The evaluation model of the enterprise energy efficiency based on DPSR
- 656 Downloads
The reasonable evaluation of the enterprise energy efficiency is an important work in order to reduce the energy consumption. In this paper, an effective energy efficiency evaluation index system is proposed based on DPSR (Driving forces-Pressure-State-Response) with the consideration of the actual situation of enterprises. This index system which covers multi-dimensional indexes of the enterprise energy efficiency can reveal the complete causal chain which includes the “driver forces” and “pressure” of the enterprise energy efficiency “state” caused by the internal and external environment, and the ultimate enterprise energy-saving “response” measures. Furthermore, the ANP (Analytic Network Process) and cloud model are used to calculate the weight of each index and evaluate the energy efficiency level. The analysis of BL Company verifies the feasibility of this index system and also provides an effective way to improve the energy efficiency at last.
KeywordsEnterprise energy efficiency Index system DPSR model ANP Cloud model
Since the 1990s, World Energy Council (WEC) (2006) proposed the concept of “energy efficiency” at the first time; many countries began to pay attention to the energy efficiency management of the enterprise and found the methods to improve the energy efficiency constantly. As the terminal consumer of energy resources, the improvement of enterprise energy efficiency is significant for the sustainable development of national energy resources. In order to raise its energy efficiency level, it is important for the enterprise to find the inefficiency of the energy consumption. Here, the scientific and reasonable evaluation of the enterprise energy efficiency is particularly critical. Evaluating the energy efficiency can not only judge the energy efficiency level of the enterprise objectively but also encourage the enterprise to find the improvement way by the scientific and comprehensive evaluation index system which can indicate the direction for improving energy efficiency.
With the continuous consumption of global energy resources, in order to realize the comprehensive evaluation of energy efficiency, some authoritative organizations put forward the index system of the energy efficiency: EU energy efficiency index system (Bosseboeuf et al., 1997; Sun and Meristo, 1999), British energy industry index system, WEC energy efficiency index system, and IAEA energy efficiency index system (OECD 2003). China National Bureau of statistics also proposed 12 types of the framework of energy statistics index system, domestic standards, and general rules for our country. These indexes were mostly evaluating the energy efficiency and sustainable development capacity from the national level which can provide a reference for the enterprise to carry on the energy efficiency evaluation to a certain extent. However, the energy efficiency evaluation index system from the micro level of the enterprise was not proposed.
To further study the enterprise energy efficiency level, more and more scholars took the enterprise as the research object to carry on the energy efficiency analysis. To provide theoretical support for the enterprise to improve energy efficiency, scholars tried to establish the evaluation index system and selected a reasonable evaluation method to evaluate the level of enterprise energy efficiency. Up to now, lots of the research on the energy efficiency evaluation index system has been published. Liu (2007) established the evaluation system from five aspects: mechanism, technology, management, structure, and quality, which provided reference for enterprise energy efficiency evaluation. Chen et al. (2008) and Lin (2002) mainly focused on the research of energy efficiency index system at the enterprise level. Sardian (2008) found the cost and manpower were the important means to improve the enterprise energy efficiency level through the evaluation of the Greek enterprise energy efficiency. Because of the lack of the evaluation system of the railway transportation enterprise, Zhang (2012) established a set of the comprehensive energy consumption evaluation system. The index system included a total target, 3 first level indexes and 13 secondary indexes. And then, taking Railway Bureau of Harbin as an example, the comprehensive energy consumption was evaluated by AHP and gray correlation analysis method. The results showed that the energy consumption index system was in line with the actual situation of the railway transport enterprise and it could be applied for most railway transport enterprises. In order to research the energy efficiency level of the Malaysia Petrochemical Enterprise, Samuel et al. (2013) set an evaluation index system of the energy efficiency. Through the evaluation of environment, economy, society, product liability, and other indexes, the lack of the energy efficiency of petrochemical enterprises and some reliable recommendations for the sustainable development of enterprise were proposed. Starting from the state of the oil drilling system, the machine productions system, the water injection system, and the energy efficiency of the gathering and transportation system, according to the principle of evaluation index system, Yuan (2013) constructed the evaluation index system of the energy efficiency in China’s petroleum enterprises and provided reasonable suggestions for improving the energy efficiency level. Ergu et al. (2013) and E. BAL Besikci et al. (2016) studied on the improvement of the energy efficiency of shipping enterprises. The former combined the tangible and intangible factors to establish the evaluation index system of the ship operation energy efficiency. By analyzing the relationship between the various indexes, the influence degree of the energy efficiency of the ship caused by each factor was assessed. The latter proposed that the indexes of voyage management, new equipment, and ship management were the main indexes affecting the energy efficiency of shipping company. An energy efficiency evaluation index system was built which was based on these influencing factors and using the fuzzy comprehensive evaluation method to evaluate the energy efficiency. Hao et al. (2015) established a four-level energy efficiency evaluation index system for an enterprise. This system broke through the limitations of product unit consumption index, taking the index system extended to some key process parameters and operating parameters gradually and could provide an effective way for enterprises to carry out a more detailed analysis of the differences and find the key factors of energy efficiency more accurately. Iskin and Daim (2016) combined the energy efficiency evaluation index with the hierarchical decision model to make project decision through the evaluating of the energy efficiency level of the power project. At the same time, his research could provide theoretical support for the selection and development of power projects.
In addition to the evaluation index system, the objective and reasonable evaluation method was also important. Many scholars have carried out this research. Taking industrial enterprises in the East, West, and Central China as an example, Wang et al. (2012) considered the factors of the regional division, economic level, and technical level to carry out the energy efficiency calculation and evaluation of industrial enterprises in different regions by using DEA. The empirical results showed that the energy efficiency level of industrial enterprises in Western China had a great improvement space, which provided the direction for the development of industrial enterprises in the future. Li (2012) used the characteristic value method to screen the index of the power energy efficiency of the enterprise and established the evaluation index system of the enterprise power energy efficiency. Then, the model of fuzzy comprehensive evaluation for power energy efficiency of the enterprise was established based on this index system, as well as evaluated the power energy efficiency level of Port Enterprises. Nouri et al. (2013) carried on energy efficiency calculation and evaluation analysis for plant oil manufacturing enterprises by using DEA. The results indicated that the enterprise was insufficient in the energy efficiency and some feasible measures for its energy efficiency optimization were proposed. Based on the evaluation index system of energy efficiency in port, Yang (2010) deeply analyzed the factors which affected the energy efficiency of the port. He made a reasonable evaluation of the energy consumption level of Wuhan port by combining matter element analysis and AHP, so as to prove the evaluation system and evaluation model were scientific and practical. Ge (2014) used Monte Carlo method to study the energy consumption of the fiber board manufacturing process by building energy consumption evaluation model of the fiber board manufacturing process and evaluating the energy consumption in the manufacturing process of the fiber board. Through the analysis of the actual production process, the accuracy and reliability of the model were verified, and the corresponding energy saving measures were put forward. According to the characteristics of machine tool manufacturing system, Mao and Wang (2016) evaluated the energy efficiency of the system from four aspects: economy, product, equipment, and task flow, and provided an important basis for improving the energy efficiency using rough set—AHM combination method, gray correlation method, and fuzzy evaluation method.
From previous research, it is not difficult to find that although most scholars focus on the construction of energy efficiency evaluation index system and the selection of evaluation methods, however, the proposed index systems cover a few dimensions and the research methods are more traditional, mostly adopting DEA, fuzzy comprehensive evaluation, and AHP. Actually, the level of energy efficiency of an enterprise not only depends on the impact of one or several aspects, the evaluation results are also affected by different methods. Therefore, in order to take more various factors into consideration, an multi-dimensional evaluation index system based on DPSR (Driving forces-Pressure-State-Response) of enterprise is established which is on the basis of defining the scope of enterprise energy efficiency measurement. This index system can reveal the energy efficiency and the change law of the enterprise objectively and provide systematic and scientific theoretical support on effective energy management for enterprises, and then the cloud model is used to verify the model. Finally, the feasibility and scientificity of the index system and the evaluation method are confirmed.
Complexity analysis of the enterprise energy efficiency management
The research on enterprises’ energy efficiency is a comprehensive and systematic work which needs to consider the effect of various factors as much as possible. Enterprise energy efficiency system is a combination of some subsystems, which is different in scale and function. It is a complex open system which is composed of intelligent agents (person) and has the characteristics of complex structure, complex relationship, complex behavior, and complex environment. By defining the boundary of enterprise energy system, taking the enterprise energy supplier (utilities system) and demander (production system) as a core, focusing on the energy flow, manufacturing process flow, value flow, and emission flow in dynamic networks as well as the related to personnel, technology, and management measures, the energy efficiency factors and the key energy consumption source (system, production line, process, equipment, facilities, etc.) are identified accurately. And then the “input” energy, “useful” energy or system output, and “loss” (energy, product, or service) are determined through each energy source.
Enterprise energy efficiency can be measured by the ratio of input energy to the energy, product, service, or performance, which can be output from each key energy consumption source. The improvement of energy efficiency is the means to achieve the maximum economic output and bring the smaller environmental impact under certain energy consumption amount. Therefore, establishing a set of multi-dimensional index system, which is based on considering the important degree of each index which fully reflects the energy efficiency of enterprises, the rationality of the index system and the logical relations between the indexes have the very vital significance for enterprise that truly reflects its energy efficiency.
Construction of evaluation index system based on DPSR
Model framework based on DPSR
In 1993, OECD proposed the DPSIR model through the revision of the PSR model and the DSR model. The PSR model was proposed by Canadian scholars David J. Rapport and Tony Friend (Kohl et al. 2014), which the basic idea is that human activities exert pressure on the environment and resources which can change the environment quality and the quantity of resources, then the society responds to these changes by policies or measures to alleviate the pressure in order to achieve sustainable development. The model uses the “reason-state-response” to represent the causal and intercoordinated control relationships among indexes. From the generation mechanism, the index system is constructed in the answer to why, what happened, and how to deal with it, but it still has some situations that cannot be solved when analyzing the social problems. Therefore, based on the PSR model, the UNCSD (United Nations Commission on Sustainable Development) proposed the DSR model to solve this problem. In this model, the driving force indexes present all kinds of factors that cause the problem of social development, state indexes represent the realistic state of the system in the process of social development, response indexes are expressed as a variety of countermeasures and measures which are taken to promote the development of human society. Based on PSR model, the “pressure” generated by the “driving forces” and the “impact” factor produced by the system “state” are added to the DPSIR model, which is not only change the meaning of the original factors but also further expand the application of the theoretical model. The DPSIR model emphasizes the relationship between economic operation and environment, reveals the causality between environment and economy, and also integrates resources, development, environment, and human health problems effectively.
At present, the DPSIR model has been widely used in the study of complex environmental systems because of its advantages such as comprehensiveness and flexibility. It can effectively integrate the influencing factors, energy efficiency status, and countermeasures to reflect the causal link of the system. The above ideas are considered suitable for the enterprise energy efficiency research in this paper. However, the conceptual framework of the DPSIR model is needed to make a concrete analysis of the practical problems in this research. In the study of enterprise energy efficiency analysis, intrinsic correlation among the dimension of “impact,” “state,” and “response” is so strong that boundaries among them are not obviously. At the same time, the “state” dimension index can reflect the current status of enterprise energy efficiency, clearly present the existence of the problem, and can also point out the direction of the improvement for the “response” level. In addition, compared with the “impact” dimension, “response” dimension can improve the enterprise energy efficiency more effectively. Therefore, the “response” dimension is selected as the index in this paper and the “impact” dimension is ignored.
Enterprise energy efficiency index system based on DPSR
In the index system based on the DPSR model, “driving forces” is the basic power for the change of enterprise energy efficiency. Among them, the external driving forces include policy, technology, market, society, and natural factors. The internal driving forces refer to the internal factors of the enterprise (including the environmental enthusiasm of the decision maker, enterprise scale, foreign direct investment, R&D investment and human capital). After the driving forces effect, “pressure” is the direct cause of the development and the change of the energy efficiency of the enterprise which is directly applied to the energy system, including energy infrastructure management, energy, product manufacturing, enterprise technology, equipment, personnel, and financial performance factors. “State” is the real situation of the enterprise energy efficiency. In this paper, it is described by seven kinds of indexes including the enterprise overall energy efficiency, real enterprise energy efficiency, enterprise energy physical efficiency, enterprise energy valuable efficiency, enterprise energy technology efficiency, enterprise energy environmental efficiency, and enterprise energy management efficiency. “Response” means the energy saving way and concrete measures taken by enterprise, such as structure energy-saving, technology energy-saving, and management energy-saving.
According to the abovementioned statement, the index system based on the DPSR model must have the following functions: (1) It should be able to describe and characterize the current situation of energy efficiency in a certain period of time. (2) It should be able to show the changes in the trend of a certain period. (3) It can reflect the causal relationship between the four criteria layers dynamically in the model.
Function relationship of Driving forces (D)-Pressure (P) and Pressure (P)-State (S).
Function relationship between state (S) and response (R).
Function relationship of Response (R)-Pressure (P) and Response (R)-Driving forces (D)。
The method of ANP
The ultimate goal of research on energy efficiency of the enterprise is to provide a feasible way for the enterprise to improve energy efficiency. Therefore, as the basis for making the enterprise energy efficiency management decision scientifically, the evaluation of enterprise energy efficiency is becoming particularly important. Because the impact degree of different indexes for energy efficiency evaluation is different, the weight of each index should be given according to the degree of the importance to make a reasonable evaluation of the enterprise energy efficiency. The logic relation of the DPSR model is shown in the dynamic network relationship between the hierarchy and the hierarchy. The internal indexes of each level influence each other, so the method of assigning the weight should take the internal relationship of each index into consideration. Based on these, ANP is selected to assign the weights for each index in the paper which can reflect the complex network characteristics of the constructed index system well.
Weights assignment for enterprise energy efficiency evaluation index
Combined with the enterprise energy efficiency index system based on DPSR and the basic principle of ANP, the weight of each index is calculated. Because the enterprise energy efficiency index system based on DPSR has so many indexes that the process of calculation is complex, the Super Decisions software will be used to calculate the weights in the paper.
Secondly, to validate the confidence of data from the experts, consistency index is checked by using Super Decisions software. When consistency test value of the judgment matrix is less than 0.1, it means the judgment matrix effective (Fig. 11). The pairwise matrices and consistency check results of other secondary index can be found in the Appendix.
Weights of all level index
The first level index /weight
Secondary level index /weight
The first level index /weight
Secondary level index /weight
Comprehensive evaluation method of enterprise energy efficiency
The cloud model
The cloud model was proposed by Li, an Academician of Chinese Academy of Engineering; it is a kind of uncertainty transformation model which is used to deal with the qualitative concept and quantitative description. Because the index system established in this paper contains some qualitative indexes which are difficult to quantify (such as energy management responsibilities, energy-saving regulations, and energy-saving monitoring and correction). Taking into account the multi-dimensional, fuzzy, and random characteristics of the index system, this paper selects the cloud model for enterprise energy efficiency evaluation. At present, the cloud model has been widely used by scholars in prediction, comprehensive evaluation, and the field of different risk assessment (Xu and Wang 2016; Pang and Liu 2016; Wang et al. 2016). It has strong applicability and reliability to different evaluation category.
Calculation steps of cloud model
- Step 1:
Analyzing and determining each factor which is the impact of the evaluation objectives and forming the set of individual factors.
- Step 2:
Assigning the weights through qualitative or quantitative methods according to the influence degree of individual factors to the overall target.
- Step 3:
Determining the evaluation set, i.e., the set of evaluation levels.
- Step 4:
Processing the actual collected data by using backward cloud generator, obtaining the cloud model of each factor and the numerical characteristics in the target evaluation model.
- Step 5:
Calculating the cloud model’s numerical characteristics of each factor by using the integrated cloud algorithm in virtual cloud, getting the comprehensive assessment cloud by adding the evaluation cloud of each factor and the weights together, so as to determine the evaluation level.
Modeling the evaluation level cloud. In this paper, the evaluation level is divided into five levels: high, moderate, low, lower, and very low. The evaluation standard cloud model can be obtained based on bilateral constraints, which k = 0.5: high (9, 1/3, 0.5), moderate (7, 1/3, 0.5), low (5, 1/3, 0.5), lower (3, 1/3, 0.5), very low (1, 1/3, 0.5).
- 2.According to the relevant data of BL Company, combined weight value, using MATLAB program to calculate the parameters of the cloud model of evaluation indexes at all levels, is shown in Tables 2 and 3.Table 2
Secondary index parameters of cloud model
Parameters of cloud model
Parameters of cloud model
First level index parameters of cloud model
The first level index
Parameters of cloud model
Combined the weights and first level index parameters of cloud model, carried out the operation of the integrated cloud to obtain the cloud model digital features (7.425,0.804,0.261). And then the comprehensive evaluation value of cloud model is obtained by using MATLAB, as shown in Fig. 15.
As shown in the comprehensive cloud image, during the digital characteristics of the cloud model for the BL company’s energy efficiency evaluation, the Ex (expected value) is 7.425, overall reached a “moderate,” close to the “high” level. However, because of its En (entropy) is too large, making the overall distribution of cloud droplet is more dispersed and some cloud droplet in the range of the “low,” so that the overall level has some distance with the “high” level. It is not difficult to see that the evaluation index of R1 is lower in the parameters of the secondary indexes in the cloud model. This is mainly because the way of the enterprise to increase the energy efficiency is by improving technology. So, the structure of energy-saving level has been ignored and the response ability of structure energy-saving should be strengthened.
In addition, because of the rising of market demand of the company, the good enterprise operation environment and the gradual improvement of the energy saving standards prompted the evaluation index of D3, P7, and S6 was significantly higher than other indexes. It is obvious that DPSR four aspects of the indexes in the evaluation are distributed uniformly, but the index of R still needs to be further improved so as to improve enterprise efficiency. This is mainly due to the index of R as a response index, which can generate feedback effects on the driving force, pressure, and state. The index of R is the most intuitive means to improve the energy efficiency of enterprise. The above results also confirm that the proposed enterprise energy efficiency evaluation index system, which is based on “DPSR”, is feasible. Combined with common evaluation methods can evaluate the energy efficiency level of enterprises accurately and provide direction for the enterprise to carry out energy efficiency management.
Establishing evaluation index system for enterprise energy efficiency is the theoretical basis to strengthen enterprise energy-saving management; to improve the energy use efficiency, economic efficiency and competitiveness of enterprises; and to fully exploit enterprise energy-saving potential. And it is also the foundation of the responsibility evaluation and assessment system of the enterprises to implement energy-saving and emission reduction targets. Based on the domestic and international energy efficiency index system, the index system constructed in this paper is proposed which is combined with the general principles and standards of China’s energy consumption and the practice of enterprise energy management. The index system includes four first level indexes and 22 secondary indexes, and the key factors of enterprise energy efficiency and the relative importance of energy saving ways are comprehensively measured by the network analytic hierarchy process. Finally, a cloud model is used to evaluate the level of energy efficiency of BL company; the results show that the index system and the evaluation method are feasible, which provides a research framework and a very good reference value for enterprise to implement energy management.
- Chen YW, Mu J, Ma JG, Liu B, Yu JQ (2008) Study on the energy efficiency indicator system and evaluation model in the oil-gas fields. Petroleum Planning & Engineering 3:147–148Google Scholar
- Ergu D, Kou G, Peng Y, Shi Y, Shi Y (2013) The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J Supercomput 2013:643Google Scholar
- Ge B (2014) Research on evaluation and model establishment of the energy consumption in the process of fiberboard manufacturing. Dissertation, Northeast Forestry UniversityGoogle Scholar
- Hao HB, Lu YL, Song XL (2015) Construction of energy efficiency evaluation index system of a mechanical enterprise. Energy Conservation 2:16–18Google Scholar
- Jharkharia S, Shankar R (2007) Selection of logistics service provider: ananalytic network process (ANP) approach. Int J Manag Sci 35:274–289Google Scholar
- Liu ZF (2007) Study on establishing evaluation index system of energy utilization efficiency. Energy and Environment 2007:2–4Google Scholar
- Lin J (2002) Research on energy efficiency evaluation system of paper making enterprises. Dissertation, Guangxi UniversityGoogle Scholar
- Li SL (2012) Energy efficiency evaluation and energy saving analysis of enterprises. Dissertation, Yanshan UniversityGoogle Scholar
- Mao ZH, Wang Y (2016) Energy efficiency evaluation method of machine tool product manufacturing system based on grey fuzzy algorithm. Computer Systems & Applications 7:1–7Google Scholar
- OECD (2003) Organisation for economic co-operation and development (OECD) core set of indicators for environmental performance reviews. France, ParisGoogle Scholar
- Pang HM, Liu ZH (2016) Study on food safety surveillance and risk assessment based on cloud model. J Food Sci Technol 3:90–94Google Scholar
- Saaty TL (1996) Decision making with dependence and feedback: the analytic network process, 1st edn. RWS Publications, PittsburghGoogle Scholar
- WEC (2006) Wec statement 2006: energy efficiencies: pipe-dream or reality? Presented at the world energy council (WEC). UK, LondonGoogle Scholar
- Wang HZ, Liu K, Zhu HY (2016) Load forecasting of power system based on cloud model and support vector machine with RBF. Journal of Lanzhou University of Technology 4:1–4Google Scholar
- Xu YX, Wang HY (2016) Evaluation of airport service quality cloud model based on AHP-entropy weight method. Value Engineering 2:7–10Google Scholar
- Yuan J (2013) Study on our oil companies of energy efficiency benchmarking system and energy efficiency evaluation. Dissertation, Northeast Petroleum UniversityGoogle Scholar
- Yang Y (2010) Study on evaluation for energy conservation and emission. Dissertation, Wuhan University of TechnologyGoogle Scholar
- Zhang R (2012) Study on monitoring and evaluation method of energy consumption index of railway transportation enterprises. Dissertation, Southwest Jiaotong UniversityGoogle Scholar