanization represents a dynamic cumulative and spatial spillover effect on urban CO2 emissions. Second, a mechanism analysis reveals that effective land urbanization can promote CO2 emission reductions through efficiency improvement, structure optimization and proper control of the land-use intensity. Additionally, we analyze heterogeneity in regional differences. In the line with study findings, the central government in China should promote the optimization of territorial spatial governance, optimize energy consumption structures, make comprehensive use of its funds, tax policies, industrial development support, and market-oriented mechanisms, and further optimize the layout of urban space.

Non-Iterative Multiscale Estimation for Spatial Autoregressive Geographically Weighted Regression Models

Multiscale estimation for geographically weighted regression (GWR) and the related models has attracted much attention due to their superiority. This kind of estimation method will not only improve the accuracy of the coefficient estimators but also reveal the underlying spatial scale of each explanatory variable. However, most of the existing multiscale estimation approaches are backfitting-based iterative procedures that are very time-consuming. To alleviate the computation complexity, we propose in this paper a non-iterative multiscale estimation method and its simplified scenario for spatial autoregressive geographically weighted regression (SARGWR) models, a kind of important GWR-related model that simultaneously takes into account spatial autocorrelation in the response variable and spatial heterogeneity in the regression relationship. In the proposed multiscale estimation methods, the two-stage least-squares (2SLS) based GWR and the local-linear GWR estimators of the regression coefficients with a shrunk bandwidth size are respectively taken to be the initial estimators to obtain the final multiscale estimators of the coefficients without iteration. A simulation study is conducted to assess the performance of the proposed multiscale estimation methods, and the results show that the proposed methods are much more efficient than the backfitting-based estimation procedure. In addition, the proposed methods can also yield accurate coefficient estimators and such variable-specific optimal bandwidth sizes that correctly reflect the underlying spatial scales of the explanatory variables. A real-life example is further provided to demonstrate the applicability of the proposed multiscale estimation methods.

Bayesian P-Splines Quantile Regression of Partially Linear Varying Coefficient Spatial Autoregressive Models

This paper deals with spatial data that can be modelled by partially linear varying coefficient spatial autoregressive models with Bayesian P-splines quantile regression. We evaluate the linear and nonlinear effects of covariates on the response and use quantile regression to present comprehensive information at different quantiles. We not only propose an empirical Bayesian approach of quantile regression using the asymmetric Laplace error distribution and employ P-splines to approximate nonparametric components but also develop an efficient Markov chain Monte Carlo technique to explore the joint posterior distributions of unknown parameters. Monte Carlo simulations show that our estimators not only have robustness for different spatial weight matrices but also perform better compared with quantile regression and instrumental variable quantile regression estimators in finite samples at different quantiles. Finally, a set of Sydney real estate data applications is analysed to illustrate the performance of the proposed method.

Bayesian Analysis of Partially Linear Additive Spatial Autoregressive Models with Free-Knot Splines

This article deals with symmetrical data that can be modelled based on Gaussian distribution. We consider a class of partially linear additive spatial autoregressive (PLASAR) models for spatial data. We develop a Bayesian free-knot splines approach to approximate the nonparametric functions. It can be performed to facilitate efficient Markov chain Monte Carlo (MCMC) tools to design a Gibbs sampler to explore the full conditional posterior distributions and analyze the PLASAR models. In order to acquire a rapidly-convergent algorithm, a modified Bayesian free-knot splines approach incorporated with powerful MCMC techniques is employed. The Bayesian estimator (BE) method is more computationally efficient than the generalized method of moments estimator (GMME) and thus capable of handling large scales of spatial data. The performance of the PLASAR model and methodology is illustrated by a simulation, and the model is used to analyze a Sydney real estate dataset.