Dequn Zhou

Measuring environmental performance in China’s industrial sectors with non-radial DEA

Abstract This paper proposes a non-radial DEA approach consisting of both a static and a dynamic environmental performance index (EPI) for measuring environmental performance. The static EPI is defined as the ratio of a non-radial efficiency measure for reducing undesirable outputs to that for increasing desirable outputs. The proposed non-radial DEA approach has been applied to model the environmental performance of industrial sectors in different provinces of China from 1998 to 2009. It has been found that the static non-radial EPI has a higher discriminating power than the EPI derived from the non-radial undesirable outputs orientation DEA models. Our empirical results also show that the environmental performance of industrial sectors in China improves by 58% in 1998–2009 that is mainly driven by the technological change.

Total-factor energy efficiency with congestion

Total-factor energy efficiency (TFEE) assessment has received increasing attention in both operations research and energy economics communities. Earlier TFEE studies implicitly assume that production activity lies in the economic area, which precludes the possibility that the production activity lies in the non-economic area and thus suffers from congestion. This paper contributes to develop TFEE index by taking into account congestion effect. It starts with the definition of congested production technology, based on which several data envelopment analysis models are proposed to construct TFEE index with congestion. The models for quantifying energy inefficiency caused by congestion effect are also developed. We apply the proposed index to evaluate the energy efficiency performance of Chinese industrial sectors at province level in 2010–2012. It is found that TFEE with congestion can yield useful insights about the choice of proper ways to achieve energy efficiency improvement. A comparison with the empirical results under congestion-free production technology indicates that ignoring congestion effect may lead to significantly different TFEE scores when congestion effect does exist.

Desirable policies of a strategic petroleum reserve in coping with disruption risk

A Markov decision process model is proposed to examine the desirable sizes and policies of a strategic petroleum reserve (SPR) for oil consumption countries. Oil consumers operate SPRs to cope with various market states. Market uncertainties include oil supply, oil price and disruption situations in which oil supply is highly stochastic. The decision criterion is to minimize total disruption losses and SPR costs. The output of the proposed model finds optimal SPR acquisition, drawdown and refill policies in response to different market states. In a representative numerical case, we examine desirable SPR size and how China should absorb into or release from its SPR in special scenarios. In a new scenario of long-duration disruption risk in particular, we find that high disruption duration risk may increase the optimal SPR size significantly, i.e., 9% greater in this case. Meanwhile, the result shows variation in the SPR drawdown policy when considering various disruption durations. Finally, a United States case has been studied with the developed model. We find interesting results by comparing the results of China and the U.S. Under the scenario of 20% disruption, although with different SPR capacities, both countries should release all SPRs to reduce GDP loss as much as possible. The paper examines strategic petroleum reserve (SPR) desirable size.The paper examines SPR policy in coping with oil supply disruptions.The SRP decision problem is formulated in a Markov decision process framework.The SPR model well handles market uncertainties factors.The optimal SPR policy minimizes SPR cost and disruption loss.

Decoupling and decomposing analysis of construction industry’s energy consumption in China

The construction industry is one of the key industries for driving energy conservation in China. Decoupling of the construction industry development from energy consumption has become the focus of the green economy. This study applied an elastic decoupling model to explore the decoupling status between energy consumption and output value in China’s construction industry. Log-Mean Divisia Index was utilized to explain the factors influencing decoupling from the perspectives of labor, efficiency, and investment. Results indicate that weak decoupling is the main status during 2000–2015. This means Chinese construction industrial development is no longer occurring at the expense of faster energy consumption growth. The labor factor is the dominant factor in the appearance of the decoupling status, and its cumulative increase impact on energy productivity is 230%, followed by efficiency factors (135%). Investment factors fail to drive the construction industry to a decoupling development state in most years, but there have been improvements during the 12th “Five-Year Plan.”