This paper proposes a new non-radial biennial Luenberger energy and environmental performance index (EEPI) to measure the energy and environmental performance (EEP) change in various Chinese cities. The sources of EEP change, in terms of technical efficiency change and technological change, are examined by Luenberger EEPI. The contributions from specific undesirable outputs and energy inputs to the EEP change are identified by means of the non-radial efficiency measure. The proposed approach is applied to evaluate the EEP of the industrial sector in 283 cities in China over 2010-2014. Factors influencing the emission abatement potential are investigated by employing geographically weighted regression (GWR) model. We find that (1) changes in EEP can be attributed to technological progress but that technological progress slows down across the study period; (2) the soot emission performance experiences a downtrend among four specific sub-performances (i.e., energy, wastewater, SO 2 and soot performances) in line with the truth that severe haze happened frequently in China; (3) the best performers begin to move from the coastal to inland cities with the less resource consumption and higher ecological quality; (4) cities with the strongest positive effect in regards to pollution intensity on emission abatement potential are located in the areas around the Bohai Gulf, where air pollution is particularly severe.

Abstract:Weather information is an important factor in short-term load forecasting (STLF). However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introduction and variable selection in STLF. Fisher information computation for one-dimensional and multidimensional weather variables is first described, and then the introduction of meteorological factors and variables selection for STLF models are discussed in detail. On this basis, different forecasting models with the proposed methodology are established. The proposed methodology is implemented on real data obtained from Electric Power Utility of Zhenjiang, Jiangsu Province, in southeast China. The results show the advantages of the proposed methodology in comparison with other traditional ones regarding prediction accuracy, and it has very good practical significance. Therefore, it can be used as a unified method for introducing weather variables into STLF models, and selecting their features.

This paper proposes a new non-radial biennial Luenberger energy and environmental 11 performance index (EEPI) to measure the EEP change in various Chinese cities. The sources of EEP 12 change, in terms of technical efficiency change and technological change, are examined by 13Luenberger EEPI. The contributions from specific undesirable outputs and energy inputs to the EEP 14 change are identified by means of the non-radial efficiency measure. The proposed approach is 15 applied to evaluate the EEP of the industrial sector in 283 cities in China over influencing the emission abatement potential are investigated by employing geographically 17 weighted regression (GWR) model. We find that 1) changes in EEP can be attributed to technological 18 progress but that technological progress slows down across the study period; 2) the soot emission 19 performance experiences a downtrend among four specific sub-performances in line with the truth 20 that severe haze happened frequently in China; 3) the best performers begin to move from the 21 coastal to inland cities with the less resource consumption and higher ecological equality; 4) cities 22 with the strongest positive effect in regards to pollution intensity on emission abatement potential 23 are located in the areas around the Bohai Gulf, where air pollution is particularly severe. 24

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