Manufacturing sector in China - ownership

Manufacturing sector in China - ownershi

On decomposing the energy rebound effect

YesWe suggest a new decomposition of the rebound effect, highlighting the contribution of four components: efficiency, technology, capital, and energy changes. The new approach offers several advantages: it has a strong economic foundation as it starts from the modeling of the production process, it naturally gives the option to understand how each of the four components contributes to the rebound effect, and it is based on a non-parametric estimation method that does not resort on strong assumptions nor require estimating parameters. We apply our technique to the case of China’s logistics industry, which is counteracted by increased energy consumption and carbon emissions to support economic growth. Our findings reveal that the rebound effect varies significantly across provinces, with an average of 0.76. Economic growth, driven by factors such as capital accumulation and technological advancements, plays a crucial role in determining the rebound effect, with provinces experiencing higher growth benefiting from improved energy efficiency. We further establish determinants of the rebound effect, viz., government intervention, environmental control, and economic growth. The results highlight region-specific energy policy that takes note of the spatially heterogeneous impacts of economic development and policy efforts on the rebound effect

Manufacturing sector in China - ownership

Manufacturing sector in China - ownershi

Manufacturing sector in China - ownership

Manufacturing sector in China - ownershi

Multi-output efficiency with good and bad outputs

© 2014 Elsevier B.V. All rights reserved. Cherchye, De Rock, Dierynck, Roodhooft, and Sabbe (2013) introduced a DEA methodology that is specially tailored for multi-output efficiency measurement. The methodology accounts for jointly used inputs and incorporates information on how inputs are allocated to outputs. In this paper, we present extensions that render the methodology useful to deal with undesirable (or "bad") outputs in addition to desirable (or "good") outputs. Interestingly, these extensions deal in a natural way with several limitations of existing DEA approaches to treat undesirable outputs. We also demonstrate the practical usefulness of our methodological extensions through an application to US electric utilities.status: publishe