Measuring decarbonated development of tourist attractions associated with ecological environment and tourism economy

Abstract

The more and more significant the global ecological environment problems highlight, putting forward new requirements on the development of tourism and the transformation way of economic growth. So it indicates that tourism economy development and environmental protection should coexist, which is the base for measuring decarbonated development in the system of tourist attractions (DDSTA). Due to the complicated system of a tourist attraction, it not only contains connection between tourist activities and tourism resources, but also includes the relationships between human activity and ecological economy as well as the interaction of various factors in tourist attractions. This paper concentrates on how to measure decarbonated development of tourist attractions using the organic combination of qualitative and quantitative approach, proposing M²DL measurement mode associated with management entropy, measuring indicator reference set and application set for DDSTA and taking Jiuzhai Valley as a case study to apply management-entropy-based measurement system for DDSTA and extend previous research on low-carbon tourist attraction and tourism.

Keywords

Decarbonated development ecological environment management entropy Jiuzhai Valley

1 Introduction

Since the 1960s, the research of tourists areas involved in scenic spots is gradually extended from the tourism environmental bearing capacity and tourist satisfaction, resource development and ecological protection to the scenic area sustainable development [1 –4]. Since the 1990s, evaluation indexes and methods of the sustainable development of tourism destination have been the mainstream in the research, and to request that in the development of tourism resources development, at the same time, reducing the adverse impacts on the environment, paying attention to the protection of environment and improving the quality of environment to ensure the sustainable development of the scenic area [5 –7]. There were also some studies of similar research on sustainable tourism, such as Castellani and Sala [8], Feng et al. [9].

With low-carbon economy and its derivative ideas affecting the industrial structure, low-carbon economic development model is established. Scholars have made researches on low-carbon economy with tourism and therefore have made some achievements [10 –13]. For example, Cheng et al. [14] used the Xixi wetland as a case study, empirically examined an evaluation index system for low-carbon tourism attractions [14]. Ya-Yen Sun [15] proposed an analytical framework for decomposing the national tourism carbon footprint and carbon efficiency to identify the dynamics between economic growth, technological efficiency, and environmental externality [15]. Law et al. (2016) presented a framework for a green economy transition in tourism destinations [16]. There were other related researches such as Xu et al. [17], Luo et al. [18] andMeng [19].

Through the process of reviewing related literatures, the discussion indicates that the researches of sustainable development with low-carbon economy for tourist attractions have obtained many achievements and laid a certain foundation for later researching. However, few works have made researches on effect evaluation of low-carbon tourist environmental implementation associated with low-carbon economy, to reveal the degrees of order, trends and decarbonated construction effectiveness of internal system for tourist attractions. In addition, comprehensive evaluation of the system has shared a close relationship with each key indicator of the data, but some data acquisition is often difficult factually, and the organic combination of qualitative and quantitative methods have seldom been given to provide research framework for avoiding that indicators would be deleted because of the lack of the corresponding data. Moreover, evaluating a tourist attraction has laid emphasis up only on the study of a tourist attraction itself, but been lack of multi-level evaluation analysis to get the whole research of integration.

For the purpose of revealing sequence degrees of DDSTA, making up for the lack of effect measurement in low-carbon environmental implementation and decarbonated development measurement to overcome weaknesses that are difficult to reveal the degrees of order, trends and decarbonated construction effectiveness of internal system, the paper focuses on considering different aspects (economy, environment and society, etc.), quantitatively researching and systematically analyzing decarbonated development of tourist attractions systems based on comprehensively integrating a variety of measurement.

2 Materials and methodology

2.1 Layers of DDSTA and M²DL measurement mode

According to the implication of DDSTA, its common goal should be the coordinated development of ecological, economic and social benefits.

To better reflect the comprehensive benefit of a tourist attraction, while developing sustainable tourism economy and satisfying tourists’ demands, it is also of great importance to protect ecological resources, lower energy consumption and carbon emission intensity, as well as enhance per unit utilization efficiency of carbon and other resources. The efforts to steer the compound system of “economy-ecology-society” to a more harmonious and effective track is a good reflection of taking account of the balance development of multiple benefits. With the integration and coordinated development of all the elements, an orderly variation of decarbonated development can be further boosted. On one hand, the integration of those elements embodies the situation of economic operation, environmental representations and social development which are correlated with the targets of decarbonated development, namely, ecological benefit, economic benefit and social benefit. On the other hand, it also reflects the interaction among the elements, showing that in the process of decarbonated development in the system of a tourist attraction, decarbonated control and constructive guarantee will interactively influence each other when realizing of the three target benefits [10]. Their relation is shown as Fig. 1.

Fig.1

Structural framework of a tourist attraction system.

The conceptive model is shown as follows in Equation (1): $Max (DDSTA) = f (x_{1}, x_{2}, \dots, x_{5})$ (1)

In the model, x₁ refers to economic operation (EO) subsystem; x₂ refers to social development (SD) subsystem; x₃ refers to environmental representation (ER) subsystem; x₄ refers to decarbonated control (DC) subsystem; x₅ refers to constructive guarantee (CG) subsystem.

Multi-dimension and multi-level measurement mode is known as MD-ML, or M²DL. It refers to the observation of a tourist attraction’s environmental representation, social development, economic development, decarbonated control and other dimensions from the angles of tourists, local residents, management staff, employees and other stakeholders by taking space and time as the measurement level [20]. Considering the differences in statistical calibers for various indicators, statistical dimensions and macro-micro characters, this paper decides to build a measurement indicator system that is scientific, systematic, comparable, pragmatic, operable and concise. With this system, it is able to employ an integrated measurement method to conduct a comprehensive measurement on the system of a tourist attraction, verify the measurement results, discuss their differences and provide references for DDSTA.

2.2 Factor set for DDSTA

On the premise of the principles of being scientific, comprehensive, systematic, structured, relatively independent, representative, operable, and combining qualitative and quantitative indicators, as well as with people’s gradually deepened understandings towards the characteristics of measurement objects, the factor set for DDSTA forms as follows, including theoretical preparation, preliminary selection of indicator set, screening of indicator set, verification and application of indicator set and so on.

Under the guidance of Delphi method, the author adopted expert consultation method to collect advice from experts in the field of low-carbon ecotourism, tourist attraction management, and environmental protection. On the basis of the author’s own understanding towards this topic and the first round of expert consultation, the author added, deleted, combined and revised factors that impact DDSTA to get an initial measurement indicator set. The set was sent back to experts for a second round of suggestion, especially in terms of each factor’s correlation and significance to the topic of decarbonated development. Highly correlated factors were screened to pave the way for the establishment of a measurement indicator reference set for DDSTA.

According to experts’ follow-up suggestion towards adjustment of the factor set, the first measurement indicator system for DDSTA was set up, and that was the initial establishment of the measurement indicator system, as shown in Column One of Tables 1 and 2. After reporting to experts statistical results of factors gained at the first-round expert consultation, a preliminary indicator set was worked out after the addition, deletion, combination and revision of those factors. Moreover, on the basis of AHP, the preliminary indicator set aimed to reveal each factor’s function of reflection, measurement and comparison during measurement by thinking of hierarchical structure of DDSTA. In order to eliminate the impact of each indicator’s revealing sequence on measurement analysis, each indicator was ordered by their initials. Consequently, there formed a three-level measurement indicator system and a research scale for the second round of expert consultation which was expected to collect experts’ opinions on each indicator’s correlation and significance to the topic of decarbonated development. Experts were asked to score for every indicator. 1, 2, 3, 4 and 5 represented not important at all, relatively not important, average important, relatively import and very important respectively. What’s more, they were invited to propose advice for the optimization of the measurement indicator system.

Table 1
Credibility analysis and scoring results of the research scale in the second round of expert consultation (EO, SD, ER and CG)

Indicator (Code) Scale mean if Scale variance if Corrected item-total Cronbach’s alpha if Mean Std.

item deleted item deleted correlation item deleted deviation

Yield of carbon emission per unit (EO01) 136.14 140.052 0.301 0.906 4.64 0.586

Management cost contribution rate (EO02) 136.64 132.452 0.506 0.903 4.14 0.962

Tourist growth rate (EO03) 136.64 135.288 0.439 0.904 4.14 0.841

Growth rate of major business (EO04) 136.61 131.843 0.551 0.902 4.18 0.936

Return rate of total asset (EO05) 136.48 130.981 0.610 0.901 4.30 0.913

Residents’ awareness of low carbon and energy conservation (SD01) 136.34 137.792 0.411 0.904 4.45 0.658

Growth rate of residents’ per capita income (SD02) 136.66 130.301 0.532 0.903 4.13 1.080

Residents’ average educational level (SD03) 137.11 138.934 0.184 0.910 3.68 1.046

Preservation degree of local culture (SD04) 136.88 134.148 0.355 0.907 3.91 1.116

Surface water quality (ER01) 136.13 137.857 0.419 0.904 4.66 0.640

Air quality (ER02) 136.23 139.491 0.378 0.905 4.55 0.537

Biodiversity level (ER03) 136.18 137.095 0.513 0.903 4.61 0.593

Vegetation coverage rate (ER04) 136.09 139.392 0.416 0.904 4.70 0.502

Integrated management level (CG01) 136.16 135.665 0.590 0.902 4.16 0.987

Tourism safety guarantee degree (CG02) 136.63 130.166 0.597 0.901 4.27 0.774

Infrastructure construction level (CG03) 136.52 130.291 0.774 0.898 4.57 0.599

Application of advanced technology (CG04) 136.21 139.371 0.342 0.905 4.43 0.657

Growth rate of R&D input (CG05) 136.36 140.016 0.265 0.906 4.64 0.586

Indicator (Code)	Scale mean if	Scale variance if	Corrected item-total	Cronbach’s alpha if	Mean	Std.
Yield of carbon emission per unit (EO01)	136.14	140.052	0.301	0.906	4.64	0.586
Management cost contribution rate (EO02)	136.64	132.452	0.506	0.903	4.14	0.962
Tourist growth rate (EO03)	136.64	135.288	0.439	0.904	4.14	0.841
Growth rate of major business (EO04)	136.61	131.843	0.551	0.902	4.18	0.936
Return rate of total asset (EO05)	136.48	130.981	0.610	0.901	4.30	0.913
Residents’ awareness of low carbon and energy conservation (SD01)	136.34	137.792	0.411	0.904	4.45	0.658
Growth rate of residents’ per capita income (SD02)	136.66	130.301	0.532	0.903	4.13	1.080
Residents’ average educational level (SD03)	137.11	138.934	0.184	0.910	3.68	1.046
Preservation degree of local culture (SD04)	136.88	134.148	0.355	0.907	3.91	1.116
Surface water quality (ER01)	136.13	137.857	0.419	0.904	4.66	0.640
Air quality (ER02)	136.23	139.491	0.378	0.905	4.55	0.537
Biodiversity level (ER03)	136.18	137.095	0.513	0.903	4.61	0.593
Vegetation coverage rate (ER04)	136.09	139.392	0.416	0.904	4.70	0.502
Integrated management level (CG01)	136.16	135.665	0.590	0.902	4.16	0.987
Tourism safety guarantee degree (CG02)	136.63	130.166	0.597	0.901	4.27	0.774
Infrastructure construction level (CG03)	136.52	130.291	0.774	0.898	4.57	0.599
Application of advanced technology (CG04)	136.21	139.371	0.342	0.905	4.43	0.657
Growth rate of R&D input (CG05)	136.36	140.016	0.265	0.906	4.64	0.586

Table 2

Credibility analysis and scoring results of the research scale in the second round of expert consultation (DC)

Indicator (Code)	Scale mean if	Scale variance if	Corrected item-total	Cronbach’s alpha if	Mean	Std.
	item deleted	item deleted	correlation	item deleted		deviation
Adoption rate of low-carbon marketing approaches (DC01)	136.46	132.435	0.634	0.900	4.32	0.789
Growth rate of environmental protection input (DC02)	136.11	139.334	0.340	0.905	4.68	0.606
Usage rate of environmentally-friendly catering items (DC03)	136.27	138.927	0.335	0.905	4.52	0.660
Harmless treatment rate of rubbish (DC04)	136.23	137.854	0.406	0.904	4.55	0.658
Usage rate of environmentally-friendly packages for tourism products (DC05)	136.32	135.531	0.514	0.903	4.46	0.713
Usage rate of clear energy (DC06)	136.34	133.465	0.621	0.901	4.45	0.737
Scale of decrease of per capita transportation vehicle carbon emission (DC07)	136.18	141.531	0.204	0.907	4.36	0.796
Usage rate of bio-toilet (DC08)	136.43	133.995	0.539	0.902	4.54	0.602
Usage rate of ecological and energy-conservation devices (DC09)	136.25	133.973	0.735	0.900	4.41	0.596
Usage rate of ecological building material (DC10)	136.38	135.620	0.619	0.901	4.54	0.571
Concentrated sewage treatment rate (DC11)	136.25	137.027	0.540	0.903	4.59	0.532
Employees’ acknowledgement towards decarbonated development (DC12)	136.20	138.488	0.463	0.904	4.50	0.572
Tourists’ involvement degree of low-carbon travel (DC13)	136.29	139.008	0.388	0.904	4.13	0.689
Saving rate of residence energy consumption (DC14)	136.66	136.483	0.473	0.903	4.63	0.620

Altogether 60 pieces of research scale for the second round of expert consultation were handed out, and 56 valid pieces were handed in with an effective collection rate of 93.3%. By examining the relation of each indicator with its score in the scale, it could be found that the Cronbach’s Alpha value of the scale was 0.906. It was bigger than 0.65, indicating that scores of indicators given by experts were with high credibility and could pass the credibility test. Considering the fact that all indicators have gone through theoretical analysis, frequency analysis and expert consultation, they were with relatively high validity. As shown in Tables 1 and 2, the mean of each indicator’s score stood for the concentration of expert advisory opinion. The higher the value was, the more significant the indicator was. The standard deviation of each indicator’s score represented the difference between its variance and the average value. The lower the standard deviation was, the smaller the dispersion of experts’ score to the indicator would be, in other words, the more likely the experts would share same opinions. Apart from residents’ average educational level and preservation degree of local culture, the average value of the rest indicators were above four; apart from growth rate of residents’ per capita income, residents’ average educational level and preservation degree of local culture, the standard deviation value of the rest indicators were below one. However, experts advised to maintain these three indicators because their average values were above 3.5 and they were a good reflection of the social benefit brought by DDSTA.

2.3 Measuring indicator reference set and application set for DDSTA

On the basis of the above mentioned decarbonated development factor set and analysis on the preliminarily selected measurement indicators, a measurement indicator reference set for DDSTA was set up, as shown in Table 3. Each individual indicator of different dimensions belonged to positive indicator as they could bring positive impact to DDSTA. Quantification was utilized to analyze all indicators, but the quantized data for some of them were difficult to get directly. Therefore, experts who have involved in the research of the system of tourist attractions for a long time were asked to assign those indicators. The assignment was based on the specific performance of the system in the field of a certain indicator. It could be divided into five grades and their corresponding score scopes were low-to-high, namely (0, 2.0], (2.0, 4.0], (4.0, 6.0], (6.0, 8.0], (8.0, 10.0].

Table 3
Measurement indicator reference set for DDSTA

Indicators of Level Two Indicators of Level Three

EO EO01,EO02,EO03,EO04,EO05

SD SD01,SD02,SD03,SD04

ER ER01,ER02,ER03,ER04

DC DC01,DC02,DC03,DC04,DC05,DC06,DC07,DC08,DC09,DC10,DC11,DC12,DC13,DC14

CG CG01,CG02,CG03,CG04,CG05

Indicators of Level Two	Indicators of Level Three
EO	EO01,EO02,EO03,EO04,EO05
SD	SD01,SD02,SD03,SD04
ER	ER01,ER02,ER03,ER04
DC	DC01,DC02,DC03,DC04,DC05,DC06,DC07,DC08,DC09,DC10,DC11,DC12,DC13,DC14
CG	CG01,CG02,CG03,CG04,CG05

By referring to measurement indicator reference set for DDSTA, adhering to the principle of being operable and relatively independent, and combining the real situation of research objects, a necessity test and a completeness test were done on the indicators in the reference set on the basis of acquiring qualitative and quantitative indicators data in different period of time, aiming to lay a foundation for the setup of measurement indicator application set for DDSTA.

2.4 Management-entropy-based measurement system for DDSTA

In order to measure the evolution trend of the system of a tourist attraction from the angle of nonlinearity [21], a management entropy increase model is built by taking into account factors that contribute to the formation of management negative entropy flow and the increase of management entropy. It is presumed that the system of a tourist attraction is a relatively closed and isolated one, that is to say, it rarely exchanges information, energy or material with the environment. Inside the system, there exists differences in energy and it is an imbalanced state that can be explained by the following mathematic model expression as shown in Equations (2 and 3). $S_{I} = \sum_{i = 1}^{n} P_{i} S_{i}$ (2) $S_{i} = - K \sum_{i = 1}^{n} Q_{i} ln Q_{i}$ (3)

In the expression, i refers to different factors that lead to the system’s management entropy increase, for instance, carbon emission increase; P_i is the weight of i; S_i is the entropy value generated by i, K is management entropy coefficient, referring to the added cost required for per unit income increase in the certain industry that the system of a tourist attraction belongs to Q_i is the probability that i impacts the system’s variation in entropy value, and ∑Q_i = 1.

Taking into account the factors that contribute to the formation of management negative entropy flow and the decrease of management entropy, a management entropy decrease model or management dissipative model is built. It is presumed that the system of a tourist attraction is an open system that is far away from the balanced state. There is a non-linear and interactive relation between elements within the system. When the system’s external environmental condition arrives at a certain threshold value while the internal part of the system constantly exchanges energy, material and information with the environment, the total entropy value of the system will become a negative one, and its mathematic expression is as follows in Equations (4 and 5): $S_{E} = \sum_{j = 1}^{n} P_{j} S_{j}$ (4) $S_{j} = K \sum_{j = 1}^{n} Q_{j} ln Q_{j}$ (5)

In the expression, j refers to different factors that lead to the system’s management entropy decrease, for instance, the lowering of energy consumption; P_j is the weight of j; S_j is the negative entropy value generated by j; K is management entropy coefficient, referring to the added cost required for per unit income increase in the certain industry that a tourist attraction system belongs to. Q_j is the probability that j impacts the system’s variation in entropy value, and ∑Q_j = 1.

With the joint function of negative entropy flow and positive entropy flow, the variation in microscopic state of the system of a tourist attraction will ultimately be reflected by the macroscopic state development trend which is obtained by integrating the characterization data of all influential elements. Either management entropy increase model or management entropy decrease model is a reflection of a tourist attraction’s degree of disorder or degree of order during its decarbonated development process under the background of interaction between various subsystems in the whole system, as shown in the following Equation (6): $S_{T} = S_{E} + S_{I}$ (6)

When two neighboring time periods’ management entropy increase amount ΔS_T < 0, management negative flow plays a dominate role in the system of a tourist attraction, ensuring orderly decarbonated development. When ΔS_T = 0, management negative entropy flow offsets management positive entropy flow, that is to say, a tourist attraction will stay at a relatively balanced condition. When ΔS_T > 0, management positive flow plays a restrictive role during the decarbonated development, making the system evolve in a disordered direction.

The evolution of DDSTA can be a complicated process. By comparing the management entropy values of three consecutive time periods, we can get two increase amounts, namely, $Δ S_{T}^{I}$ and $Δ S_{T}^{II}$ . Then, V meaning decarbonated development speed of management-entropy-based system of tourist attractions can be calculated by the following Equation (7): $V = (| Δ S_{T}^{II} - Δ S_{T}^{I} | / - | Δ S_{T}^{I} |) \times 100 %$ (7)

GC-G1 combination weighting, namely Grey Correlation and G1 Method, is a kind of multiplication normalization method synthesized by an integrated and objective weighting method, i.e. Grey Correlation [22] and a subjective weighting method, i.e. G1 method (the improved AHP). In this way, it is possible to get the combination weight of each indicator. GC-G1 combination weighting can not only reflect experts’ experience-based judgment towards indicators for specific problems, but also conduct an objective weighting for the significance degree of indicators according to statistical data.

3 Case study

3.1 Data acquisition and pre-processing

This study chooses to measure Jiuzhai Valley, a famous tourist attraction in the world. Since the authors have kept an eye on the development of Jiuzhai Valley for several years, the related data was expected to last for as long as ten years which added the difficulty in acquiring them in real practice. Therefore, it was necessary to expand the channel of data acquisition through various means, diversify indicator data gathering methods and combine the real situation in Jiuzhai Valley, such as field research and interview, expert consultation and so on.

The first step was to collect original data for some indicators in Jiuzhai Valley, and then adopt field research, interview, observation and other methods to calculate their data in varied time period ranging from 2002 to 2012.

The second step was to use expert consultation method to score for the rest indicators. In order to ensure the conformity and objectivity of indicator assignment, altogether 12 management staff who have been involved in the real practice in the tourist attractions and researchers who have participated in the study of Jiuzhai Valley for a long time were invited to form an expert team to assign for related indicators for decarbonated development in Jiuzhai Valley. They came from science and research division of Jiuzhai Valley Management Bureau, residence management office, protection division, data information center, sightseeing company, planning and construction division, accounting division and marketing division.

3.2 Establishment of measurement indicator application set

Indicators were added or deleted on the basis of requirements from necessity test and completeness test to build a measurement indicator reference set for DDSTA.

With SPSS and other relevant analysis, the correlation coefficient of indicator in various dimensions and correlation coefficient among each indicator went through a necessity test. Considering that indicator data was bigger than sample data, it was believed that indicators were highly correlated when the critical value of Pearson Correlation stood at 0.9.

Taking into account the calculated coefficients, the indicator reference set was streamlined. Specifically, in the dimension of economic operation, growth rate of major business, which was highly correlated with tourist growth rate, was deleted; in the dimension of social development, preservation degree of local culture, which was highly correlated with several indicator of other dimensions, was deleted; in the dimension of environmental representation, biodiversity, which was highly correlated with air quality and vegetation coverage was deleted; in the dimension of constructive guarantee, tourism safety guarantee degree, which was highly correlated with infrastructure level and application of advanced technology was deleted; in the dimension of decarbonated control, usage rate of environmentally-friendly packages for tourism products, usage rate of environmentally-friendly catering items, usage rate of bio-toilet, usage rate of ecological building material, employees’ acknowledgement towards decarbonated development and saving rate of residence energy consumption, whose correlation coefficient were bigger than 0.9, were deleted. In the meantime, according to completeness test, the indicator application set should ensure a relatively systematic and comprehensive reflection of decarbonated development trend in Jiuzhai Valley, thus able to realize the goal of measurement. Therefore, after consulting experts, it was decided that in the dimension of low-carbon dimension, indicators with relatively high coefficients were maintained, and that was to say, adoption rate of low-carbon marketing approaches, harmless treatment rate of rubbish, usage rate of clear energy, usage rate of ecological and energy-conservation devices, concentrated sewage treatment rate, tourists’ involvement degree of low-carbon travel, which were crucial to decarbonated development of Jiuzhai Valley, were remained. Eventually, the measurement indicator application set and data for decarbonated development in Jiuzhai Valley was worked out and shown in Table 4.

Table 4
Measurement indicator application set and data for decarbonated development in Jiuzhai Valley from 2002 to 2012

Indicator 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

EO01 5130.49 587.50 9659.56 11351.45 13571.20 15441.50 3275.43 15538.22 15882.03 16199.46 19253.97

EO02 19.98 18.09 20.79 27.16 27.57 27.99 19.09 23.87 25.60 29.72 33.43

EO03 3.21% –11.28% 75.32% 5.39% 15.02% 8.83% –74.47% 121.41% 19.29% 68.21% 26.92%

EO05 11.55% 0.83% 14.65% 11.74% 12.10% 12.94% 0.55% 5.55% 6.58% 8.71% 10.24%

SD01 3.62 4.28 5.09 6.21 7.38 7.57 6.88 7.73 7.99 7.80 7.93

SD02 1.38% 0.00% 0.00% –3.40% 26.76% 26.39% –47.25% 8.33% 11.54% 24.14% 25.00%

SD03 4.12 4.20 4.32 4.53 4.65 5.00 5.10 5.22 5.46 5.50 5.83

ER01 8.41 8.13 8.33 8.53 8.04 9.51 9.58 9.64 9.66 9.64 9.72

ER02 8.94 9.15 9.31 9.28 9.33 9.43 9.52 9.62 9.65 9.70 9.75

ER04 84.06% 84.31% 84.65% 85.15% 85.25% 85.39% 85.45% 85.48% 85.50% 85.50% 85.53%

DC01 5.46 5.93 6.21 6.42 6.79 7.20 7.73 8.06 8.30 8.30 8.60

DC02 5.16% 22.78% –3.56% 5.99% 6.52% 15.71% –11.64% 24.75% 13.60% 4.23% 6.76%

DC04 85.40% 86.20% 87.60% 90.30% 94.50% 96.20% 97.80% 98.20% 98.70% 99.20% 99.00%

DC06 3.69 4.28 4.42 4.49 4.60 5.26 5.55 5.73 5.85 5.95 5.98

DC07 –2.01% –1.11% 0.20% 14.08% 0.66% –13.03% 13.54% 11.23% –4.83% 13.67% –1.59%

DC09 3.23 3.35 3.49 3.78 3.87 4.03 4.13 4.72 4.95 5.03 5.05

DC11 86.30% 86.80% 87.00% 91.70% 93.50% 95.20% 96.50% 96.80% 97.00% 98.20% 99.00%

DC13 3.93 4.83 5.23 5.81 6.65 7.00 7.14 7.18 7.36 7.38 7.40

CG01 3.97 4.47 5.49 6.88 7.00 7.03 7.03 7.63 7.82 8.57 8.71

CG03 5.97 6.04 6.05 6.08 6.10 6.38 6.67 7.47 7.80 8.42 8.73

CG04 5.76 5.94 5.95 5.96 6.66 6.82 6.60 7.73 7.97 8.71 8.86

CG05 7.50% 46.51% –20.95% –16.06% 40.19% 6.83% –18.21% 50.00% 4.17% 3.00% 2.18%

Indicator	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012
EO01	5130.49	587.50	9659.56	11351.45	13571.20	15441.50	3275.43	15538.22	15882.03	16199.46	19253.97
EO02	19.98	18.09	20.79	27.16	27.57	27.99	19.09	23.87	25.60	29.72	33.43
EO03	3.21%	–11.28%	75.32%	5.39%	15.02%	8.83%	–74.47%	121.41%	19.29%	68.21%	26.92%
EO05	11.55%	0.83%	14.65%	11.74%	12.10%	12.94%	0.55%	5.55%	6.58%	8.71%	10.24%
SD01	3.62	4.28	5.09	6.21	7.38	7.57	6.88	7.73	7.99	7.80	7.93
SD02	1.38%	0.00%	0.00%	–3.40%	26.76%	26.39%	–47.25%	8.33%	11.54%	24.14%	25.00%
SD03	4.12	4.20	4.32	4.53	4.65	5.00	5.10	5.22	5.46	5.50	5.83
ER01	8.41	8.13	8.33	8.53	8.04	9.51	9.58	9.64	9.66	9.64	9.72
ER02	8.94	9.15	9.31	9.28	9.33	9.43	9.52	9.62	9.65	9.70	9.75
ER04	84.06%	84.31%	84.65%	85.15%	85.25%	85.39%	85.45%	85.48%	85.50%	85.50%	85.53%
DC01	5.46	5.93	6.21	6.42	6.79	7.20	7.73	8.06	8.30	8.30	8.60
DC02	5.16%	22.78%	–3.56%	5.99%	6.52%	15.71%	–11.64%	24.75%	13.60%	4.23%	6.76%
DC04	85.40%	86.20%	87.60%	90.30%	94.50%	96.20%	97.80%	98.20%	98.70%	99.20%	99.00%
DC06	3.69	4.28	4.42	4.49	4.60	5.26	5.55	5.73	5.85	5.95	5.98
DC07	–2.01%	–1.11%	0.20%	14.08%	0.66%	–13.03%	13.54%	11.23%	–4.83%	13.67%	–1.59%
DC09	3.23	3.35	3.49	3.78	3.87	4.03	4.13	4.72	4.95	5.03	5.05
DC11	86.30%	86.80%	87.00%	91.70%	93.50%	95.20%	96.50%	96.80%	97.00%	98.20%	99.00%
DC13	3.93	4.83	5.23	5.81	6.65	7.00	7.14	7.18	7.36	7.38	7.40
CG01	3.97	4.47	5.49	6.88	7.00	7.03	7.03	7.63	7.82	8.57	8.71
CG03	5.97	6.04	6.05	6.08	6.10	6.38	6.67	7.47	7.80	8.42	8.73
CG04	5.76	5.94	5.95	5.96	6.66	6.82	6.60	7.73	7.97	8.71	8.86
CG05	7.50%	46.51%	–20.95%	–16.06%	40.19%	6.83%	–18.21%	50.00%	4.17%	3.00%	2.18%

After indicators went through translation non-dimension process, the steps of GC-G1 assignment were used to integrate respectively the Grey Correlation assignments of second-tier indicator weights as well as their G1 assignments.

3.3 Results and analysis

In accordance with management-entropy-based measurement process for DDSTA, and on the basis of conducting assignment for indicators in the dimensions of economic operation, social development, environmental representation, decarbonated control and constructive guarantee with data organization and analysis as well as GC-G1 combination, this paper included the weights of all indicators into the statistic of those five dimensions to get their own dimension value, which was the sum of indicator values (in percentage or within the range of 0 to 1) multiplied their corresponding weights in a sole dimension. Under the same measurement, this method could clearly reflect the variation trend of each subsystem year by year. On this basis, management-entropy-based measurement system for DDSTA was adopted to comprehensively integrate the measurement indicator application set as well as the variation trend for the management entropy in the system ofJiuzhai Valley.

The corresponding data from 2002 to 2012 for 22 indicators after they have gone through the translation non-dimension process were substituted into several equations, namely Equations (2) to (7). The relative change in Jiuzhai Valley was analyzed from a vertical perspective without doing any comparison with other tourist attractions. Therefore, the management entropy coefficient was set at 1. On the premise that the corresponding data of all factors (indicators) which have influence on the system’s entropy value would go through a non-dimension process, the probability for the variation of the system’s entropy value would be calculated with the proportion of indicator data in certain stages. As for the influential indicators’ weights, they were calculated by measurement indicator weight. By integrating the corresponding management entropy values of all indicators from varied dimensions in this system, the lumped variation trend of the system’s management entropy value could be worked out, as shown in Fig. 2. The variation trend of management entropy value could clearly indicate the development trend of the system of Jiuzhai Valley.

Fig.2

Variation trend for comprehensive integration of management entropy in Jiuzhai Valley.

According to Fig. 2, if we made a comparison between the management entropy values of Jiuzhai Valley in two neighboring years, as management entropy increment referred to the value that the later year’s system management entropy value deducted that of the previous year, we could find that the system developed towards a disorder direction in 2003 and 2008, and the management entropy value has a trend to increase; while in the year of 2004, 2005, 2006, 2007, 2009, 2010, 2011 and 2012, the system management entropy value has the rend to decrease, as demonstrated in Table 5. That was to say, in those years, this system was influenced by negative entropy flow, and it moved towards decarbonated development thanks to the fact that the system’s social benefit, economic benefit and ecological benefit interacted and interplayed with each other.

Table 5

Management entropy variation of Jiuzhai Valley

	2002	2003	2004	2005	2006	2007
S _T	–0.14251	–0.14169	–0.14309	–0.14313	–0.14328	–0.14347
ΔS _T	–	0.00083	–0.00140	–0.00005	–0.00015	–0.00019
V	–	–	269.61%	96.70%	219.51%	27.95%
	2008	2009	2010	2011	2012
S _T	–0.14075	–0.14163	–0.14242	–0.14249	–0.14330
ΔS _T	0.00272	–0.00088	–0.00079	–0.00007	–0.00081
V	1538.66%	132.36%	10.02%	91.22%	1059.62%

Referring to management-entropy-based rating scale for decarbonated development speed in tourist attractions, management entropy increment variation showed that the orderly development speed of this system had increased by 269.61% from 2002 to 2004, while it decreased by 96.70% from 2003 to 2005. It was revealed that orderly development speed of this system had increased by 219.51% from 2004 to 2006. And comparisons of other years are as shown in Table 5. These indicated that in the process of decarbonated development, the tourist attraction has gained some preliminary achievement after positive exploration and efforts in all aspects. Although there existed some increment towards disorder development in the process, on the whole, it was towards the decarbonated development direction. Thus, it could be defined as a decarbonated system of tourist attractions. When developing tourism economy, it was also necessary to protect the environment so that economic development and environmental protection could mutually promote each other.

By applying different kinds of advanced technologies, improving integrated management level, promoting tourist attractions’ ability in energy conservation and environmental protection, maintaining high vegetation coverage degree, tourist attractions’ yield of carbon emission per capita would definitely increase. In other words, when ecological benefit was enhanced, economic benefit and social benefit could be acquired at the same time.

4 Conclusions

In order to expound the decarbonated development of tourist attractions under the influence of low-carbon economy based on the goals and layers of DDSTA, giving consideration to both sides of economic efficiency and ecological benefits, the paper has discussed M²DL measurement mode, measuring indicator reference set and application set for DDSTA, developed a proper and dynamic management-entropy-based system for assessing the decarbonated development level and the degrees of order, trends and decarbonated construction effectiveness of tourist attractions’ system scientifically and systematically using indicators in the dimensions of economic operation, social development, environmental representation, decarbonated control and constructive guarantee with data organization and analysis as well as GC-G1combination.

Advocating the low-carbon tourism, keeping decarbonated development of tourist attractions and how to measuring its state scientifically and reasonably have already become a indispensable component of depth discussions of the theory of tourism development and current management practice in tourist attractions. Such researches are still on the way. Due to the limitation of subjective and objective conditions, data acquisition and processing tend to be difficult in making the research, so further in-depth studies would be needed.

Footnotes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant nos. 71501019, 71501138, 71601164 and 71371130), Youth Program of Social Science Research of Sichuan Province for the Twelfth Five-year Plan (Grant nos. SC15C005 and SC15C030), General Program of Education Department in Sichuan Province (Grant nos. 16SB0071 and 16SB0049), General Program of Mineral Resources Research Center in Sichuan Province (Grant no. SCKCZY2014-YB04).

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Measuring decarbonated development of tourist attractions associated with ecological environment and tourism economy

Abstract

Keywords

1 Introduction

2 Materials and methodology

2.1 Layers of DDSTA and M2DL measurement mode

Table 3 Measurement indicator reference set for DDSTA Indicators of Level Two Indicators of Level Three EO EO01,EO02,EO03,EO04,EO05 SD SD01,SD02,SD03,SD04 ER ER01,ER02,ER03,ER04 DC DC01,DC02,DC03,DC04,DC05,DC06,DC07,DC08,DC09,DC10,DC11,DC12,DC13,DC14 CG CG01,CG02,CG03,CG04,CG05

3.1 Data acquisition and pre-processing

3.2 Establishment of measurement indicator application set

Footnotes

Acknowledgments

References

2.1 Layers of DDSTA and M²DL measurement mode

Table 3
Measurement indicator reference set for DDSTA

Indicators of Level Two Indicators of Level Three

EO EO01,EO02,EO03,EO04,EO05

SD SD01,SD02,SD03,SD04

ER ER01,ER02,ER03,ER04

DC DC01,DC02,DC03,DC04,DC05,DC06,DC07,DC08,DC09,DC10,DC11,DC12,DC13,DC14

CG CG01,CG02,CG03,CG04,CG05