Energy efficiency and firm performance

This thesis sheds light on different aspects of the performance of Swedish industrial firms. To this end, the analysis defines and measures energy efficiency in an economic context, as well as investigating the implicit relationships between energy efficiency and other firm performance metrics – productivity and environmental performance. Paper I estimates energy efficiency using a “true” random effects stochastic frontier model. The presence of energy inefficiency indicates the potential for energy consumption reduction. Paper II includes undesirable outputs when measuring energy efficiency in a non-parametric model approach. To assess the impacts of efficiency determinants, a double bootstrap procedure is adopted for the second-stage regression analysis. Paper III investigates firm performance in three dimensions – productivity, energy efficiency, and environmental performance. A panel vector auto-regression model is utilized to examine the causal and dynamic relationships between the three dimensions of firm performance and the environmental investment. The overarching conclusion from the thesis is that there is considerable potential to improve energy efficiency in Swedish industrial firms. It is very likely that the permit price of the EU emissions trading system for CO2 and the Swedish CO2 tax rate were too low to create incentives to improve energy efficiency. A firm strategy that emphasizes energy efficiency improvements is also likely to save costs and be beneficial for overall productivity in later periods. Environmental performance comes at a cost in terms of lower productivity, and thus the results cannot corroborate the win-win outcome postulated by the so-called Porter Hypothesis

Conditional Distance Correlation Test for Gene Expression Level, DNA Methylation Level and Copy Number

Over the past years, efforts have been devoted to the genome-wide analysis of genetic and epigenetic profiles to better understand the underlying biological mechanisms of complex diseases such as cancer. It is of great importance to unravel the complex dependence structure between biological factors, and many conditional dependence tests have been developed to meet this need. The traditional partial correlation method can only capture the linear partial correlation, but not the nonlinear correlation. To overcome this limitation, we propose to use the innovative conditional distance correlation (CDC), which measures the conditional dependence between random vectors and detect nonlinear relations. In this thesis, the CDC measure is applied to the rich Cancer Genome Atlas (TCGA) ovarian cancer data, and we identify a list of interesting genes with nonlinear features. We integrate three important types of molecular features including gene expression, DNA methylation and copy number variation, and implement the partial correlation test and CDC test to infer the relations between the three measurements for each gene. Out of 196 candidate oncogenes and tumor suppressors, we identify 19 genes in which two of the molecular features are nonlinearly dependent given the third variable. Of these 19 genes, many were reported to be associated with ovarian cancer or breast cancer in the literature. Our findings could shed new light on the biological relations between the three important molecular aspects. This thesis is structured as follows: we begin with a brief introduction to ovarian cancer, TCGA data, the three molecular measurements, and two testing methods in Chapter 1. In the second chapter, we review different statistical methods including Pearson’s partial correlation and conditional distance correlation. In Chapter 3, we conduct an extensive simulation study to compare the empirical performance of different methods. In Chapter 4, we apply the new method to the TCGA ovarian data. We conclude the thesis with future directions in Chapter 5