To make the environmental performance measuring system a useful inspective and supervisory tool during the life cycle of a Green Manufacturing (GM) program, a measure system is presented. It includes three primary indexes corresponding to the GM program life cycle, which are fundamental construction level, application level and continuous improving level. And 19 secondary measures are subdivided, too. After that, the values of the measures are determined involving qualitative and quantitative index. And then Back Propagation Neural Network (BPNN) is applied to evaluate the environmental performance of GM programs in enterprise, in which 10 samples are used to train the measuring model with Matlab 7.0, and the data are selected from the Internet and digital library. The environmental performance evaluation of a machine tool company in Chongqing shows the effectiveness and validity of the model, and this method can be applied in industrial enterprises while implementing GM.

Analysis of energy consumption which could explore energy saving potential is of important significance for energy saving in manufacturing enterprises. An analysis model including physical and monetary input-output models for energy consumption in manufacturing enterprises based on input-output theory is constructed. Physical and monetary energy consumption coefficient matrices are obtained from the analysis model. Then, some application ideas are proposed, including vertical and horizontal comparison of coefficient matrices elements of a manufacturing enterprise in per unit time, comparison of coefficient matrices elements of a manufacturing enterprise in different unit times, comparison of coefficient matrices elements of manufacturing enterprises within the same industry in per unit time, effect analysis of energy price change to energy consumption of a manufacturing enterprise. Finally, a case study of a heavy-machinery manufacturing enterprises validates its practicability.

To provide referenced risk assessment model for implementing remanufacturing program in enterprise, a set of evaluating indicators was proposed according to the characteristics of the remanufacturing program’s life cycle, which includes acquisition, assessment, disassembly, reproducing and reprocessing phases; And Back Propagation neural network (BPNN) was applied to measure the risk of the remanufacturing system as evaluating method; In addition, the influence of the evaluating indicators on the output was calculated by the Relationship Function between the networked weights, so the key indicators can be found out. The risk assessment model is trained by five samples obtained from the Internet, and is verified by the case of one machining tools company.

To help electronic manufacturers implement their green manufacturing (GM) strategies more friendly to environment and more effectively in saving resources, an operational GM model is proposed based on systematical analyses of each stage of the electronic product life cycle including material selection, design, production, use, end-of-life product disassembly and recycling, etc. This model consists of three levels-supply chain, enterprise and workshop and each level deals with different issues. Supply chain level focuses on manufacturing plants and remanufacturing plants based on the analysis of the whole supply chain-raw materials supply, production and marketing, etc. Enterprise level consists of GM implementation motivation, enterprise strategies, system implementation, supporting elements such as information systems. Based on analysis of the main resources and environment problems, workshop level mainly includes green manufacturing process such as lead-free soldering, process improvement, process monitoring and data acquisition, etc.