Semester

Summer

Date of Graduation

2014

Document Type

Dissertation

Degree Type

PhD

College

Davis College of Agriculture, Natural Resources and Design

Department

Forest Resource Management

Committtee Chair

Jerald J. Fletcher

Committee Co-Chair

J. Wesley Burnett

Committee Member

Alan R. Collins

Committee Member

Stratford M. Douglas

Committee Member

Timothy T. Phipps

Abstract

The three essays in this dissertation study the influential factors of energy-related carbon dioxide emission intensity, whether the province-level carbon dioxide emission intensity is convergence, and how the spatial panel data models perform in forecasting against non-spatial panel data models for province-level carbon dioxide emissions in China.;The first essay entitled "Spatial Analysis of China Province-Level CO 2 Emission Intensity" offers a unique contribution to the literature by investigating the influential factors of energy-related carbon dioxide emission intensity among a panel of 30 provinces in China covering the period 1990-2010. This study uses novel spatial panel data models to analyze the influential factors of energy-related emission intensity, which are characterized by spatial dependence. It is found that emission intensities are negatively affected by per-capita, province-level GDP and population density. This relationship implies that promoting the local economic development and population concentration may help to reduce CO2 emission intensity. In addition, emission intensities are positively affected by energy consumption structure and transportation structure. These empirical evidences indicate that Chinese government should encourage the development of less carbon-intensive energy resources and further fuel efficiency standards in its transportation sector. Finally, energy prices have been found that there is no significant effect on emission intensities. This finding may suggest that the Chinese government should further deregulate energy prices to reduce artificial price distortions.;The second essay entitled "Province-Level Convergence of China CO 2 Emission Intensity" further explores the convergence of province-level CO2emission intensity among a panel of 30 provinces in China over the period 1990-2010. This study use a novel, spatial dynamic panel data model to evaluate an empirically testable hypothesis of convergence among provinces. Based on the estimation results, I find evidence that CO2emission intensities are converging across provinces in China. Moreover, the rate of convergence is higher with the dynamic panel data model (conditional convergence) than with a cross-sectional regression model (absolute convergence). This result suggests that the individual effects that are ignored in cross-sectional regressions potentially create omitted variable bias and the panel data framework arguably offers a more precise (efficiency) rate of convergence. Finally, it is found that province-level CO2emission intensities are spatially correlated, and the rate of convergence, when controlling for spatial autocorrelation, is higher than with the non-spatial models. This result indicates that technological spillovers, embodied in both the unobserved individual effects and the spatial autocorrelation coefficient, have a direct effect on the rate of convergence of carbon intensity among provinces.;The third essay entitled "Forecasting Province-Level CO2Emissions in China" examines the performance of spatial panel data models by comparing forecasts of province-level CO2emissions against empirical reality using dynamic, spatial panel data models with and without fixed effects. From a policy standpoint, understanding how to predict emissions is important for designing climate change mitigation policies. From a statistical standpoint, it is important to test spatial econometrics models to see if they are a valid strategy to describe the underlying data. The results of this essay suggest that the best model of forecasting province-level CO2emissions is the spatio-temporal panel data model with controlling the fixed effects. The findings demonstrate the importance of considering not only spatial and temporal dependence, but also the heterogeneous characteristics within each province.

Share

COinS