Research on energy conservation and emissions reduction based on AHP-fuzzy synthetic evaluation model: A case study of tobacco enterprises

Accepted Manuscript Research on energy conservation and emissions reduction based on AHP-fuzzy synthetic evaluation model: A case study of tobacco enterprises Qian Wang, Rong Han, Qilu Huang, Jun Hao, Nan Lv, Tianyang Li, Baojun Tang PII:

S0959-6526(18)32259-5

DOI:

10.1016/j.jclepro.2018.07.270

Reference:

JCLP 13724

To appear in:

Journal of Cleaner Production

Received Date: 27 April 2018 Revised Date:

11 July 2018

Accepted Date: 27 July 2018

Please cite this article as: Wang Q, Han R, Huang Q, Hao J, Lv N, Li T, Tang B, Research on energy conservation and emissions reduction based on AHP-fuzzy synthetic evaluation model: A case study of tobacco enterprises, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.07.270. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Research on energy conservation and emissions

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reduction based on AHP-fuzzy synthetic evaluation

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model: A case study of tobacco enterprises

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Qian Wanga,b,c, Rong Hana,b,c,d, Qilu Huange, Jun Haof, Nan lvf, Tianyang Lif, Baojun Tanga,b,c,d,*

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Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Bejing, 100081, China b School of Management and Economics, Beijing Institute of Technology, Bejing, 100081, China c Beijing Key Lab of Energy Economics and Environmental Management, Bejing, 100081, China d Collaborative Innovation Center of Electric Vehicles in Beijing, Bejing, China e Zhengzhou Tobacco research Institute of China National Tobacco Corporation, Zhengzhou, 450001, China f Green Building Design and Research Institute of China Building Design Consultants Co. LTD., Beijing, 100120, China

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Highlights

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This paper evaluate the effect of tobacco industry’s technical measures for energy conservation and emissions reduction, which have not been studied widely by previous research. A comprehensive index combining the quantitative and qualitative indicators is constructed, meanwhile, the AHP and Delphi expert consultation method are applied. Thus, the application effects of various energy saving technologies can be evaluated comprehensively and objectively. The selection of advanced and applicable energy saving and emission reduction technologies by enterprises provides the basis for scientific decision-making.

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*Corresponding author at: School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China. Tel: +86 10 68918013. Emai address: [email protected] (Bao-Jun Tang)

ACCEPTED MANUSCRIPT Abstract: How to evaluate the effect of tobacco industry’s technical measures for energy conservation and emissions reduction is important to promote the application of advanced technology. Motived by this purpose, this study applies the fuzzy comprehensive evaluation method to construct a comprehensive index and use the Analytic Hierarchy Process (AHP) and Delphi expert consultation method to determine weights of indicators. Furthermore, the comprehensive evaluation of nine core energy-saving technical measures of tobacco industry is made. The results show that, the comprehensive performance of refrigeration system’s optimized technique energy efficiency index is the best, which is 0.73, followed by air conditioning system and lighting system, which are 0.62 and 0.60, respectively. These results may provide insightful support for decision makers to promote the energy conservation and emissions reduction of tobacco industry.

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Key words: Analytic Hierarchy Process (AHP); Fuzzy comprehensive evaluation; Tobacco industry; Energy saving technology

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1. Introduction

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The energy consumption of industry accounts for more than 50% of the whole world energy consumption (US Department of Energy, 2016). Therefore, the Chinese government sets the goal for the industrial sector that by 2020 the energy consumption and CO2 emissions will be reduced by more than 18% and 22% compared with the 2015 level (State Council, 2017). Moreover, in Paris conference, Chinese government pledge to reduce carbon dioxide emissions per unit of GDP by 60% to 65% from the 2005 level through 2030 and to achieve the peaking of CO2 emissions around 2030 (NDRC, 2015). In order to respond to the requirements of national reduction targets, several energy saving technologies have been adopted in the tobacco industry. During the 12th five-year period, the energy consumption per unit industrial added value and per 10 thousand cigarettes of the tobacco industry are 18.4 and 2.95 kilogram of coal equivalent (kgce), and which are reduced by 49.7% and 18.9%, respectively (STMA, 2016). The tobacco industry has exceeded the energy conservation and emission reduction targets issued by China central government and has made positive contributions to the construction of a resource-conserving and environment-friendly industry. The Chinese government sets the target that by 2020 the average energy consumption per industrial added value in tobacco industry should reduce 15% and the CO2 intensity should decrease 18%. Generally speaking, the energy consumption of tobacco enterprise is characterized by large structural difference and large dynamic load function. Energy saving and

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To promote the concept of green building in the field of industry, in 2011, the state tobacco monopoly administration has issued the "evaluation standard for green industrial building of tobacco industry, ESGIB-TI" (STMA, 2011). Since then, nearly 20 cigarette manufacturers have implemented the green industrial building projects, 11of them have been put into operation. There are more than 20 energy saving and

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emission reduction technologies used in the green projects. However, obvious difference exists among energy saving technology measures in the actual effect, economic benefit and environmental impact. Current research is mainly aimed at green building energy saving equipment (including air conditioning, heating,

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ventilation, refrigeration equipment) of energy analysis (Wang et al., 2009), technology improvement (Ji et al., 2009; Kang et al., 2010) and energy saving potential (Yu et al., 2009; Ding et al., 2013). Nevertheless, few of them focus on the comprehensive evaluation of green building technologies for energy conservation and emissions reduction. Considering technical and economic factors as well as management and operation, maintenance synthetically to evaluate the industrial enterprises technical measures for energy conservation and emissions reduction is crucial for tobacco enterprise to select and apply energy saving technology. The main research objectives of this paper are:

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emission reduction target can be achieved through management and energy-saving technologies. At present, Chinese economy has been recovering slowly from the global financial crisis, but it cannot achieve the same rapid development of the pre-recession period. Instead, the country has entered a new phase of economic development-a “new normal” (Mi et al., 2017). China is striving to promote economic growth while driving down CO2 emissions by change economic structures, improving efficiency levels, and cleaning up energy mix. Based on the background, making a comprehensive analysis on green industrial building energy saving technical measures of tobacco industry can help enterprises promote the application of advanced technology and achieve the national energy conservation and emissions reduction targets.

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(1) Establish a comprehensive evaluation index system for energy saving and emission reduction technology of tobacco enterprises;

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(2) Construct a fuzzy comprehensive evaluation model based on AHP method;

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(3) Analyze the advantages and disadvantages of various energy saving technology through the evaluation results from the AHP-Fuzzy model of a typical Chinese tobacco enterprise and put forward the policy recommendation for the energy saving technology development plan of industrial enterprises.

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(1) This paper evaluate the effect of tobacco industry’s technical measures for energy conservation and emissions reduction, which have not been studied widely by previous research;

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Compared with previous studies, this work goes beyond several aspects:

(2) A comprehensive index combining the quantitative and qualitative indicators is constructed, meanwhile, the AHP and Delphi expert consultation method are applied. Thus, the application effects of various energy saving technologies can be evaluated comprehensively and objectively;

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The remainder of the article is organized as follows. Section 2 reviews relevant literature on the evaluation mechanism and methods of energy conservation technology. Section 3 introduces data definitions and provides methodologies of establishing comprehensive evaluation index and model. Section 4 describe the collection and processing of data from nine energy saving technology from a typical tobacco enterprises. The conclusions and policy implications are drawn in Section 5.

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2. Literature Review

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(3) The selection of advanced and applicable energy saving and emission reduction technologies by enterprises provides the basis for scientific decision-making.

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A wealth of literature has raised the discussion about the comprehensive evaluation

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of energy conservation technology. In the aspect of index system, research on energy conservation and emissions reduction technology evaluation in the thermal power industry is a hot topic. The European Union develop a set of index system to evaluate the "best feasible technology" based on the life cycle assessment method and

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multi-objective decision method (European Union, 1996). China energy research association combines the basic situation of China with four indices of environmental characteristics, energy saving rate, maturity and economy to comprehensively evaluate clean coal power generation technology (Institute of Scientific and Technical Information of China, 2008). Many researchers have established the evaluation index system of clean coal technology including technical, economic, environmental and social indicators from the perspective of energy-environment and economic analysis (Tsinghua University, 2009; Qiu et al., 2006; Xu et al., 2014). In addition, researchers have carried out comprehensive evaluation of energy conservation technology in the fields of power grid (Xue et al., 2015; Guo et al., 2015), ethylene (Zhang and Qiu, 2012) and electric vehicle (Li et al., 2013). Study of building energy efficiency 4 / 25

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In terms of energy saving technology evaluation methods, researchers under the framework of the United Nations Intergovernmental Panel on Climate Change (IPCC), has carried out extensive research about the assessment methodology of pollution prevention and control of best available technology (BAT). The life cycle assessment method is widely used in the evaluation process of BAT technology (Derden et al., 2002; Dijkmans, 2000; Geldermann and Rentz, 2004; Georgopoulou et al., 2008). There are also many other energy efficiency technologies, e.g., Process Integration, Mathematical Programming, etc., which can be used for energy efficiency studies from perspectives of process and engineering technology (Feng and Wang, 2017). However, due to the relatively complicated process and high data requirements, there are still some obstacles to its promotion and application. Multiple attribute comprehensive evaluation method can make multi-dimensional complex system

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evaluation index into one dimensional evaluation index system, which is integrated by qualitative analysis and quantitative calculation of comprehensive evaluation method. The process fully reflect the main characteristics of the evaluation object information and reflect the main value judgment (Jin et al., 2006). Due to the relatively simple and

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operable of calculating process, the multiple attribute comprehensive evaluation method is widely used in the evaluation of clean coal technology (Wu and Chen, 2002), coal pollution control technology (Xiao, 2007), water conservancy project safety (Jin et al., 2008), ecological environment system (Zhou, 2000; Bi and Hong, 2001) and energy investment projects (Tian, 2005; Zhang and Wu, 2011).

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technology, however, has focused on the influential factors (Sawhney et al., 2002; Junnilas, 2007; Todds, 1997), energy efficiency (Zhou, 2017; Xiao et al., 2012) and economic benefit (Jin et al., 2015; Han et al., 2008; Li, 2015). Few of them focus on the comprehensive evaluation of building’s energy conservation technology.

Research on energy saving of tobacco enterprises has mainly been carried out from the perspective of energy consumption analysis, energy saving characteristics of process equipment and the application effect of energy saving measures in green industrial building. Hong et al. (2009) and Chen (2014) use the input-output model to analyze energy consumption for tobacco enterprises. Then, the energy consumption coefficient and the comprehensive energy consumption index of energy and non-energy products in the process of tobacco manufacturing have been obtained. Wang et al. (2018) establish a comprehensive energy consumption analysis model for tobacco production by using E-p analysis method, and analyze the influence of section energy intensity and production ratio on the energy consumption of tobacco production. Liu et al. (2013) and Jin (2013) research on the effect of tobacco primary processing line key equipment and energy saving technology. Li et al. (2009) conduct

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This study conduct a comprehensive evaluation of energy saving technical measures for tobacco industry. Firstly, we screen out high correlation index and build comprehensive evaluation index system of energy saving technology, which is based on the Analytic Hierarchy Process (AHP) combined with experts’ authority level to determine the index weight. Then the qualitative and quantitative indicators are classified. Finally, the fuzzy hierarchy comprehensive evaluation model is established to evaluate the superiority of energy saving technology of tobacco enterprise. The technology roadmap is shown as follows:

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a preliminary study on energy conservation and energy utilization in the evaluation standards of green industrial building. Weng et al. (2014) and Sun et al. (2011) study the effect of energy saving technology and waste heat resource recycling in factory air-conditioning system. Most of these studies focus on analysis and evaluation of the present situation of energy consumption and energy saving effect of a single device or single technology. Few of them has paid close attention to the comprehensive evaluation of energy saving technology for tobacco enterprise.

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[Fig.1. inserted here]

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3. Methodology

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3.1 Comprehensive index construction

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3.1.1

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Based on the enterprise investigation and expert consultation, combining of quantitative and qualitative index, we select technical performance, economic benefits, operation management and environmental impact as proxies for the first-level indicators. After that, the 17 secondary-level indicators such as comprehensive energy saving rate are selected. The comprehensive evaluation indicator system of tobacco

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Primary selection of evaluation index

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enterprises’ energy conservation and emission reduction technology are shown in table 1.

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[Table 1. inserted here]

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3.1.2

Correlation analysis of evaluation index

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Correlation analysis is used to quantify the association between two continuous variables. In correlation analysis, we estimate a sample correlation coefficient to 6 / 25

ACCEPTED MANUSCRIPT determine the correlation degree and direction between variables. In this study, we establish the comprehensive evaluation index system of energy saving technology. Since qualitative indicators cannot be as accurate as quantitative index, thus they are not suitable to analyze data characteristics. Therefore, we only apply correlation analysis to quantitative indicators.

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The Pearson correlation coefficient analysis is adopted in this study to measure the degree of correlation between evaluation indicators. The indicators are regarded as random variable and the calculation steps are shown in supplement material A.

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3.2 Fuzzy comprehensive evaluation model based on AHP

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In the discipline of ergonomics, there is a good understanding of the role of fuzzy set theory in showing a quantifiable degree of uncertainty in human judgement. The fuzzy evaluation method is based on fuzzy set theory developed by Zadeh (1965) for capturing the uncertainties inherent in a system. The universal method involve combining the AHP and fuzzy evaluation methods for synthesizing performance data and subjective response data. The following steps can describe the formal procedures of the general fuzzy evaluation model (see Supplementary material B).

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4. Empirical results and discussion

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During the 11th Five-Year Plan and 12th Five-Year Plan periods, tobacco industry is intensively implementing the overall technological transformation. According to incomplete statistics, the area of joint workshops for tobacco factories after technological transformation has increased by an average of 120% (the maximum

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increase has reached 250%), and the actual output has only increased by an average of less than 20% before the technical reform. Meanwhile, during the 13th Five-Year Plan period, tobacco industry must complete the goal of reducing energy consumption per unit of industrial value-added by 15%, reducing the combined energy consumption of 10,000 cigarettes by 5%, and reducing the CO2 emissions per unit of industrial value-added by 18%. Under the urgent situation of energy conservation and emission reduction, the systematic planning of the application of various energy saving technical measures in the technological transformation projects of cigarette factories is of great significance for the successful completion of the energy conservation and emission reduction targets during the 13th Five-Year Plan period. Based on this background, we apply the evaluation model established in the previous section to evaluate the energy saving technologies of a tobacco industry, analyze the advantages, disadvantages and applicability of each technology in combination with the evaluation results that can provide decision-making reference for the selection and promotion of

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energy saving technologies in the tobacco industry. The technology innovation project of a tobacco enterprise has a target production

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capacity of 50 billion cigarettes/year, and the combined construction area has increased from 52,000 square meters before the technical innovation to 179,000 square meters. According to the industry's energy saving and emission reduction target, the energy saving rate of the comprehensive energy consumption must be 18.9% after the technical transformation of the plant. Presuming that, after technical reform, the technological energy consumption per unit output remains unchanged. To achieve the goal of energy conservation and emission reduction, the energy saving rate of the building energy system must reach 30%. The project is built in accordance with the requirements of the three-star green building project and is now officially put into operation. More than ten energy saving technology measures adopted and the expected energy saving goals have been achieved. This study intends to carry out comprehensive evaluation and analysis of the nine energy saving technology measures adopted by the tobacco enterprise, and selects advanced and mature energy saving technologies to provide scientific basis for the promotion and use of the

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technology. Combining the features of energy saving technical measures and the availability of data, quantitative indicators are obtained through simulation calculations, on-site investigations, and previous literature. This study uses eQuest energy dynamic simulation software to simulate the building energy consumption of

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4.1 Construction of comprehensive evaluation index system of

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energy saving technology in tobacco enterprises

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the industrial building of tobacco enterprise. The acquisition of qualitative indicators and the determination of weights are mainly based on experts scoring.

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After the correlation analysis, the combination of indicators with a correlation coefficient greater than 0.8 is selected one by one for judgment analysis and whether or not the indicators are substantially related are analyzed. After the indicators that could replace each other are screened, 17 indicators are adjusted to 12. The comprehensive evaluation of energy saving technologies for tobacco enterprises is set as the target level, with four first level indicators as the standard level and 12 second level indicators as the indicator level. Table 3 shows the comprehensive evaluation index system for energy saving technologies.

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4.2 Evaluation index weight determination

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The expert opinion would be influenced by their work and personal preferences. In order to comprehensively collect opinions from various experts, we choose 25 experts who are from energy saving technology consultation, supply, design and application (shown in Table 2), mainly working in top 10 related companies. 21 valid questionnaires are recovered.

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[Table 2. inserted here]

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For the evaluation index system, based on the weight calculation method described above, the weight of each indicator calculated based on the expert consultation results is checked by the consistency test (shown in Table 3).

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[Table 3. inserted here]

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4.3 Quantitative index calculation method

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4.3.1

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(1) U1: Comprehensive energy efficiency (%):

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Refers to the capacity of an energy saving technology, the ratio of the energy consumption saved in a certain energy saving technology year to the total annual energy consumption of a tobacco enterprise. Both units are converted to standard coal. The formula is:

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ESR =

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es Etotal

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Technical performance of quantitative indicators

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In Eq. (1),
 is the comprehensive energy saving rate,  is the annual energy saving rate of a certain energy saving technology, and
 is the total annual energy consumption of tobacco enterprises.

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(2) U12: Energy conversion efficiency:

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Refers to the ratio of available energy to its input energy output by energy-using equipment. It is used here to measure the ratio of annual energy savings generated by the application of certain energy saving technologies to the annual total energy consumption of the energy-using systems involved in the technology. The formula is:

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ECE =

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(3) U13: Energy saving of per unit product: Refers to whether the application of a certain energy saving technology has the

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potential to reduce energy consumption index of enterprises year by year. It is defined as the annual energy saving generated by increasing the energy saving increments per

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unit of input, which is represented as , and is measured by the derivative of  or the rate of change. The formula is:

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4.3.2

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(1) U21: Energy saving capacity of per ten thousand yuan investment (million / ton of coal equivalent):

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The indicator shows how much energy can be reduced by applying energy saving technology, which refers to the ratio of the initial investment in an energy saving technology and its life-cycle energy savings, and its formula is: II 4 ESI = ES

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Quantitative indicators of environmental impact

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In Eq. (3), represents energy saving of per unit product; ∆ refers to increased investment of technical optimization. In this study, the ratio of energy saving increments and investment increments for optimal operating conditions and optimal operating conditions are used for the calculation.

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In Eq. (4), ESI represents energy saving capacity of per ten thousand yuan

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investment;

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infrastructure investment, equipment investment and installation cost, excluding

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operation and maintenance costs.

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(2) U22: Energy saving capacity of per ten thousand yuan investment (10 thousand yuan):

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is the initial investment of energy saving technology including

Refers to the increase in the efficiency of certain energy saving technologies through the reduction of production costs and energy conservation. Based on the 10 / 25

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concept of net present value of financial indicators, this indicator is expressed as the difference between the value and the initial investment. The formula is: -t

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VAB =∑ (CI - CO)t (1+ic ) - II

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In Eq.(5),  is value-added benefits;  is energy saving costs;  is

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operation and maintenance expenditure;  −   represents the cash flow in 

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year;  is calculation period;  is the discount rate which has been set; the initial

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investment has been set as  = 0; after investment the first year cash flow t = 1. (3) U23: Operation and maintenance cost (10 thousand yuan):

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Refers to the sum of the operating and maintenance costs of a certain energy saving

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technology during its life cycle. Based on the concept of net present value of financial

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indicators, the formula is: n

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4.3.3

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(1) CO2 reduction:

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The reduction of CO2 emissions of a certain technology during the life cycle due to the conservation of various types of energy consumption. According to the CO2

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Environmental impact indicators

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air consumed by tobacco enterprises. The formula is:

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emission factor of greenhouse gas emissions in IPCC (2006), we convert the CO2 emissions of different energy sources such as electricity, steam, fuel and compressed

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CO2 emission = ∑ Ei × CFi × HEi × COFi × i =1

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In Eq. (7),  refers to energy variety;
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consumption, carbon content factor, heat equivalent and carbon oxidation factor,

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respectively;

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4.4 Standardization of evaluation indicators

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4.4.1

Normalization of quantitative index

Based on the method described above, the quantitative index data of the energy

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saving technology of tobacco enterprise is standardized, and the standardized values of the quantitative indicators after processing are shown in Table 4.

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[Table 4. inserted here] 4.4.2

Standardization of qualitative index

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Through experts’ consultation, the membership index of qualitative indicators is

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obtained, and the degree of qualitative indicators’ membership is standardized using Eq. (10) (see Supplementary material B). Standardized values of qualitative indicators

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are shown in Table 5.

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[Table 5. inserted here]

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4.5 Multi-level fuzzy comprehensive evaluation

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According to the relative superiority of quantitative and qualitative indicators in

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Table 4 and 5 and the weight of each evaluation index determined in Table 3, a comprehensive evaluation of the energy saving technologies of tobacco enterprises is

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conducted, and the evaluation sequence is performed from low-level to high-level.

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(1) Evaluation of first-level index

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The value and weight for each secondary index are assessed. The energy efficiency index system is taken as an example. Table 7 and Table 8 show that the relative degree

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of superiority of each evaluation index is:

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0.28  0.05 R1 =  0.23  0.92

0.13 0.10 0.16 1.00

0.59 0.52 0.50 0.92

0.31 0.09 0.16 1.00

0.92 1.00 0.39 0.70

0.62 0.43 0.47 0.86

0.14 0.02 0.06 0.57

1.00 0.28 1.00 0.00

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     

From Table 7 we obtain the weight of each technical performance indicators, which is represented as (&) = 0.49 0.25 0.15 0.11. According to Eq. (5), the technical 12 / 25

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performance evaluation result of each energy saving technology is: E1 = (0.29 0.23 0.60 0.31 0.84 0.58 0.15 0.71 0.06 )

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index.

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(2) Comprehensive evaluation

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Following the same method, the results of other secondary indicators can be assessed. Table 6 and Fig. 2 show the evaluation results of the first-level evaluation

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The evaluation result of the second-level evaluation is used as the index value of

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the first-level index, and the first-level index is evaluated following the same method. The evaluation result of the first-level comprehensive evaluation is:

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B = ( 0.45 0.39 0.62 0.43 0.73 0.60 0.20 0.54 0.15 )

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The comprehensive evaluation results of the first-level evaluation are shown in Table 7 and Fig. 3.

[Table 7. inserted here]

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4.6. Analysis and Discussion of Results (1) In the evaluation index system of energy saving technologies of tobacco

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enterprises constructed by using Analytic Hierarchy Process (AHP) and Delphi expert consultation methods, the technical performance and environmental impact indicators

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of energy saving technologies have the highest weight, and they are the core of energy saving technology evaluation. It can be seen that, facing the increasing pressure of

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energy conservation and emission reduction, the weight of technical performance that embodies energy conservation effects is much higher than other indicators, and the impact on the environment is also highly concerned and exceeds the weight of energy efficiency technology economic indicators. Among the technical performance indicators, the comprehensive energy saving rate, which embodies the current energy saving effect of energy saving technologies, accounts for nearly half of the weight, and is a key indicator that experts are concerned about. Among the environmental impact indicators, the value of CO2 emission reduction targets is 0.66, accounting for 50% of the weight, indicating that the impact of greenhouse gas emissions on the environment is increasingly attracting attention. The indicators that comprehensively 13 / 25

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embody the technical energy saving benefits in economic efficiency indicators also have a weighting factor that exceeds one-half of the cost of energy saving investment. The difficulty in implementing the technology in operational management indicators is a problem that people pay high attention and the weight accounts for about 70%.

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energy saving technologies.

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The construction of evaluation index system for energy saving technologies of tobacco enterprises accurately reflects the current focus of society on the evaluation of

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(2) According to the results of fuzzy comprehensive evaluation, the optimization of

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three major energy consuming systems, such as the refrigerating system, AHU system and lighting system have ranked the top three in the overall evaluation. The value of

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machines, water pumps, and cooling towers. The optimization of the energy efficiency index of refrigeration systems mainly refers to the use of high-efficiency chillers,

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adjustable fresh air ratio, cooling tower direct cooling technology, and efficient frequency conversion pumps. Its comprehensive evaluation ranked first, due to its ranking first in both high performance and environmental impact indicators. The top ranking conditions for the comprehensive evaluation of AHU system and lighting system are similar. However, in practical applications, these energy saving technologies work well for tobacco companies, implying that evaluation results in this

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the refrigerating system, AHU system and lighting system are 0.73, 0.62 and 0.60, respectively. The refrigerating system of tobacco factory mainly includes cold

paper are in line with reality. What’s more, the evaluation methods are scientific and practical. (3) Based on the ranking results of the first-level index, we can see that different types of energy saving technologies perform significantly differently among the four

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first level indicators. The optimization of energy efficiency indexes of AHU systems, waste heat utilization technologies in the plant area, and optimization of energy efficiency indexes in refrigeration systems have significant advantages in the evaluation of technical performance indicators due to good energy-saving effects, and

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leads to the advantages in the comprehensive evaluation results. Since the three technologies, optimization of indoor temperature and humidity standards, indoor illumination standards and energy efficiency optimization of lighting systems are energy saving management measures that are reasonably required for the production of environmental standards. Therefore, there do not exist any cost of initial investment and technical operations management. The advantages are obvious in both economic

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efficiency and operation management. Optimization of energy efficiency index for refrigerating system, AHU systems and boiler systems can effectively improve the

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energy efficiency of major energy-using equipment for tobacco companies, as well as

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5. Conclusions and policy recommendations

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This paper uses AHP and fuzzy comprehensive evaluation methods to construct a comprehensive evaluation index system for energy saving and emission reduction

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reduce pollutant emissions and reduce the impact on the environment. Therefore, such technologies show obvious advantages in environmental impact indicators.

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technologies of tobacco enterprises. Taking the energy saving and emission reduction technologies used by a tobacco enterprises as an example, it has nine mature

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follows.

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5.1 Main conclusions

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(1) The empirical research results show that the three energy-using systems, namely the refrigerating system, AHU system and lighting system, list the top three

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core to the promotion and application of energy saving and emission reduction technologies in the industrial sector. It is mainly due to the good energy saving effect

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rankings in the comprehensive evaluation of energy saving technologies. Therefore, the optimization of the three main energy-using system of industrial buildings is the

of the technology and benefited the reduction of CO2 emissions and other pollutants. Environmental protection has made a positive contribution and it is appropriate to focus on promotion and application. The evaluation results of this model reflect the objective reality of energy saving in industrial buildings, and the differences in the

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selection of advanced applicable technologies for energy saving and emission reduction between industrial building and public or residential buildings.

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455

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technologies with potential for promotion. Comprehensive evaluation and analysis are conducted. The main conclusions of this study and policy recommendations are as

450

(2) At present, industrial enterprises in most parts of China have changed from coal-fired boilers to gas-fired boilers, and CO2 emissions from boiler systems have been greatly reduced. Since the proportion of thermal power in China is approximately 72%, the corresponding CO2 emission reductions for energy saving systems are large. The optimization of energy consumption for cooling systems, air-conditioning systems and lighting systems is electric energy, so the potential for CO2 emission reduction is also relatively large. Although the integrated energy saving rate of boiler system and lighting system index optimization is the same, the optimized CO2 emission reduction of the boiler system is only 26% of the lighting system. Therefore, the comprehensive evaluation model based on energy 15 / 25

ACCEPTED MANUSCRIPT 477 478 479 480 481

environmental impact assessment results shows that the overall ranking of the boiler system index optimization is low. (3) This paper uses eQuest energy dynamics simulation software to simulate the building energy consumption of the manufacturing industrial room of tobacco enterprises, and effectively solves the problem that technical measures such as the optimization of production environment standards and the application of passive

RI PT

476

484

5.2 Policy recommendations

485

(1) In order to reduce the building energy consumption of industrial enterprises effectively, the three major energy-consuming systems should be focused on

488 489 490 491 492 493 494

implementing energy saving technological measures. The state government should also increase the corresponding technical research, development efforts and financial

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487

support to make a further promotion of the energy efficiency of major energy consuming system equipment in the industrial sector. (2) Based on the perspective of cleaner production, the system for the use of electrical energy in the four major energy-using systems of industrial buildings has a large CO2 emission reduction potential and is the key to the promotion and application of energy saving and emission reduction technologies in the future.

495

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486

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energy saving technologies cannot obtain the data of energy saving. What’s more, the technical evaluation results are increased accuracy and credibility.

482

Acknowledgments

497

We gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant Nos. 71573013, 71521002, 71642004), Special Items

499 500

Fund for Cultivation and Development of Beijing Creative Base (Grant No. Z171100002217023), Key Project of Beijing Social Science Foundation Research

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498

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502

Base (Grant No. 15DJA084), National Key R&D Program (Grant No. 2016YFA0602603) and Special Items Fund of Beijing Municipal Commission of

503

Education.

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504 505 506 507 508 509 510 16 / 25

ACCEPTED MANUSCRIPT 511 512

514

Supplementary material A

515

Correlation analysis of evaluation index

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513

(1) A sample of m evaluation objects is selected to obtain the original index values of n primary indexes.

518

(2) The correlation coefficient "/ is calculated according to the original value of indicators: m

520

Rij =

cov(Z i

Zj)

σZ σZ i

=

∑ (Z k =1

m

∑ (Z

j

k =1

ki

− Z i )(Z kj − Z j )

(i, j = 1, 2,..., n)

M AN U

519

SC

516 517

1

m

ki

− Z i ) 2 ∑ (Z kj − Z j ) 2 k =1

521

Where "/ represents the correlation coefficient between two indicators;

522

cov34" , 4/ 6 is covariance, which indicates the degree of synergistic strength between

523

two indicators;

524

indicators; 4;" and 4;/ are the sample < indexed with indicator  and

525

respectively; 4>= and 4>? are the sample mean of indicators  and @ respectively.

527 528 529 530 531 532 533 534 535 536 537

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j

(3) The criteria used to determine the degree of correlation are shown in table a1.

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526

789 78: is the standard deviation referring to data volatility of

[Table a1. inserted here]

(4) To analyze the highly correlated indicator combinations and analyze whether the indicators are of substantial correlation. (5) According to the correlation analysis, we further combine, delete or adjust the relevant indicators.

17 / 25

ACCEPTED MANUSCRIPT 538

Supplementary material B

540

Fuzzy comprehensive evaluation model based on AHP

541

1. Data standardization

RI PT

539

Comprehensive evaluation indexes of energy conservation and emission

543

reduction include quantitative and qualitative indicators. For quantitative

544

indicators, the initial value of quantitative index is obtained according to the

545

actual parameters of technical measures. For qualitative indexes, the expert

546

consultation method is used to determine the indicator, and the expert evaluation

547

set is used as the initial value of the qualitative indicator. Different indicators

548

generally belong to different fields, so before the judgement, it is necessary to

549

eliminate the influence of dimensional and unified measurement to ensure the

550

equivalence of judgment. Data standardization is the critical process of bringing

551

data into a common format that allows for collaborative research.

552

2. Determining the set of evaluation factors

553 554

Evaluation factors can be defined according to the objectives of the product evaluation process. A set of A evaluation factors can be represented as a vector

555

U = {U1 ,U 2 ,...,U s } , where the factors set of U i (i = 1, 2,..., s) is U i = {ui1 , ui 2 ,..., uin } .

556

3. Determining the weight of each evaluation factor

558 559 560 561

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557

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542

The Analytic Hierarchy Process (AHP) is a theory of measurement for dealing with

quantifiable and/or intangible criteria that has found rich applications in decision theory and conflict resolution. It is based on the principle that, to make decisions, experience and knowledge of people is at least as valuable as the data they use. Based on the previous work, in this study, we use the AHP method to construct the judgment

563

matrix according to the importance of the indicator. The weight of indicator is obtained by calculating the maximum eigenvalues of the matrix.

564

(1) Construct a pair-wise comparison matrix

562

18 / 25

ACCEPTED MANUSCRIPT 565 566 567 568

The major advantage of AHP method is that, instead of asking experts to directly give a weight for a particular evaluation factor, experts will be asked to rate the relative importance of the different factors. Saaty (1980) proposes a linear scale for assigning comparison values to "/ , and has been widely used. Table b1 presents

571

where  is the size of the matrix.

[Table b1. inserted here]

572

RI PT

570

Saaty’s scale of preferences in the pair-wise comparison process. Usually, the numbers of judgments needed in upper-right triangle of the matrix are  − 1/2,

569

(2) Calculate the maximum eigenvalue and corresponding characteristics of the judgement matrix

575

In this study, the Average of Normalized Columns (ANC) method is used to calculate the weight vectors of evaluated factors, which can be represented as: n

577

λmax = ∑ si wi = SW = i =1

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576

SC

573 574

1 n ( AW )i ∑ n i =1 wi

2

The weight vector can therefore be obtained from matrix A = 3"/ 6D×D by

579 580

normalizing the vector in each column and then averaging over the rows of the resulting matrix.

581

(3) Consistency check

582

It is important to check the human judgments are internally consistent. One method is to calculate the consistency ratio (CR), which is a measure of how a given matrix

585

586

587 588

589

590

EP

584

compares to a purely random matrix in terms of their consistency indices. CR can be calculated by:

AC C

583

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578

C .R. =

C .I . R.I .

3

Where RI is the average random index, which is computed and tabulated as shown in Table b2. CI is the consistency index, which can be calculated as: C .I . =

λ max − n

4

n−I

[Table b2. inserted here]

19 / 25

ACCEPTED MANUSCRIPT 592

If a value of the consistency ratio CR is less than 0.1, the numerical judgements will be considered acceptable.

593

(4) Expert authority

594 595 596 597 598

The degree of expert authority The authority of experts is usually determined by two factors, one is the basis of the expert's judgment on the plan, which is divided into four aspects: practical experience,

RI PT

591

theoretical analysis, peer understanding and expert intuition. The value of these four aspects can be determined as 1.0, 0.8, 0.5 and 0.2. Another one is expert’s familiarity

601

0.8, 0.5, 0.2, 0.0. Ca + Cs 2

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602

Cr =

SC

600

with the problem, which is divided into five degrees: very familiar, familiar, general, not familiar and unfamiliar. The value of these five degrees can be determined as 1.0,

599

603

In Eq. (5), F refers to the degree of expert authority;  is the basis of experts’

604

judgement on indicators;  represents the degree of experts’ familiarity with the

605

problem. Expert consultation weights

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606

According to the advice of expert <, the weight vector of indicators can be

608

obtained as Wk = (w1k , w2k ,..., wnk )T . Where w jk is the weight of indicator j which is

609

consulted from expert <.

Expert authority coefficient Based on the judgement and familiar of experts, we calculate the degree of

AC C

611

EP

607

610

612

experts’ authority Crjk of indicator j which is consulted from expert <. Then the

613

experts’ authority coefficient C jk can be obtained:

614

5

C jk =

Crjk 6

q

∑ Cr k =1

jk

20 / 25

ACCEPTED MANUSCRIPT 615

The expert’s authority weight vector C j = (C j1 , C j 2 ,..., C jq ) of indicator j that is

616

consulted from expert < is composed by authority coefficient of G experts.

617

Where

q

∑C k =1

619

=1.

Weighted average weights The weighted average weight of indicator j is:

RI PT

618

jk

q

620

w ' j = ∑ C jk w jk

7

k =1

Then the corresponding weight vector is composed by weighted average weights

622

of  indicators.

623

4. Standardized of quantitative indicators

M AN U

SC

621

Quantitative indicators are divided into two categories: the benefit-type

625

(positive index) and the cost-type (reverse index) indicators. The higher the value

626

of efficiency indicator, the better performance it is. However, the cost indicator

627

has the opposite trend. The technical performance indicator of building energy

628

saving technology, the value-added benefits indicator of economic benefits

629

belongs to the efficiency index. On the contrary, the investment cost of energy

630

saving per unit, the operation and maintenance cost indicators all belong to the

631

cost index.

EP

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624

Benefit-type indicators and cost-type indicators are standardized according to

633

Eqs. (8) and (9), respectively. The processing method satisfies the trend and the

634

processing result is that the best is 1 and the worst is 0.

635

636

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632

Benefit-type indicator: rij =

Cost-type indicator: rij =

aij − min (aij ) j

max (aij ) − min (aij ) max (aij ) − aij j

max (aij ) − min (aij ) j

(i = 1 2 L m j = 1 2 L n )

8

j

j

(i = 1 2 L m j = 1 2 L n )

9

j

637

Where H"/ refers to the standardized value of indicator j which belongs to

638

(aij ) , min (aij ) represent the maximum and minimum evaluation target  ; max j j

21 / 25

ACCEPTED MANUSCRIPT 639

eigenvalue which are obtained by comparing different indicator j which belongs

640

to evaluation target  .

641

5

Standardized of qualitative indicators The qualitative index values of energy saving technologies of tobacco

643

enterprises are obtained through expert consultation. Five levels of “good, better,

644

general, poor, and bad” are set. Experts give the levels of the qualitative

645

indicators corresponding to each energy saving technology. For qualitative

646

indicators,

647

yij =  d(1)ij ,d(2)ij ,d(3)ij ,d(4)ij ,d(5)ij  . Where d(x)ij ( x = 1, 2,3, 4,5)

648

of experts at different levels. Based on the weighted average method, the

649

evaluation value of the qualitative index fuzzy number is quantified and set as the

650

corresponding quantification value of each level. The five levels of “good, better,

651

general,

transfer

experts’

comment

sets

into

fuzzy

numbers

refers to the proportion

poor,

and

M AN U

SC

we

RI PT

642

bad”

are

valued

as

f( 1 ) = 1, f( 2 ) = 0.75, f( 3 ) = 0.5, f( 4 ) = 0.25, f( 5 ) = 0 . d(x) is regarded as the

653

membership degree of x level and after quantifying, the value of qualitative

654

indicator is: 5

aij = ∑ d ( x )ij f ( x )

655

x =1

TE D

652

(i = 1 2 L m j = 1 2 L n )

(10)

The quantified value of the qualitative indicators is normalized by Eq. (8). After

657

the quantitative and qualitative indicators are standardized, the evaluation matrix

658

of each evaluation subsystem is obtained:

EP

656

659

660

6

L r1n  r22 L r2 n  L L L  rm 2 L rmn  r12

AC C

 r11 r R =  21 L   rm1

(11)

Fuzzy comprehensive evaluation

661

Fuzzy comprehensive evaluation is a method of comprehensive evaluation

662

based on fuzzy mathematics and applying the principle of fuzzy relation synthesis.

663

In this study, we use fuzzy comprehensive evaluation weighted model to make

664

comprehensive evaluation of all levels, see Eq. (12). 22 / 25

ACCEPTED MANUSCRIPT 665

E =W' R

(12)

666 667 668

Then the evaluation result vector of I evaluation targets is obtained. Noticed that, the evaluation value " of evaluation targets @ is: n

(13)

RI PT

669

ei = ∑ rij w ' j j =1

670

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ACCEPTED MANUSCRIPT Table 1. Primary selection of evaluation index Attribute

1 2

Comprehensive energy efficiency Energy conversion efficiency

Quantitative Quantitative

3 4

Energy saving of per unit product Energy saving of per unit built up area

Quantitative Quantitative

5

Continuously optimized energy saving potential

Quantitative

6

Technical maturity

Qualitative

1

Value-added benefits

4 5

Investment cost of per unit build up area Continuously optimized of investment cost

Quantitative Quantitative

6

Operation and maintenance cost

Quantitative

1

Difficulty of technology implementation

Operation management

Qualitative

2

Complexity of operation and maintenance

Qualitative

1

CO2 reduction

Quantitative

Environmental impact

2

Air pollution emissions

Qualitative

3

Utilization efficiency of resources

Qualitative

SC

3

2 Economic benefits

Quantitative

Energy saving capacity of per ten thousand yuan investment Investment cost of per unit energy conservation

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Technical performance

Second-level

RI PT

First-level

Quantitative Quantitative

Table 2. Professional composition of expert advisory group. Number of experts 4 4 3 5 9 25

Professional title Two senior engineer/Two engineer Two senior engineer/Two engineer One senior engineer/Two engineer Three senior engineer/Two engineer Three senior engineer/Two engineer

EP

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Research fields Technology consultation Energy saving design Equipment supply Energy management Users Total

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Table 3. Weight of indicators. First-level

U1: Technical performance

U2: Economic benefits

U3: Operation management

Weight

0.50

0.18

0.11

Second-level

Weight

U11: Comprehensive energy efficiency U12: Energy conversion efficiency U13: Energy saving of per unit product U14: Technical maturity U21: Energy saving capacity of per ten thousand yuan investment U22: Energy saving capacity of per ten thousand yuan investment U23: Operation and maintenance cost U31: Difficulty of technology implementation

0.49 0.25 0.15 0.11 0.51 0.25 0.24 0.71

ACCEPTED MANUSCRIPT

U4: Environmental impact

0.21

U32: Complexity of operation and maintenance U41: CO2 reduction U42: Air pollution emissions U43: Utilization efficiency of resources

0.29 0.50 0.27 0.23

3 4 5 6 7 8 9

U12 U13 U21 U22 U23

U41CO2

0.28

0.05 0.23 1.00 0.27 1.00

0.14

0.13

0.10 0.16 1.00 0.25 1.00

0.17

0.59

0.52 0.50 0.92 0.74 0.76

0.66

0.31

SC

2

U11

0.09 0.16 0.85 0.18 0.92

0.08

0.92

1.00 0.39 0.82 1.00 0.60

1.00

0.62

0.43 0.47 0.89 0.76 0.84

0.70

0.14

0.02 0.06 0.07 0.00 0.91

0.08

1.00

0.28 1.00 0.94 0.52 0.00

0.26

0.00

0.00 0.00 0.00 0.02 0.76

0.00

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1

Energy saving technics Indoor temperature and humidity standard optimization Indoor illumination standard optimization AHU system energy efficiency index optimization Boiler system energy efficiency index optimization Refrigerating system energy efficiency index optimization Lighting system energy efficiency index optimization Passive energy-saving technology applications Waste heat utilization technology in the plant Renewable energy utilization technology

M AN U

No.

RI PT

Table 4. Quantitative index normalization value.

Table 5. Standardized values of qualitative indicators.

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2

Single energy saving technics Indoor temperature and humidity standard optimization Indoor illumination standard optimization AHU system energy efficiency index optimization Boiler system energy efficiency index optimization Refrigerating system energy efficiency index optimization Lighting system energy efficiency index optimization Passive energy saving technology applications Waste heat utilization technology in the plant Renewable energy utilization

EP

No. 1

3 4 5 6 7 8 9

U14

U31

U32

U42

U43

0.92

0.87

0.89

0.70

0.26

1.00

1.00

1.00

0.10

0.00

0.92

0.63

0.38

0.40

0.32

1.00

0.71

0.36

1.00

0.47

0.70

0.58

0.32

0.10

0.05

0.86

0.76

0.72

0.00

0.00

0.57

0.63

0.62

0.00

0.11

0.00

0.00

0.00

0.30

0.68

0.54

0.29

0.36

0.15

1.00

ACCEPTED MANUSCRIPT technology

3

4

5

6

7 8 9

U2

Sorting

U3

Sorting

U4

Sorting

0.29

6

0.82

3

0.88

2

0.32

6

0.23

7

0.81

5

1.00

1

0.11

8

0.60

3

0.84

2

0.56

6

0.51

2

0.31

5

0.70

0.84

1

0.81

RI PT

2

Indoor temperature and humidity standard optimization Indoor illumination standard Optimization AHU system energy efficiency index optimization Boiler system energy efficiency index optimization Refrigerating system energy efficiency index optimization Lighting system energy efficiency index optimization Passive energy saving technology applications Waste heat utilization technology in the plant Renewable energy utilization technology

Sorting

6

0.61

5

0.42

3

4

0.50

7

0.54

1

0.58

4

0.84

1

0.75

3

0.35

5

0.15

8

0.25

8

0.63

4

0.07

9

0.71

2

0.61

7

0.00

9

0.37

4

0.06

9

0.19

9

0.31

8

0.27

7

TE D

1

U1

M AN U

No. Energy saving technics

SC

Table 6. Fuzzy comprehensive evaluation results of first-level index.

EP

Table 7. The results of comprehensive evaluation.

AC C

Energy saving technics Evaluation results T1: Indoor temperature and humidity standard 0.45 optimization T2: Indoor illumination standard optimization 0.39 T3: AHU system energy efficiency index optimization 0.62 T4: Boiler system energy efficiency index 0.43 optimization T5: Refrigerating system energy efficiency index 0.73 optimization T6: Lighting system energy efficiency index 0.60 optimization T7: Passive energy saving technology applications 0.20 T8:Waste heat utilization technology in the plant 0.54 T9: Renewable energy utilization technology 0.15

Sorting 5 7 2 6 1 3 8 4 9

ACCEPTED MANUSCRIPT Table 8. Standardized values of qualitative indicators.

4 5 6 7 8 9

U31

U32

U42

U43

0.92

0.87

0.89

0.70

0.26

1.00

1.00

1.00

0.10

0.00

0.92

0.63

0.38

0.40

0.32

1.00

0.71

0.36

1.00

0.47

0.70

0.58

0.32

0.10

0.05

0.86

0.76

0.72

0.00

0.00

0.57

0.63

0.62

0.00

0.11

0.00

0.00

0.00

0.30

0.68

0.15

1.00

RI PT

3

U14

SC

2

Single energy saving technics Indoor temperature and humidity standard optimization Indoor illumination standard optimization AHU system energy efficiency index optimization Boiler system energy efficiency index optimization Refrigerating system energy efficiency index optimization Lighting system energy efficiency index optimization Passive energy saving technology applications Waste heat utilization technology in the plant Renewable energy utilization technology

M AN U

No. 1

0.54

0.29

0.36

Table a1. Criteria used to determine the degree of correlation. Related degree

TE D

Correlation coefficient

Micro related

±0.30 - ±0.50

Moderate related

±0.50 - ±0.80

Significant related

±0.80 - ±1.00

Highly related

EP

0.00 - ±0.30

AC C

Table b1. Saaty’s lineal scale of preferences in the pair-wise comparison process. Numerical rating

Judgments of preferences between factor ࢏ and j rating

1 3 5 7 9 2，4，6，8

Factor ݅ is equally important to factor j Factor ݅ is slightly more important than factor j Factor ݅ is clearly more important than factor j Factor ݅ is strongly more important than factor j Factor ݅ is extremely more important than factor j Intermediate values

Table b2. Average random index values according to matrix size. n

2

3

4

5

6

7

8

9

ACCEPTED MANUSCRIPT 0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

AC C

EP

TE D

M AN U

SC

RI PT

R.I.

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

Fig.1. Comprehensive evaluation technology roadmap of energy-saving technology for tobacco enterprises.

ACCEPTED MANUSCRIPT

Evaluation of first-level index U2: Economic benefits U4: Environmental impact

T1: Indoor temperature and humidity standard optimization 1.00 0.80 0.60 0.40 T8:Waste heat utilization technology in the plant

0.20

T7: Passive energy saving technology applications

T3: AHU system energy efficiency index optimization

M AN U

0.00

T2: Indoor illumination standard optimization

SC

T9: Renewable energy utilization technology

RI PT

U1: Technical performance U3: Operation management

TE D

T6: Lighting system energy efficiency index optimization

T4: Boiler system energy efficiency index optimization

T5: Refrigerating system energy efficiency index optimization

AC C

EP

Fig.2. Evaluation results of first-level index.

ACCEPTED MANUSCRIPT Comprehensive evaluation T1: Indoor temperature and humidity standard optimization 0.8 0.7

T2: Indoor illumination standard optimization

0.6 0.45

RI PT

T9: Renewable energy utilization technology

0.5 0.4

0.39

0.3 0.15 0.2 0.54

0 0.2

0.43

M AN U

T7: Passive energy saving technology applications

T3: AHU system energy efficiency index 0.62 optimization

0.1

SC

T8:Waste heat utilization technology in the plant

TE D

T6: Lighting system 0.6 energy efficiency index optimization

T4: Boiler system energy efficiency index optimization

T5: Refrigerating system energy 0.73 efficiency index optimization

AC C

EP

Fig.3. Comprehensive evaluation results.