Understanding the underlying reasons for greenhouse gas (GHG) emissions trends in different countries is fundamental for climate change mitigation. This paper identifies the main determinants that affect GHG emissions growth and assesses their impact and differences among countries in Europe. Previous studies have produced inconclusive results and presented several limitations, such as the lack of quality of the data used, the reduced identification of determinants and the use of methods that did not enable hypothesis testing. Conversely, this research identifies an extended list of determinants of GHG emissions, performs an in-depth statistical analysis and contrasts the significance of determinants using panel data and multiple linear regression models for the period 1990–2017 for the main Eurozone countries. The study found that GDP and final energy intensity are the main drivers for the reduction of GHG emissions in Europe. Furthermore, energy prices are not significant and heterogeneous results are found for the renewable energy, fuel mix and carbon intensity determinants, pointing to a different behavior at the country level. The uneven impact of the main determinants of GHG emission growth suggest that a differentiated application of European policies at country level will enhance the efficiency of mitigation efforts in Europe.

This paper explores the cross-sectional variation of expected returns for a large cross section of industry and size/book-to-market portfolios. We employ mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors and innovations to well-known macroeconomic variables. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications and model misspecification. However, the traditional 2-pass ordinary least squares (OLS) cross-sectional regressions produce an estimate of the market risk premium that is negative, and significantly different from 0. Using alternative procedures, we compare both beta estimators. We conclude that beta estimates under MIDAS present lower mean absolute forecasting errors and generate better out-of-sample performance of the optimized portfolios relative to OLS betas.

Purpose: – In this paper, the authors aim to analyze the impact of International Financial Reporting Standards' (IFRS) mandatory adoption on the financial statements of Spanish listed companies.Design/methodology/approach: – The authors estimate a panel data model by generalized least squares' within-between in order to contrast the possible structural breaks in the relations between income statement items and balance sheet items, using data from the 35 largest listed companies.Findings: – The results show significant changes on these relations, but with different signs and degrees of intensity depending on the balance sheet item analyzed.Research limitations/implications: – The data choice introduces a size bias that could be taken into account in the generalization of the results to other listed companies.Originality/value: – This work is developed using a mandatory, local, accounting and panel data framework for first time using Spanish listed companies in order to measure the impact of the IFRS adoption.