Abstract:
We contribute to energy policy discourse in China by demonstrating the existence of multiple energy-intensity equilibria across its provinces. Using recently developed club convergence methods, we identify three unique clubs in China, each with markedly different energy intensity profiles. Unlike in previous studies, our club groupings do not strictly adhere to common geographic separations e.g. east, west and central divisions. To better understand what commonalities/disparities lay behind their groupings, we undertake a regression of the determinants of energy intensity, in a similar vein to a number of recent studies. Doing so, we demonstrate a number of significant differences in the determinants for each of the identified clubs, given which we are able to offer a rich set of policy implications. Not all determinants are common across the three clubs, and where they are common, they can differ both in magnitude and sign, reflecting the fundamental differences across the groups.

Abstract:
Oil price shocks are known to affect the financial sector of the economy, due to the inflationary effects, and increasing costs of doing business they create. Though oil-shocks and financial markets are widely researched, there remains scope for deeper understanding using firm level data. We therefore contribute to the literature by extending widely applied multi-factor asset pricing models to a sample of 963 Chinese firms (between 2005-2013) to (i) systematically evaluate their reactions to oil price shocks, and (ii) further include regulated gasoline prices as a more direct measure of the energy-prices faced by firms. 89.2% of firms are susceptible to oil shocks, with positive and negative reactions observed even for firms within the same industry. Gasoline price shocks are more pervasive, affecting 95.7% of firms. Considering oil and gasoline separately allows us to review gasoline price regulation in China, which ultimately appears ineffective in achieving its intended goals.

Abstract:
Extending existing studies based on constant structure, we adopt a time-varying approach to study energy consumption and GDP causality for China in a context of industrialization and urbanization. We find that in light of structural change, China's energy consumption is trend-stationary and thus forms no cointegration with GDP. Further, the relationship between energy consumption and GDP is two-way causal and has been decreasing in strength over time. Finally, industrialization and urbanization, especially the former, have limited effects on energy consumption, suggesting the decreasing energy intensities in individual sectors, instead of structure shift between sectors, as the main reason for China's decreasing energy intensity over the years.

Abstract:
We evaluate the probability that oil prices affect excess stock returns for U.S. listed firms. The probabilities are obtained from a time-varying multi-factor asset pricing framework estimated using dynamic model averaging techniques, including oil price information among several other possible risk factors. Two widely used oil price measures are considered, one based on raw oil price changes and another based on disentangling the source of oil price changes due to supply-side or demand-side effects. As far as we know our dataset, which comprises 10,118 stock price series with up to 25,372,588 observations between 1995-2018, is the most comprehensive used for this purpose. We develop two "beauty-contests" in which we estimate the multi-factor models separately for individual stocks, for each of the two oil price measures. The results suggests that, when working with daily data (beauty contest 1), oil price changes are a significant (important) determinant for around 1-3% of the sample. When using oil price shocks-as opposed to oil price changes-(beauty contest 2) this percentage increases to 27-45%, suggesting that oil supply and demand shocks (as opposed to oil price changes) can better explain firm-level excess returns, at least for monthly frequency data where such a decomposition is available. We provide evidence that the increase in percentage is only partially attributable to data-frequency, and more likely attributed to the decomposition into supply/demand driven oil price changes. We reconcile differences between our findings and those reported in previous literature on the basis of the fully dynamic nature of our adopted methodology.