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The measurement and comparative study of carbon dioxide emissions from tourism in typical provinces in China
Acta Ecologica Sinica 35 (2015) 184–190
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Acta Ecologica Sinica journal homepage: www.elsevier.com/locate/chnaes
The measurement and comparative study of carbon dioxide emissions from tourism in typical provinces in China Pu Wu a, Yuanjun Han a,⁎, Mi Tian b,c a b c
China Tourism Academy, Beijing 100005, China Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100010, China University of Chinese Academy of Sciences, 100049, China
a r t i c l e
i n f o
Article history: Received 22 April 2014 Received in revised form 13 January 2015 Accepted 12 February 2015
Keywords: Tourist industry Carbon dioxide emissions Tourism consumption stripping coefficient
a b s t r a c t The calculation of carbon dioxide emissions from tourism is the precondition of setting goals of energy conservation and emission mitigation in certain areas of China. It's also essential to the sustainable tourism development in certain areas. This paper develops a method, based on the concept of “tourism consumption stripping coefficient”, to calculate the emissions from tourism of Beijing, Shandong, Zhejiang, Hubei and Hainan. The study shows that during 2009 and 2011, the total emissions from tourism of the five provinces and cities kept increasing, while emissions per tourist dropped from 56.569 kg to 54.088 kg. During this period, Hainan's emissions from tourism remained the lowest. Hubei's emissions from tourism surged from the third place in 2009 to the first place in 2011. Beijing was the only subject area to show an un-disturbed downward trend. Hainan, though with the lowest total emissions, had the highest emissions per tourist. Only Beijing and Hainan saw their emissions per tourist dropped continuously during 2009 and 2011. Zhejiang's emissions from tourism showed a reverse U-shape trend, while those of Shandong and Hubei showed U-shape trends. In the future, China should promote energy conservation and emission reduction through formulating action guidelines, innovating energy conservation technology, strengthening environmental protection awareness and regional tourism cooperation. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Climate change, energy conservation and emission reduction are no longer just science matters. They have become international affairs in recent years. China has become the world's largest carbon dioxide emitter [1–2]. It is under considerable pressure to cut its greenhouse gas emissions. Therefore, energy conservation, emission mitigation and tackling climate change are now a priority of China in developing its economy and society. Amid this background, China has put forward its emission reduction target. By 2020, it will reduce its CO2 per unit of GDP by 40% to 45%, relative to 2005 levels [3–4]. To this end, all sectors need to take stronger actions that fit the current situations and future developments of their CO2 emissions. Tourist industry is in a good position in this regard for it is an energy conservative and environmental friendly industry with low energy consumption and pollution. Since the 1990s, emissions from tourism and related topics have gradually gained the attention of the Western scholars. Earlier studies focused on energy utilization, including emissions from tourism. Natalia Tabatchnaia-Tamirisa et al. analyzed the linkage between energy use and a tourism destination experiencing a raid growth. The main finding ⁎ Corresponding author. Tel.: +1 86 166 024. E-mail addresses: [email protected] (P. Wu), [email protected] (Y. Han).
http://dx.doi.org/10.1016/j.chnaes.2015.09.004 1872-2032/© 2015 Elsevier B.V. All rights reserved.
of the study was that tourists accounted for 60% of total energy use of the area [5]. Stefan et al. studied the energy use of global tourist industry from five aspects, including energy demands, land use, vegetation changes and species diversity. The results showed that, in 2001, transport contributed 94% of tourism energy consumption, and accommodation 3.5% [6]. Low carbon economy has gained momentum in the West ever since the British Government put forward the idea in 2003. There are an increasing number of researches on emissions in tourism from different perspectives, including means of transport, tourist destination, energy conservation and emission mitigation policies, calculation of emissions from tourism and etc. The World Tourism Organization's (WTO) report suggested that 75% of the emissions from tourism were contributed by the transport industry. Thus, capping emissions from the transport industry was essential to emission reduction of tourist industry [7]. Peeters and Dubois found that tourists caused 4.4% of global CO2 emissions in 2005. Also, these emissions were projected to grow at an average rate of 3.2% per year up to 2035 [8]. Paravantis and Georgakellos conducted a research in Greece leading them to the conclusion that passenger cars and busses were responsible for 95% of carbon emissions from land transportation. Their research showed that traveling by public transportation is of significance to emission reduction [9]. Becken et al. carried out empirical studies on energy consumption mode of tourist attraction and activities in New Zealand.
Becken et al. [10];Howitt et al. [17] Nielsen et al. [18] It studies emission from tourism at the country and local levels. It facilitates the comparison of emission levels among tourist industry, the other service industries and the second industry. Top–bottom It uses national or regional carbon dioxide approach emission data and the integrated environmental and economic accounting method to calculate emissions from tourism.
Aggregation of tourist statistics of different levels and different items. It breaks down the data from the country level, to local level and to sector level. Starts from tourist arrivals, and works upward to arrive at energy consumption and emissions. Bottom-up approach
References
Hunter [15];Roberto et al. [16] The area of productive land required to produce the resources that tourism needs and to assimilate the wastes that it produces. Ecological footprint
Quantified measurement of the ecological influence of tourism on an area.
This criterion, measured by land area, is direct. Related material is highly available. The calculation method is practical and repeatable. It gives an effective depiction of the linkage between tourism demands and land. The logic is simple. Indirect energy consumption and emissions are not considered.
It magnifies indirect energy consumption and indirect emissions from tourism, and is therefore not conducive to studying the background energy consumption and emissions from tourism. It obscures the territorial borders, and shifts a proportion of the energy consumption from source markets to tourist destinations. The variation of land productivity makes the result less comparable. It doesn't differentiate the land functions according to the types of lands, and is a static analysis method. It highly depends on the availability of tourism data. Large volumes of data need to be gathered through field investigations. The results are lower than actual levels. Data availability is not satisfying because a statistic system has not been set up in most countries.
Disadvantages Advantages Characteristics
It's comprehensive, and gives a whole picture Covers direct and indirect emissions produced during of energy consumption and emissions. the whole process, from production to consumption. The aggregation of greenhouse gas emissions produced by enterprises, activities, products and individuals during transport, food production and consumption, and other production processes.
Definition
Carbon Footprint
Table 1 Common quantification method of CO2-emissions from tourism.
Department of Resources, Energy and Tourism of Australia [11];Loke et al. [5]
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They revealed that transport accounted for 65% to 73% of the tourism energy bill [10]. There are few studies on the carbon emissions of tourist destination because territorial system is relatively complicated. The Department of Resources, Energy and Tourism of Australia estimated emissions from tourism from two aspects: production and consumption. They determined that the tourism carbon footprint of Australia during 2003 and 2004 stood at 115 million t [11]. Using the carbon footprint approach, Loke et al. explored the linkage between energy use of Hawaii and the rapid growth of tourist arrivals and changing tourist mix. The main finding of the study was that tourists accounted for 60% of total energy use in Hawaii. Arising proportion of foreign tourists in the total mix of tourists would increase the demand for energy [5]. A system of energy conservation and emission mitigation measures has taken shape. WTO has proposed measures that the governments, tourism companies and tourists can take to reduce energy consumption and emissions from tourism. It also proposed measures and technical approaches for energy conservation and emission mitigation in related sectors, such as transport, construction and equipment manufacturing [12]. Richard et al. used a simulation model to estimate the impact of a carbon tax. They pointed out that a carbon tax of USD 1000/t would reduce demand for air travel by 0.8%, and reduce carbon dioxide emissions by 0.9% [13]. Becken et al. studied the carbon emissions of ecological hotels in the Lamington National Park, a world heritage site. The hotels had been granted the Green Globe 21 Certificate. The study showed that after being certified, the hotels cut their energy consumption substantially, reducing carbon dioxide by 189 t per year, saving AUD 15000 [10]. Besides political and technical approaches, tourists' choices are also an important factor in energy conservation and emission mitigation. Transportation dominated the energy bill of international and domestic tourists. Alternating ones' travel styles, tourists can substantially influence their energy demand [10]. Buckley believed that “slow travel”, a type of tourism without high-speed transport like air travel, was an effective way to reduce carbon emissions from tourism. It places more value on tourist activities, and place equal importance on both tourist activities and tourist destinations [14]. There is not yet a systematic measurement of carbon dioxide emissions from tourism [2]. Literature studies show that there are four common methods (Table 1): the carbon footprint approach and ecological footprint approach usually used to research climate change and sustainable development, the “bottom–up approach” and the “top–bottom approach”. These methods have their own advantages, disadvantages and applicability. Western scholars have applied these methods in their researches according to the study areas and the subjects. Table 1 presents a comparison of these methods. Considering the situations of China's tourism, this paper uses both the bottom–up approach and the top–bottom approach. Specifically, sampling data on the composition of tourist consumption are used, as well as annual statistic data, input and output of tourism related industries. Based on these data, carbon emissions from tourism are calculated using the tourism consumption stripping coefficient proposed by Li Jiangfan et al. in 1999 [35]. The calculation of the coefficients involves two steps. First, added value ratios are used to transform into added values to the gross values of commodity trade, catering, transport, postal industries, and related services (including accommodation, entertainment, and other services) generated by tourism. Second, the coefficients are calculated by dividing these added values by the overall added values of the industries. Since 2009, there have been an increasing number of researches and media reports on carbon emissions from tourism. They mainly focus on promotion and education of low-carbon tourism [19–26], experience of mitigating emissions from tourism [27–28], and related policies and measures. For example, Cai Meng et al. studied the connotations of low-carbon tourism. They depicted a road map for developing low-carbon tourism from several perspectives, including tourist attraction, facilities, tourist experience, and tourist consumption habits [29]. Liu Xiao put forward an optimal mode for low-carbon tourism
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development in the suburbs of Beijing [30]. Lin studied the transport emissions in five national parks in Taiwan. He concluded that the government could lower carbon emissions by improving management efficiency and using price leverage. He also concluded that the government could lower emissions from tourism by encouraging short-haul tourism, improving transport load, using cleaner energy and applying other technical measures [31]. Several researches have focused on quantifying energy consumption and emissions from tourism. Shi Peihua used the bottom–up method to estimate the energy consumption and emissions from tourism in 2008 [32]. Xie Yuanfang et al. assessed and analyzed the carbon emissions of the Yangtze River Delta area. They noticed that carbon emissions from tourism of the area had been increasing. They also noticed that, emissions from transport, storage and postal industries dominated carbon emissions from tourism, while catering, accommodation and commodity trading also emitted considerable carbon dioxide [33]. Kuo & Chen applied the life-cycle appraisal method in studying the influence of tourism on the environment of the Penghu Islands, Taiwan. Their study showed that tourism of the Islands was responsible for an annual energy consumption of 7.37 × 108 MJ, and an annual emission of 5 × 1010 g carbon dioxide [2]. Tourist industry is not a stand-alone sector in the national economic census. It's comprised of many industries, such as transport and catering, etc. It's difficult to obtain accurate data of tourist industry from the results of national economic census. The lacking of tourism statistics and materials makes it difficult to quantify energy consumption and carbon emissions of this industry. This paper uses the top–bottom method and the bottom–up method, as well as the tourism consumption coefficient stripping method proposed by Li Jiangfan et al. [35] in 1999 to calculate emissions from tourism. It's a positive exploration of quantifying emissions from tourism. In addition, the method suits China's tourist industry. Existing studies of emissions from tourism focus more on the emissions at the country level, less on those at the provincial level. In this paper, five provinces and cities are chosen as the subjects from 28 areas of which tourism is a pillar industry. They are Beijing, Shandong, Zhejiang, Hubei and Hainan. They are chosen considering geographic conditions and data availability. Carbon emissions from tourism of these provinces and cities are calculated and compared. This research touches upon areas un-explored by existing studies. 2. Research method and data source 2.1. Calculation of carbon emissions Tourist industry is comprised of several industries. They should all be considered if accurate emissions from tourism of specific areas are to be obtained. However, emission data of individual industries are lacking in China. Therefore, carbon emissions are mostly obtained through studying energy consumption, a method also applied in this paper. Carbon emissions from tourism are comprised mainly of carbon dioxide [21]. So, the level of carbon dioxide emissions is used to represent the level of carbon emissions. But in China, statistics on tourism energy consumption is not directly available from the yearbooks. Thus, three steps are taken to arrive at the subject areas' carbon emissions from tourism. First, study the energy consumption of the industries related to tourism. Separate the proportion generated by tourism. Second, multiply the energy consumptions by the energy–emission ratio to identify the carbon emissions. Third, sum these up. The equations are as follow:
C¼
2 X
Ci
ð1Þ
i¼1 2 X ðEij r j βÞ Ci ¼ i¼1
ð2Þ
Eij ¼ Eij Si
ð3Þ
C is the carbon emissions from tourism of a specific area. Ci refers to the emissions generated by industries related to tourism, such as transport, storage, postal, and wholesale, retail sales and catering industries. Eij refers to consumption of j energy caused by tourism in these industries, while rj is the coefficient used to convert it into consumption of standard coal (Table 2). βrefers to carbon dioxide emissions from a unit of standard coal. It's set as 2.45 in this paper, based on existing previous studies [34] Eij⁎is the consumption of j energy by one of the following industries: transport, storage, postal industries, wholesale, retail and catering industries. Si is the tourism consumption stripping coefficient of one of these industries. The method is further explained in the following text. Before using Eqs. (1), (2) and (3) to calculate emissions from tourism of specific areas, the tourism consumption stripping coefficients must be identified. The coefficient was proposed by Li Jiangfan. It is calculated by dividing the added value contributed by tourism by the overall added value of an industry. The added value contributed by tourism is derived by multiplying the gross value contributed by tourism by the added value ratio [35]. Once the tourism consumption stripping coefficient is identified, tourism energy consumption can be separated from the overall energy consumption of an industry. The equation is as follows: Si ¼ T i =Ri :
ð4Þ
Where Si is the tourism consumption stripping coefficient of i industry; Ri refers to the added value of i industry; Ti means the added value of i industry contributed by tourism. The added value ratio is calculated by dividing the added value by the total value of the industry. Note that, to calculate the added value of i industry contributed by tourism, the added value ratios of different sectors in i industry are assumed to be the same. It should be noted that the industry classification of national economic census is basically consistent with that of China Energy Yearbook. In national economic census, industries related to tourism include three sectors: transport, storage and postal industries; wholesale and retail industries; accommodation and catering industries. In the regional energy balance of China Energy Yearbook, they include transport, storage and postal, wholesale, retail, accommodation and catering industries. To calculate the tourism consumption stripping coefficients, the tourism consumption types in the investigation are mapped to the industry classification of the national economic census and the China Energy Yearbook. As a result, long-haul transport, city transport, postal and communication, and four other industries are grouped into the transport, storage and postal industries; tourist site visit, shopping and entertainment are grouped into the wholesale and retail industries; catering and accommodation correspond to the catering and accommodation industries. Because of different compositions, the consumption of domestic and foreign tourists are calculated separately, and added together. Considering data availability of energy consumption by industry, the wholesale, retail, catering and accommodation industries are further grouped in this study. The purpose is to improve the accuracy of the energy consumption. Therefore, the tourism consumption stripping coefficients of two industry clusters are calculated in this paper: that of the transport, storage and post industries; and that of the wholesale, retail, accommodation and catering industries. 2.2. Data source The calculation of emissions from tourism requires two data-sets, one of the tourism consumption stripping coefficient and another one of energy consumption. As to tourism consumption stripping coefficients, they are different for domestic and foreign tourists because of
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Table 2 Standard coal coefficient. Energy
Standard coal coefficient
Energy
Standard Coal coefficient
Raw Coal Briquette Fuel Oil Gasoline Kerosene Diesel Blast Furnace Gas Paraffin
0.7143 kgce/kg 0.5000–0.7000 kgce/kg 1.4286 kgce/kg 1.4714 kgce/kg 1.4714 kgce/kg 1.4571 kgce/kg 0.1286 kgce/m3 1.3648 kgce/kg
Solvent Oil Petroleum asphalt Other petroleum products Liquefied petroleum gas Oil field gas Gas field gas Thermal (equivalent) Power (equivalent)
1.4672 kgce/kg 1.3307 kgce/kg 1.400 kgce/kg 1.7143 kgce/kg 1.3300 kgce/m3 1.2143 kgce/m3 0.03412 kgce/MJ 0.1229 kgce/(kW·h)
Note: The standard coal coefficient of briquette is between 0.5000 and 0.7000. To simplify the calculation, the mean, or 0.6, is used in calculating the carbon emissions from tourism of Hainan. China Energy Yearbook only shows the total consumption of natural gas in Hainan. It doesn't contain breakdowns of different natural gas. To be consistent, the standard coal coefficient of natural gas is set as 1.2722. This is the mean of the standard coal coefficients of oil-based natural gas and gas-based natural gas. Data source: Chinese General Principles of Calculation of the Comprehensive Energy Consumption (GB/T2589-2008), and Handbook of Energy Statistics (2006).
their different consumption structures. The reports on the sample surveys of consumption structures in the five provinces and cities during 2009 and 2011 are used. Other data used include 2010 to 2012 yearbooks, industrial outputs posted on the statistic bureaus, tourism statistic communiqués, summaries and fact sheets. As to energy consumptions, they can be found in the regional energy balance sheets of China Energy Statistical Yearbook (2010–2012). In addition, standard coal coefficients are obtained from the Chinese General Principles for Calculation of the Comprehensive Energy Consumption (GB/T25892008)and the Handbook of Energy Statistics (2006).
3. Empirical results and analysis 3.1. Economic status of tourism and overall characteristics of carbon emissions from tourism in the five provinces and cities Two steps are involved in calculating the tourism consumption stripping coefficients for Beijing, Zhejiang, Shandong, Hubei and Hainan. The first step is calculating the added values of tourism during 2009 and 2011, and their proportions in local GDPs (Table 3). The results are then used to calculate the tourism consumption stripping coefficients using Eq. (4). In China, the driving effect of tourism on regional economy is commonly evaluated by the proportion of tourism turnover to GDP. But this method is flawed in that turnover measures the total output, while GDP measures added value and is only a part of the total output. In light of this, this paper uses the proportion of tourism added value to GDP instead. The results, as shown in Table 3, are distinctively different for the five provinces and cities during 2009 and 2011. The proportion of Beijing was the largest, staying above 9% in all three years. That of Shandong, on the other hand, was the smallest, between 3% and 4%. But it showed an upward trend, signaling a larger driving effect. Internationally, an industry with a proportion over 5% is qualified as a pillar industry; and over 8%, a strategic pillar industry. Thus, tourist industry was a strategic pillar industry for Beijing during 2009 and 2011. For Zhejiang and Hainan, it was a pillar industry and was on track to become their strategic pillar industries. For Hubei, it had rapidly elevated into a pillar industry. But its importance to Shandong was not showing since the proportion was between 3% and 4%. This was not consistent with the people's impression that Shandong was strong in tourism. It
might be attributed to large turnovers of the other industries in Shandong, which weaken the contribution of tourism. The tourism consumption stripping coefficients of the five provinces and cities during 2009 and 2011 are presented in Table 4. They are significantly different from each other. In 2009, Beijing had the largest coefficients: 0.387 for transport, storage and postal industries, 0.451 for wholesale, retail, accommodation and catering industries. Shandong had the smallest coefficients, 0.167 and 0.225 respectively. In 2010 and 2011, however, Zhejiang's coefficients for transport, storage and postal industries were the highest, standing at 0.334 and 0.343. Those of Shandong, 0.177 and 0.167, remained the lowest. In 2010 and 2011, Beijing was still the area with the largest tourism consumption stripping coefficients for wholesale, retail, accommodation and catering industries. The figures were 0.504 and 0.522 respectively. Meanwhile, Shandong, again, had the smallest coefficient, both of which stood at 0.214. These figures suggest that tourism made a considerable and stable contribution to both industry clusters of Beijing. On the other hand, it made insignificant contribution to those of Shandong as the coefficients were small. When examining the coefficients of the two industry clusters, one can see that tourism made an increasingly larger contribution to Zhejiang's transport, storage and postal industries. In 2010 and 2011, its coefficients were the highest. Also note that, during 2009 and 2011, the coefficients for transport, storage and postal industries were both larger than those for wholesale, retail sale, accommodation and catering industries. This meant that tourism had a larger play in the former industry cluster than the latter. This paper divides tourism related industries into two clusters: transport, storage and postal industries, wholesale, retail, accommodation and catering industries. The emissions of these two industry clusters are calculated for the five provinces and cities, and summed up to arrive at the total emissions (Table 5). They are discussed in this section while the comparison among the five provinces and cities are left for the following sections. Table 5 shows that, during 2009 and 2011, the total emissions of the subject areas kept increasing, from 49,804.45 thousand t to 65,867.65 thousand t, or by 32.85%. The emissions per tourist, on the other hand, dropped from 56.569 kg to 54.088 kg, 0r by 4.39%. This continuous drop indicates that tourists in China have stronger awareness of environmental protection. It also suggests that energy conservative technologies and new energies have been able to improve energy efficiency. Total emissions from tourism and emissions per tourist of transport, storage and postal industries
Table 3 Added values of tourism (unit: 100million RMB) and their proportion to GDP, 2009–2011 (%). Beijing
2009 2010 2011
Zhejiang
Shandong
Hubei
Hainan
Added value
Proportion to GDP
Added value
Proportion to GDP
Added value
Proportion to GDP
Added value
Proportion to GDP
Added value
Proportion to GDP
1185.3 1345.6 1563.4
9.99 9.77 9.77
1353.1 1671 2080.4
5.93 6.14 6.50
1121.8 1404.9 1732.9
3.32 3.56 3.81
549.2 794.8 1096.2
4.28 5.03 5.59
97 121 149.2
5.89 5.90 5.93
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Table 4 Tourism Consumption Stripping Coefficient, 2009–2011. Transport, storage and postal industries Beijing 2009 2010 2011
0.387 0.327 0.328
Zhejiang 0.237 0.334 0.343
Wholesale, retail sale, accommodation and catering industries
Shandong
Hubei
0.167 0.177 0.167
Hainan
0.196 0.237 0.271
0.269 0.268 0.265
during 2009 and 2011 were higher than those of wholesale, retail sale, accommodation and catering industries. This means that the former was the determinant of the overall emission levels of the five provinces and cities, and remained so during 2009 and 2011. 3.2. Comparison of emissions from tourism of the five provinces and cities Fig. 1 presents the total emissions from tourism of the five provinces and cities during 2009 and 2011. It shows that the emissions from tourism of Hainan during this period remained the lowest. In 2011, for example, its emissions were only 12.20% of that of Hubei, the province with the highest emissions. This was attributable to the small tourism turnover of Hainan. The same period saw Hubei's emissions from tourism surged from ranking No.3 in 2009 to No.1 in 2011. This posed much greater pressure on its environment. The emissions from tourism of Zhejiang ranked in the middle. They also showed a weakening growth. Their growth rate declined from 44.37% in 2010 to 1.78% in 2011. Beijing's emissions from tourism went from being No.2 in 2009, to No.4 in 2010 and 2011. It was also the only subject area whose emissions from tourism decreased. Its emissions dropped from 12,393.59 thousand t in 2009 to 12,160 thousand t in 2011. The emissions of
Beijing 0.543 0.504 0.522
Zhejiang
Shandong
0.451 0.414 0.426
0.225 0.214 0.214
Hubei 0.320 0.368 0.439
Hainan 0.320 0.323 0.338
Shandong showed a moderate growth trend. They were the highest in 2009 and 2010, and the second highest in 2011.See Fig. 2 The differences of emissions from tourism of the five provinces and cities were largely caused by varying emission composition. Table 5 shows that Shandong had the highest emissions from transport, storage and postal industries. It stood at 9415.669 thousand t in 2009, 10,659.25 thousand t in 2010, and 11,116.51 thousand t in 2011, exhibiting a moderate growth trend. On the other hand, Shandong's emissions from wholesale, retail sale, accommodation and catering remained in the third place. When the emissions from the two industry clusters were added up, Shandong's emissions from tourism went from being the highest in 2009 and 2010, to being the second highest in 2011. Hubei, on the other hand, presented a sharp contrast. The years 2010 and 2011 saw its tourism emissions from transport, storage, post and telecommunication industries increased by 26.52% and 40.4% respectively, reaching 10,412.74 thousand t in 2011. Hubei's tourism emissions from wholesale, retail sale, accommodation and catering industries went up from the second place in 2009 to the first place in 2011, and reached 8769.61 thousand t. This was 3272.15 thousand t higher than Zhejiang, the second runner. This high growth rate and absolute value brought the aggregated emissions from tourism of
Table 5 Total Emissions from Tourism and Emissions per Tourist, 2009–2011.
Beijing
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Zhejiang
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and cateringindustries Total emissions from tourism (emissions per tourist)
Shandong
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Hubei
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Hainan
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Total
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Note: Figures outside the brackets are the annual emissions from tourism in 10,000 t. Those inside are the emissions per tourist in kg.
2009
2010
2011
842.805 (51.351) 396.554 (24.162) 1239.359 (75.513) 599.092 (23.992) 472.928 (18.939) 1072.02 (42.931) 941.567 (32.234) 466.042 (15.955) 1407.609 (48.189) 586.183 (38.568) 466.494 (30.693) 1052.676 (69.261) 179.352 (79.700) 29.428 (13.077) 208.781 (92.778) 3148.999 (35.767) 1831.445 (20.802) 4980.445 (56.569)
764.843 (41.590) 363.428 (19.762) 1128.271 (61.352) 986.444 (32.680) 561.211 (18.593) 1547.655 (51.273) 1065.925 (25.304) 495.141 (11.754) 1561.066 (37.059) 741.631 (35.101) 672.066 (31.809) 1413.697 (66.910) 193.717 (74.871) 23.814 (9.204) 217.531 (84.075) 3752.561 (32.798) 2115.659 (18.491) 5868.22 (51.289)
822.638 (38.498) 394.142 (18.445) 1216.78 (56.943) 1025.449 (29.489) 549.746 (15.809) 1575.194 (45.298) 1111.651 (31.521) 530.769 (15.050) 1642.42 (46.751) 1041.274 (38.047) 876.961 (32.043) 1918.235 (70.089) 205.853 (68.587) 28.284 (9.424) 234.136 (78.010) 4206.864 (34.545) 2379.901 (19.543) 6586.765 (54.088)
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Time-wise, only Beijing and Hainan saw their emissions per tourist dropped continuously during 2009 and 2011. Zhejiang's emissions per tourist increased before they declined, showing a reverse U-shape. On the other hand, those of Shandong and Hubei showed a U-shape, as they declined before they increased. Looking into the composition of emission per tourist during 2009 and 2011, it's noted that the transport, storage and postal industries were a larger contributor than the wholesale, retail sale, accommodation and catering industries. Therefore, the former should be the focus of mitigating emissions per tourist. Actions include: improving the efficiency of traditional energy, popularizing new energy, promoting innovation of energy conservation and emission reduction technologies, strengthening tourists' awareness of resource conservation and environmental protection. Fig. 1. Total Emissions from Tourism of the five provinces and Cities (10,000 thousand ton).
4. Conclusions and policy advices
Hubei from the third place in 2009 to the first place in 2011. The results were abruptly greater pressure on the environment. Hainan's emissions from the two industry clusters were both much lower than those of the others. Besides, they had much smaller growth. Therefore, the emissions from tourism of Hainan were the lowest. Tourism emissions of Beijing and Zhejiang from the two industry clusters were ranked in the middle, resulting in their total emissions from tourism ranking in the middle. Fig. 1 shows the emissions per tourist from tourism. It can be seen that Hainan had the highest emissions per tourist, though it had the lowest total emissions from tourism. This was closely related to its location and the tourist structure. The main mean of transport was flight, resulting in relatively high energy consumption. This is also shown in Table 5. Hainan's emissions from transport, storage and postal industries during 2009 and 2011 were 79.700 kg, 74.871 kg and 68.587 kg respectively, exceeding those of the second runners by 28.349 kg (Beijing), 39.77 kg (Hubei) and 30.54 kg (Hubei) respectively. It suggests that tourism emissions from transport, storage, postal and telecommunication industries were a determinant of Hainan's emissions from tourism. From the above analysis and Table 5, it can be concluded that, Hainan had the highest emissions per tourist, at 84.954 kg. Hainan faced a relatively small pressure on environment caused by emissions from tourism. Nonetheless, adequate attention should be given to this aspect because Hainan, with the highest emissions per tourist, would face larger pressure as its tourism grows. As to the other provinces and cities, Hubei merits more attention from local government. This is because Hubei had the second highest emissions per tourist, which remained as high as 68.753 kg. Beijing ranked the third with its emissions per tourist standing at 64.603 kg. Beijing is a world famous tourist attraction, which suffers critical influence of fog and haze. To create a better tourism environment, it should keep lowering the emissions per tourist. Emissions per tourist of Zhejiang and Shandong were 46.50 kg and 43.94 kg respectively, ranking No.4 and No.5. These were relatively low.
During 2009 and 2011, there was a continuous increase in the total emissions from tourism of Beijing, Shandong, Zhejiang, Hubei and Hainan. They increased by 32.85%, from 49,804.45 thousand t to 65,867.65 thousand t. Meanwhile, emissions per tourist had lowered by 4.39%, from 56.569 kg to 54.088 kg. The increase in total emissions was caused by continuous increase in tourist number. The mitigation of emissions per tourist was attributable to the tourists' strengthened awareness of environmental protection, and innovation of energy conservation technologies. New technologies and devices, such as natural gas vehicles and solar power illuminators were the technical support for mitigating emissions per tourist. On the other hand, energy conservation and emission mitigation obligations rendered policy support. The total emissions from tourism of the five provinces and cities during 2009 and 2011 are compared. The results show that emissions from tourism of Hainan remained the lowest. That of Hubei surged from the third place in 2009 to the first place in 2011. Beijing was the only area to show an un-disturbed downward trend. Its emissions from tourism dropped from 12,393.59 thousand t in 2009 to 12,160 thousand t in 2011. Hainan's emissions from tourism were the lowest because its tourist numbers and economic scale were the smallest. Its tourism turnovers were only RMB 21.17 billion in 2009 and RMB 32.404 billion in 2011. These were much lower than those of the other four areas. The rapid increase of Hubei's emissions from tourism was brought by the booming tourism. Hubei's tourism turnover was up by 66% during 2009 and 2011, from RMB 120 billion to RMB 199.2 billion. The emissions per tourist of the five subject areas during 2009 and 2011 are also compared. The results show that Hainan, though with the lowest total emissions, had the highest emissions per tourist. Coming up next was Hubei, with its average emissions per tourist standing at 68.753 kg. Following were Beijing, Zhejiang and Shandong, with their emissions per tourist at 64.60 kg, 46.50 kg and 43.94 kg respectively. The emissions per tourist of Hainan were the highest because most tourists to Hainan traveled by air, a carbon-intensive mean of transport.
Fig. 2. Emissions per Tourist (kg).
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Another cause was the large number of high-end hotels and tourism complexes. Time-wise, only Beijing and Hainan saw their emissions per tourist continued to drop during 2009 and 2011. Zhejiang's emissions per tourist increased before they declined, showing a reverse U-shape. On the other hand, those of Shandong and Hubei showed a U-shape, as they declined before they increased. Looking into the composition of emissions per tourist during 2009 and 2011, it's noted that the transport, storage and postal industries were a larger contributor than the wholesale, retail sale, accommodation and catering industries. The former was the determinant of total emissions of the five provinces and cities, and remained so throughout the period. In the future, these five provinces and cities should make energy conservation and emission mitigation a priority of sustainable tourism development. They should also take this opportunity to upgrade and transform tourism. First, governments should take actions favorable to local situations to lower emissions without sacrificing tourism development. Their rational choices will contribute to sustainable tourism development in China. Second, governments should enhance tourists' awareness of environmental protection and advocate low-carbon tourism. They should adopt political and fiscal measures to support tourism companies' efforts in innovating energy conservation technologies and tourism services. Third, they should attach great importance to emission mitigation in the transport, storage and postal industries. Other actions include: promoting changes in tourism activities, developing energy efficient vehicles, improving the efficiency of traditional energy in the transport industry and popularizing new energy. These shall lower total emissions from tourism. Fourth, governments should view tourist industry and environment from a dynamic perspective. This is because, during 2009 and 2011, only Beijing and Hainan saw their emissions per tourist continuously decreased. The five provinces and cities should make use of existing regional cooperation platforms to boost exchanges on mitigating emissions from tourism. The purpose is to form a normalized emission mitigation mechanism based on exchanges, which will create a win–win situation. Acknowledgements This paper is Supported by the National Natural Science Foundation of China (Grant No. 41101044); National Social Science Youth Foundation (Grant No. 14CGL022). References [1] IEA(International Energy Agency), World energy outlook[R], International Energy Agency, Paris, 2014. [2] L. Yiwen, H. Zongyi, Research on regional difference about carbon emission efficiency in China [J], J. Shanxi Univ. Finance Econ. 37 (2) (2015) 23–32. [3] W.P. Shi Peihua, The basic ideas and initiatives focused on the development of low-carbon tourism [N], China Tourism News, 01-19-2010. [4] Shi Peihua,Feng Ling,Wu Pu. Energy saving and low-carbon of tourism industry development [M]. Beijing: Tourism Press,2010: 37-41. [5] M.K. Loke, P.S. Leung, K.A. Tucker, Energy and tourism in Hawaii [J], Ann. Tour. Res. 24 (2) (1997) 390–401.
[6] S. Gössling, P. Peeters, et al., The Eco-efficiency of tourism[J], Ecol. Econ. 54 (2005) 417–434. [7] UNWTO, Towards a low carbon travel & tourism sector [R]. Report in World Economic Forum, 2009. [8] P. Peeters, G. Dubois, Tourism travel under climate change mitigation constraints [J], J. Transp. Geogr. 18 (3) (2010) 447–457. [9] J. Paravantis, D. Georgakellos, Trends in energy consumption and carbon dioxide emissions of passenger cars and buses[J], Technol. Forecast. Soc. Chang. 74 (5) (2007) 682–707. [10] S. Becken, D.G. Simmons, C. Frampton, Energy use associated with different travel choices [J], Tour. Manag. 24 (3) (2003) 267–277. [11] Department of Resources, Energy and Tourism of Australia, A Report of Carbon Footprint/Greenhouse Gas Emissions of Australian Tourism Industry — Based on Tourism TSA of Australia [R]Sydney 2009. [12] UNWTO, Towards a low carbon travel & tourism sector[R], Report in World Economic Forum, 2009. [13] S.J.T. Richard, The impact of a carbon tax on international tourism [J], Transp. Res. D 12 (2007) 129–142. [14] R. Buckley, Tourism under Climate change: will slow travel supersede short breaks? [J], Ambio 40 (2011) 328–331. [15] C. Hunter, Sustainable tourism and the touristic ecological footprint [J], Environ. Dev. Sustain. 4 (1) (2002) 7–20. [16] R.M.C. Roberto, P.R.S. Pedro, Ecological footprint analysis of road transport related to tourism activity: the case for Lanzarote island [J], Tour. Manag. 31 (1) (2010) 98–103. [17] O.J.A. Howitt, V.G.N. Revol, I.J. Smith, et al., Carbon emissions from international cruise ship passengers' travel to and from New Zealand [J], Energ Policy 38 (5) (2010) 2552–2560. [18] S.P. Nielsen, A. Sesartic, M. Stucki, The greenhouse gas intensity of the tourism sector: the case of Switzerland, Environ. Sci. Pol. 13 (2) (2010) 131–140. [19] J. Wang, Low-carbon technology: the development of tourism must face[N], China Tourism News, 09-23-2009 011. [20] W. Xiaoan, Low-carbon economy and low carbon travel[N], China Tourism News, 11-30-2009 002. [21] W. Xiaoan, Low-carbon economy development of China's tourism brings significant opportunities: low carbon travel is quietly popular[N], Jiangnan Tour Report 12-102009, p. 009. [22] S. Shigui, Low carbon travel stories [N], China Economic Herald, 12-24-2009 B01. [23] F. Jun, Low-carbon economy leads to the new life way [N], Qingdao Daily, 12-252009 005. [24] G. Wen, The rise of low-carbon tourism in China [N], China Tourism News, 2009-1230 013. [25] Chai Ying. Low-carbon tourism should be emphasized[N].Worker's Daily,2010–01– 03(004). [26] J. Yang, Low-carbon: the pursuit of new tourist attractions [N], China Tourism News, 01-04-2010 005. [27] H. Wensheng, Bama' new patterns of low-carbon tourism implementation[J], Today's Wealth 10 (2009) 104–105. [28] H. Wensheng, On the low-carbon tourism and the creation of low carbon tourist attractions[J], Ecol. Econ. 11 (2009) 100–102. [29] C. Meng, Y. Wang, Low-carbon tourism: a new mode of tourism development [J], Tour. Tribune 25 (2010) 13–17. [30] L. Xiao, Low carbon tour: a future rural tourism model of Beijing [J], Soc. Sci. Beijing 1 (2010) 42–46. [31] Y.P. Lin, Carbon dioxide emissions from transport in Taiwans national parks[J], Tour. Manag. 31 (2010) 285–290. [32] W.P. Shi Peihua, A rough estimation of energy consumption and CO2 emission in tourism sector of China[J], J. Geogr. Sci. 66 (2011) 235–243. [33] Y. Xie, Z. Yuan, Measuring carbon dioxide emissions from energy consumption by tourism in Yangtze river Delta[J], Geogr. Res. 31 (2012) 429–438. [34] C. Fei, Zhu Dajian Theory of research on low-carbon city and Shanghai empirical analysis [J], Urban Stud. 10 (2009) 71–79. [35] L. Jiangfan, L. Meiyun, On the calculation of tourism industry and tourist adding value [J], Tour. Tribune 5 (1999) 16–19.
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Acta Ecologica Sinica journal homepage: www.elsevier.com/locate/chnaes
The measurement and comparative study of carbon dioxide emissions from tourism in typical provinces in China Pu Wu a, Yuanjun Han a,⁎, Mi Tian b,c a b c
China Tourism Academy, Beijing 100005, China Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100010, China University of Chinese Academy of Sciences, 100049, China
a r t i c l e
i n f o
Article history: Received 22 April 2014 Received in revised form 13 January 2015 Accepted 12 February 2015
Keywords: Tourist industry Carbon dioxide emissions Tourism consumption stripping coefficient
a b s t r a c t The calculation of carbon dioxide emissions from tourism is the precondition of setting goals of energy conservation and emission mitigation in certain areas of China. It's also essential to the sustainable tourism development in certain areas. This paper develops a method, based on the concept of “tourism consumption stripping coefficient”, to calculate the emissions from tourism of Beijing, Shandong, Zhejiang, Hubei and Hainan. The study shows that during 2009 and 2011, the total emissions from tourism of the five provinces and cities kept increasing, while emissions per tourist dropped from 56.569 kg to 54.088 kg. During this period, Hainan's emissions from tourism remained the lowest. Hubei's emissions from tourism surged from the third place in 2009 to the first place in 2011. Beijing was the only subject area to show an un-disturbed downward trend. Hainan, though with the lowest total emissions, had the highest emissions per tourist. Only Beijing and Hainan saw their emissions per tourist dropped continuously during 2009 and 2011. Zhejiang's emissions from tourism showed a reverse U-shape trend, while those of Shandong and Hubei showed U-shape trends. In the future, China should promote energy conservation and emission reduction through formulating action guidelines, innovating energy conservation technology, strengthening environmental protection awareness and regional tourism cooperation. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Climate change, energy conservation and emission reduction are no longer just science matters. They have become international affairs in recent years. China has become the world's largest carbon dioxide emitter [1–2]. It is under considerable pressure to cut its greenhouse gas emissions. Therefore, energy conservation, emission mitigation and tackling climate change are now a priority of China in developing its economy and society. Amid this background, China has put forward its emission reduction target. By 2020, it will reduce its CO2 per unit of GDP by 40% to 45%, relative to 2005 levels [3–4]. To this end, all sectors need to take stronger actions that fit the current situations and future developments of their CO2 emissions. Tourist industry is in a good position in this regard for it is an energy conservative and environmental friendly industry with low energy consumption and pollution. Since the 1990s, emissions from tourism and related topics have gradually gained the attention of the Western scholars. Earlier studies focused on energy utilization, including emissions from tourism. Natalia Tabatchnaia-Tamirisa et al. analyzed the linkage between energy use and a tourism destination experiencing a raid growth. The main finding ⁎ Corresponding author. Tel.: +1 86 166 024. E-mail addresses: [email protected] (P. Wu), [email protected] (Y. Han).
http://dx.doi.org/10.1016/j.chnaes.2015.09.004 1872-2032/© 2015 Elsevier B.V. All rights reserved.
of the study was that tourists accounted for 60% of total energy use of the area [5]. Stefan et al. studied the energy use of global tourist industry from five aspects, including energy demands, land use, vegetation changes and species diversity. The results showed that, in 2001, transport contributed 94% of tourism energy consumption, and accommodation 3.5% [6]. Low carbon economy has gained momentum in the West ever since the British Government put forward the idea in 2003. There are an increasing number of researches on emissions in tourism from different perspectives, including means of transport, tourist destination, energy conservation and emission mitigation policies, calculation of emissions from tourism and etc. The World Tourism Organization's (WTO) report suggested that 75% of the emissions from tourism were contributed by the transport industry. Thus, capping emissions from the transport industry was essential to emission reduction of tourist industry [7]. Peeters and Dubois found that tourists caused 4.4% of global CO2 emissions in 2005. Also, these emissions were projected to grow at an average rate of 3.2% per year up to 2035 [8]. Paravantis and Georgakellos conducted a research in Greece leading them to the conclusion that passenger cars and busses were responsible for 95% of carbon emissions from land transportation. Their research showed that traveling by public transportation is of significance to emission reduction [9]. Becken et al. carried out empirical studies on energy consumption mode of tourist attraction and activities in New Zealand.
Becken et al. [10];Howitt et al. [17] Nielsen et al. [18] It studies emission from tourism at the country and local levels. It facilitates the comparison of emission levels among tourist industry, the other service industries and the second industry. Top–bottom It uses national or regional carbon dioxide approach emission data and the integrated environmental and economic accounting method to calculate emissions from tourism.
Aggregation of tourist statistics of different levels and different items. It breaks down the data from the country level, to local level and to sector level. Starts from tourist arrivals, and works upward to arrive at energy consumption and emissions. Bottom-up approach
References
Hunter [15];Roberto et al. [16] The area of productive land required to produce the resources that tourism needs and to assimilate the wastes that it produces. Ecological footprint
Quantified measurement of the ecological influence of tourism on an area.
This criterion, measured by land area, is direct. Related material is highly available. The calculation method is practical and repeatable. It gives an effective depiction of the linkage between tourism demands and land. The logic is simple. Indirect energy consumption and emissions are not considered.
It magnifies indirect energy consumption and indirect emissions from tourism, and is therefore not conducive to studying the background energy consumption and emissions from tourism. It obscures the territorial borders, and shifts a proportion of the energy consumption from source markets to tourist destinations. The variation of land productivity makes the result less comparable. It doesn't differentiate the land functions according to the types of lands, and is a static analysis method. It highly depends on the availability of tourism data. Large volumes of data need to be gathered through field investigations. The results are lower than actual levels. Data availability is not satisfying because a statistic system has not been set up in most countries.
Disadvantages Advantages Characteristics
It's comprehensive, and gives a whole picture Covers direct and indirect emissions produced during of energy consumption and emissions. the whole process, from production to consumption. The aggregation of greenhouse gas emissions produced by enterprises, activities, products and individuals during transport, food production and consumption, and other production processes.
Definition
Carbon Footprint
Table 1 Common quantification method of CO2-emissions from tourism.
Department of Resources, Energy and Tourism of Australia [11];Loke et al. [5]
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They revealed that transport accounted for 65% to 73% of the tourism energy bill [10]. There are few studies on the carbon emissions of tourist destination because territorial system is relatively complicated. The Department of Resources, Energy and Tourism of Australia estimated emissions from tourism from two aspects: production and consumption. They determined that the tourism carbon footprint of Australia during 2003 and 2004 stood at 115 million t [11]. Using the carbon footprint approach, Loke et al. explored the linkage between energy use of Hawaii and the rapid growth of tourist arrivals and changing tourist mix. The main finding of the study was that tourists accounted for 60% of total energy use in Hawaii. Arising proportion of foreign tourists in the total mix of tourists would increase the demand for energy [5]. A system of energy conservation and emission mitigation measures has taken shape. WTO has proposed measures that the governments, tourism companies and tourists can take to reduce energy consumption and emissions from tourism. It also proposed measures and technical approaches for energy conservation and emission mitigation in related sectors, such as transport, construction and equipment manufacturing [12]. Richard et al. used a simulation model to estimate the impact of a carbon tax. They pointed out that a carbon tax of USD 1000/t would reduce demand for air travel by 0.8%, and reduce carbon dioxide emissions by 0.9% [13]. Becken et al. studied the carbon emissions of ecological hotels in the Lamington National Park, a world heritage site. The hotels had been granted the Green Globe 21 Certificate. The study showed that after being certified, the hotels cut their energy consumption substantially, reducing carbon dioxide by 189 t per year, saving AUD 15000 [10]. Besides political and technical approaches, tourists' choices are also an important factor in energy conservation and emission mitigation. Transportation dominated the energy bill of international and domestic tourists. Alternating ones' travel styles, tourists can substantially influence their energy demand [10]. Buckley believed that “slow travel”, a type of tourism without high-speed transport like air travel, was an effective way to reduce carbon emissions from tourism. It places more value on tourist activities, and place equal importance on both tourist activities and tourist destinations [14]. There is not yet a systematic measurement of carbon dioxide emissions from tourism [2]. Literature studies show that there are four common methods (Table 1): the carbon footprint approach and ecological footprint approach usually used to research climate change and sustainable development, the “bottom–up approach” and the “top–bottom approach”. These methods have their own advantages, disadvantages and applicability. Western scholars have applied these methods in their researches according to the study areas and the subjects. Table 1 presents a comparison of these methods. Considering the situations of China's tourism, this paper uses both the bottom–up approach and the top–bottom approach. Specifically, sampling data on the composition of tourist consumption are used, as well as annual statistic data, input and output of tourism related industries. Based on these data, carbon emissions from tourism are calculated using the tourism consumption stripping coefficient proposed by Li Jiangfan et al. in 1999 [35]. The calculation of the coefficients involves two steps. First, added value ratios are used to transform into added values to the gross values of commodity trade, catering, transport, postal industries, and related services (including accommodation, entertainment, and other services) generated by tourism. Second, the coefficients are calculated by dividing these added values by the overall added values of the industries. Since 2009, there have been an increasing number of researches and media reports on carbon emissions from tourism. They mainly focus on promotion and education of low-carbon tourism [19–26], experience of mitigating emissions from tourism [27–28], and related policies and measures. For example, Cai Meng et al. studied the connotations of low-carbon tourism. They depicted a road map for developing low-carbon tourism from several perspectives, including tourist attraction, facilities, tourist experience, and tourist consumption habits [29]. Liu Xiao put forward an optimal mode for low-carbon tourism
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development in the suburbs of Beijing [30]. Lin studied the transport emissions in five national parks in Taiwan. He concluded that the government could lower carbon emissions by improving management efficiency and using price leverage. He also concluded that the government could lower emissions from tourism by encouraging short-haul tourism, improving transport load, using cleaner energy and applying other technical measures [31]. Several researches have focused on quantifying energy consumption and emissions from tourism. Shi Peihua used the bottom–up method to estimate the energy consumption and emissions from tourism in 2008 [32]. Xie Yuanfang et al. assessed and analyzed the carbon emissions of the Yangtze River Delta area. They noticed that carbon emissions from tourism of the area had been increasing. They also noticed that, emissions from transport, storage and postal industries dominated carbon emissions from tourism, while catering, accommodation and commodity trading also emitted considerable carbon dioxide [33]. Kuo & Chen applied the life-cycle appraisal method in studying the influence of tourism on the environment of the Penghu Islands, Taiwan. Their study showed that tourism of the Islands was responsible for an annual energy consumption of 7.37 × 108 MJ, and an annual emission of 5 × 1010 g carbon dioxide [2]. Tourist industry is not a stand-alone sector in the national economic census. It's comprised of many industries, such as transport and catering, etc. It's difficult to obtain accurate data of tourist industry from the results of national economic census. The lacking of tourism statistics and materials makes it difficult to quantify energy consumption and carbon emissions of this industry. This paper uses the top–bottom method and the bottom–up method, as well as the tourism consumption coefficient stripping method proposed by Li Jiangfan et al. [35] in 1999 to calculate emissions from tourism. It's a positive exploration of quantifying emissions from tourism. In addition, the method suits China's tourist industry. Existing studies of emissions from tourism focus more on the emissions at the country level, less on those at the provincial level. In this paper, five provinces and cities are chosen as the subjects from 28 areas of which tourism is a pillar industry. They are Beijing, Shandong, Zhejiang, Hubei and Hainan. They are chosen considering geographic conditions and data availability. Carbon emissions from tourism of these provinces and cities are calculated and compared. This research touches upon areas un-explored by existing studies. 2. Research method and data source 2.1. Calculation of carbon emissions Tourist industry is comprised of several industries. They should all be considered if accurate emissions from tourism of specific areas are to be obtained. However, emission data of individual industries are lacking in China. Therefore, carbon emissions are mostly obtained through studying energy consumption, a method also applied in this paper. Carbon emissions from tourism are comprised mainly of carbon dioxide [21]. So, the level of carbon dioxide emissions is used to represent the level of carbon emissions. But in China, statistics on tourism energy consumption is not directly available from the yearbooks. Thus, three steps are taken to arrive at the subject areas' carbon emissions from tourism. First, study the energy consumption of the industries related to tourism. Separate the proportion generated by tourism. Second, multiply the energy consumptions by the energy–emission ratio to identify the carbon emissions. Third, sum these up. The equations are as follow:
C¼
2 X
Ci
ð1Þ
i¼1 2 X ðEij r j βÞ Ci ¼ i¼1
ð2Þ
Eij ¼ Eij Si
ð3Þ
C is the carbon emissions from tourism of a specific area. Ci refers to the emissions generated by industries related to tourism, such as transport, storage, postal, and wholesale, retail sales and catering industries. Eij refers to consumption of j energy caused by tourism in these industries, while rj is the coefficient used to convert it into consumption of standard coal (Table 2). βrefers to carbon dioxide emissions from a unit of standard coal. It's set as 2.45 in this paper, based on existing previous studies [34] Eij⁎is the consumption of j energy by one of the following industries: transport, storage, postal industries, wholesale, retail and catering industries. Si is the tourism consumption stripping coefficient of one of these industries. The method is further explained in the following text. Before using Eqs. (1), (2) and (3) to calculate emissions from tourism of specific areas, the tourism consumption stripping coefficients must be identified. The coefficient was proposed by Li Jiangfan. It is calculated by dividing the added value contributed by tourism by the overall added value of an industry. The added value contributed by tourism is derived by multiplying the gross value contributed by tourism by the added value ratio [35]. Once the tourism consumption stripping coefficient is identified, tourism energy consumption can be separated from the overall energy consumption of an industry. The equation is as follows: Si ¼ T i =Ri :
ð4Þ
Where Si is the tourism consumption stripping coefficient of i industry; Ri refers to the added value of i industry; Ti means the added value of i industry contributed by tourism. The added value ratio is calculated by dividing the added value by the total value of the industry. Note that, to calculate the added value of i industry contributed by tourism, the added value ratios of different sectors in i industry are assumed to be the same. It should be noted that the industry classification of national economic census is basically consistent with that of China Energy Yearbook. In national economic census, industries related to tourism include three sectors: transport, storage and postal industries; wholesale and retail industries; accommodation and catering industries. In the regional energy balance of China Energy Yearbook, they include transport, storage and postal, wholesale, retail, accommodation and catering industries. To calculate the tourism consumption stripping coefficients, the tourism consumption types in the investigation are mapped to the industry classification of the national economic census and the China Energy Yearbook. As a result, long-haul transport, city transport, postal and communication, and four other industries are grouped into the transport, storage and postal industries; tourist site visit, shopping and entertainment are grouped into the wholesale and retail industries; catering and accommodation correspond to the catering and accommodation industries. Because of different compositions, the consumption of domestic and foreign tourists are calculated separately, and added together. Considering data availability of energy consumption by industry, the wholesale, retail, catering and accommodation industries are further grouped in this study. The purpose is to improve the accuracy of the energy consumption. Therefore, the tourism consumption stripping coefficients of two industry clusters are calculated in this paper: that of the transport, storage and post industries; and that of the wholesale, retail, accommodation and catering industries. 2.2. Data source The calculation of emissions from tourism requires two data-sets, one of the tourism consumption stripping coefficient and another one of energy consumption. As to tourism consumption stripping coefficients, they are different for domestic and foreign tourists because of
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Table 2 Standard coal coefficient. Energy
Standard coal coefficient
Energy
Standard Coal coefficient
Raw Coal Briquette Fuel Oil Gasoline Kerosene Diesel Blast Furnace Gas Paraffin
0.7143 kgce/kg 0.5000–0.7000 kgce/kg 1.4286 kgce/kg 1.4714 kgce/kg 1.4714 kgce/kg 1.4571 kgce/kg 0.1286 kgce/m3 1.3648 kgce/kg
Solvent Oil Petroleum asphalt Other petroleum products Liquefied petroleum gas Oil field gas Gas field gas Thermal (equivalent) Power (equivalent)
1.4672 kgce/kg 1.3307 kgce/kg 1.400 kgce/kg 1.7143 kgce/kg 1.3300 kgce/m3 1.2143 kgce/m3 0.03412 kgce/MJ 0.1229 kgce/(kW·h)
Note: The standard coal coefficient of briquette is between 0.5000 and 0.7000. To simplify the calculation, the mean, or 0.6, is used in calculating the carbon emissions from tourism of Hainan. China Energy Yearbook only shows the total consumption of natural gas in Hainan. It doesn't contain breakdowns of different natural gas. To be consistent, the standard coal coefficient of natural gas is set as 1.2722. This is the mean of the standard coal coefficients of oil-based natural gas and gas-based natural gas. Data source: Chinese General Principles of Calculation of the Comprehensive Energy Consumption (GB/T2589-2008), and Handbook of Energy Statistics (2006).
their different consumption structures. The reports on the sample surveys of consumption structures in the five provinces and cities during 2009 and 2011 are used. Other data used include 2010 to 2012 yearbooks, industrial outputs posted on the statistic bureaus, tourism statistic communiqués, summaries and fact sheets. As to energy consumptions, they can be found in the regional energy balance sheets of China Energy Statistical Yearbook (2010–2012). In addition, standard coal coefficients are obtained from the Chinese General Principles for Calculation of the Comprehensive Energy Consumption (GB/T25892008)and the Handbook of Energy Statistics (2006).
3. Empirical results and analysis 3.1. Economic status of tourism and overall characteristics of carbon emissions from tourism in the five provinces and cities Two steps are involved in calculating the tourism consumption stripping coefficients for Beijing, Zhejiang, Shandong, Hubei and Hainan. The first step is calculating the added values of tourism during 2009 and 2011, and their proportions in local GDPs (Table 3). The results are then used to calculate the tourism consumption stripping coefficients using Eq. (4). In China, the driving effect of tourism on regional economy is commonly evaluated by the proportion of tourism turnover to GDP. But this method is flawed in that turnover measures the total output, while GDP measures added value and is only a part of the total output. In light of this, this paper uses the proportion of tourism added value to GDP instead. The results, as shown in Table 3, are distinctively different for the five provinces and cities during 2009 and 2011. The proportion of Beijing was the largest, staying above 9% in all three years. That of Shandong, on the other hand, was the smallest, between 3% and 4%. But it showed an upward trend, signaling a larger driving effect. Internationally, an industry with a proportion over 5% is qualified as a pillar industry; and over 8%, a strategic pillar industry. Thus, tourist industry was a strategic pillar industry for Beijing during 2009 and 2011. For Zhejiang and Hainan, it was a pillar industry and was on track to become their strategic pillar industries. For Hubei, it had rapidly elevated into a pillar industry. But its importance to Shandong was not showing since the proportion was between 3% and 4%. This was not consistent with the people's impression that Shandong was strong in tourism. It
might be attributed to large turnovers of the other industries in Shandong, which weaken the contribution of tourism. The tourism consumption stripping coefficients of the five provinces and cities during 2009 and 2011 are presented in Table 4. They are significantly different from each other. In 2009, Beijing had the largest coefficients: 0.387 for transport, storage and postal industries, 0.451 for wholesale, retail, accommodation and catering industries. Shandong had the smallest coefficients, 0.167 and 0.225 respectively. In 2010 and 2011, however, Zhejiang's coefficients for transport, storage and postal industries were the highest, standing at 0.334 and 0.343. Those of Shandong, 0.177 and 0.167, remained the lowest. In 2010 and 2011, Beijing was still the area with the largest tourism consumption stripping coefficients for wholesale, retail, accommodation and catering industries. The figures were 0.504 and 0.522 respectively. Meanwhile, Shandong, again, had the smallest coefficient, both of which stood at 0.214. These figures suggest that tourism made a considerable and stable contribution to both industry clusters of Beijing. On the other hand, it made insignificant contribution to those of Shandong as the coefficients were small. When examining the coefficients of the two industry clusters, one can see that tourism made an increasingly larger contribution to Zhejiang's transport, storage and postal industries. In 2010 and 2011, its coefficients were the highest. Also note that, during 2009 and 2011, the coefficients for transport, storage and postal industries were both larger than those for wholesale, retail sale, accommodation and catering industries. This meant that tourism had a larger play in the former industry cluster than the latter. This paper divides tourism related industries into two clusters: transport, storage and postal industries, wholesale, retail, accommodation and catering industries. The emissions of these two industry clusters are calculated for the five provinces and cities, and summed up to arrive at the total emissions (Table 5). They are discussed in this section while the comparison among the five provinces and cities are left for the following sections. Table 5 shows that, during 2009 and 2011, the total emissions of the subject areas kept increasing, from 49,804.45 thousand t to 65,867.65 thousand t, or by 32.85%. The emissions per tourist, on the other hand, dropped from 56.569 kg to 54.088 kg, 0r by 4.39%. This continuous drop indicates that tourists in China have stronger awareness of environmental protection. It also suggests that energy conservative technologies and new energies have been able to improve energy efficiency. Total emissions from tourism and emissions per tourist of transport, storage and postal industries
Table 3 Added values of tourism (unit: 100million RMB) and their proportion to GDP, 2009–2011 (%). Beijing
2009 2010 2011
Zhejiang
Shandong
Hubei
Hainan
Added value
Proportion to GDP
Added value
Proportion to GDP
Added value
Proportion to GDP
Added value
Proportion to GDP
Added value
Proportion to GDP
1185.3 1345.6 1563.4
9.99 9.77 9.77
1353.1 1671 2080.4
5.93 6.14 6.50
1121.8 1404.9 1732.9
3.32 3.56 3.81
549.2 794.8 1096.2
4.28 5.03 5.59
97 121 149.2
5.89 5.90 5.93
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Table 4 Tourism Consumption Stripping Coefficient, 2009–2011. Transport, storage and postal industries Beijing 2009 2010 2011
0.387 0.327 0.328
Zhejiang 0.237 0.334 0.343
Wholesale, retail sale, accommodation and catering industries
Shandong
Hubei
0.167 0.177 0.167
Hainan
0.196 0.237 0.271
0.269 0.268 0.265
during 2009 and 2011 were higher than those of wholesale, retail sale, accommodation and catering industries. This means that the former was the determinant of the overall emission levels of the five provinces and cities, and remained so during 2009 and 2011. 3.2. Comparison of emissions from tourism of the five provinces and cities Fig. 1 presents the total emissions from tourism of the five provinces and cities during 2009 and 2011. It shows that the emissions from tourism of Hainan during this period remained the lowest. In 2011, for example, its emissions were only 12.20% of that of Hubei, the province with the highest emissions. This was attributable to the small tourism turnover of Hainan. The same period saw Hubei's emissions from tourism surged from ranking No.3 in 2009 to No.1 in 2011. This posed much greater pressure on its environment. The emissions from tourism of Zhejiang ranked in the middle. They also showed a weakening growth. Their growth rate declined from 44.37% in 2010 to 1.78% in 2011. Beijing's emissions from tourism went from being No.2 in 2009, to No.4 in 2010 and 2011. It was also the only subject area whose emissions from tourism decreased. Its emissions dropped from 12,393.59 thousand t in 2009 to 12,160 thousand t in 2011. The emissions of
Beijing 0.543 0.504 0.522
Zhejiang
Shandong
0.451 0.414 0.426
0.225 0.214 0.214
Hubei 0.320 0.368 0.439
Hainan 0.320 0.323 0.338
Shandong showed a moderate growth trend. They were the highest in 2009 and 2010, and the second highest in 2011.See Fig. 2 The differences of emissions from tourism of the five provinces and cities were largely caused by varying emission composition. Table 5 shows that Shandong had the highest emissions from transport, storage and postal industries. It stood at 9415.669 thousand t in 2009, 10,659.25 thousand t in 2010, and 11,116.51 thousand t in 2011, exhibiting a moderate growth trend. On the other hand, Shandong's emissions from wholesale, retail sale, accommodation and catering remained in the third place. When the emissions from the two industry clusters were added up, Shandong's emissions from tourism went from being the highest in 2009 and 2010, to being the second highest in 2011. Hubei, on the other hand, presented a sharp contrast. The years 2010 and 2011 saw its tourism emissions from transport, storage, post and telecommunication industries increased by 26.52% and 40.4% respectively, reaching 10,412.74 thousand t in 2011. Hubei's tourism emissions from wholesale, retail sale, accommodation and catering industries went up from the second place in 2009 to the first place in 2011, and reached 8769.61 thousand t. This was 3272.15 thousand t higher than Zhejiang, the second runner. This high growth rate and absolute value brought the aggregated emissions from tourism of
Table 5 Total Emissions from Tourism and Emissions per Tourist, 2009–2011.
Beijing
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Zhejiang
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and cateringindustries Total emissions from tourism (emissions per tourist)
Shandong
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Hubei
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Hainan
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Total
Emissions from tourism (emissions per tourist) of transport, storage, and postal industries Emissions from tourism (emissions per tourist) of wholesale, retail sale, accommodation and catering industries Total emissions from tourism (emissions per tourist)
Note: Figures outside the brackets are the annual emissions from tourism in 10,000 t. Those inside are the emissions per tourist in kg.
2009
2010
2011
842.805 (51.351) 396.554 (24.162) 1239.359 (75.513) 599.092 (23.992) 472.928 (18.939) 1072.02 (42.931) 941.567 (32.234) 466.042 (15.955) 1407.609 (48.189) 586.183 (38.568) 466.494 (30.693) 1052.676 (69.261) 179.352 (79.700) 29.428 (13.077) 208.781 (92.778) 3148.999 (35.767) 1831.445 (20.802) 4980.445 (56.569)
764.843 (41.590) 363.428 (19.762) 1128.271 (61.352) 986.444 (32.680) 561.211 (18.593) 1547.655 (51.273) 1065.925 (25.304) 495.141 (11.754) 1561.066 (37.059) 741.631 (35.101) 672.066 (31.809) 1413.697 (66.910) 193.717 (74.871) 23.814 (9.204) 217.531 (84.075) 3752.561 (32.798) 2115.659 (18.491) 5868.22 (51.289)
822.638 (38.498) 394.142 (18.445) 1216.78 (56.943) 1025.449 (29.489) 549.746 (15.809) 1575.194 (45.298) 1111.651 (31.521) 530.769 (15.050) 1642.42 (46.751) 1041.274 (38.047) 876.961 (32.043) 1918.235 (70.089) 205.853 (68.587) 28.284 (9.424) 234.136 (78.010) 4206.864 (34.545) 2379.901 (19.543) 6586.765 (54.088)
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Time-wise, only Beijing and Hainan saw their emissions per tourist dropped continuously during 2009 and 2011. Zhejiang's emissions per tourist increased before they declined, showing a reverse U-shape. On the other hand, those of Shandong and Hubei showed a U-shape, as they declined before they increased. Looking into the composition of emission per tourist during 2009 and 2011, it's noted that the transport, storage and postal industries were a larger contributor than the wholesale, retail sale, accommodation and catering industries. Therefore, the former should be the focus of mitigating emissions per tourist. Actions include: improving the efficiency of traditional energy, popularizing new energy, promoting innovation of energy conservation and emission reduction technologies, strengthening tourists' awareness of resource conservation and environmental protection. Fig. 1. Total Emissions from Tourism of the five provinces and Cities (10,000 thousand ton).
4. Conclusions and policy advices
Hubei from the third place in 2009 to the first place in 2011. The results were abruptly greater pressure on the environment. Hainan's emissions from the two industry clusters were both much lower than those of the others. Besides, they had much smaller growth. Therefore, the emissions from tourism of Hainan were the lowest. Tourism emissions of Beijing and Zhejiang from the two industry clusters were ranked in the middle, resulting in their total emissions from tourism ranking in the middle. Fig. 1 shows the emissions per tourist from tourism. It can be seen that Hainan had the highest emissions per tourist, though it had the lowest total emissions from tourism. This was closely related to its location and the tourist structure. The main mean of transport was flight, resulting in relatively high energy consumption. This is also shown in Table 5. Hainan's emissions from transport, storage and postal industries during 2009 and 2011 were 79.700 kg, 74.871 kg and 68.587 kg respectively, exceeding those of the second runners by 28.349 kg (Beijing), 39.77 kg (Hubei) and 30.54 kg (Hubei) respectively. It suggests that tourism emissions from transport, storage, postal and telecommunication industries were a determinant of Hainan's emissions from tourism. From the above analysis and Table 5, it can be concluded that, Hainan had the highest emissions per tourist, at 84.954 kg. Hainan faced a relatively small pressure on environment caused by emissions from tourism. Nonetheless, adequate attention should be given to this aspect because Hainan, with the highest emissions per tourist, would face larger pressure as its tourism grows. As to the other provinces and cities, Hubei merits more attention from local government. This is because Hubei had the second highest emissions per tourist, which remained as high as 68.753 kg. Beijing ranked the third with its emissions per tourist standing at 64.603 kg. Beijing is a world famous tourist attraction, which suffers critical influence of fog and haze. To create a better tourism environment, it should keep lowering the emissions per tourist. Emissions per tourist of Zhejiang and Shandong were 46.50 kg and 43.94 kg respectively, ranking No.4 and No.5. These were relatively low.
During 2009 and 2011, there was a continuous increase in the total emissions from tourism of Beijing, Shandong, Zhejiang, Hubei and Hainan. They increased by 32.85%, from 49,804.45 thousand t to 65,867.65 thousand t. Meanwhile, emissions per tourist had lowered by 4.39%, from 56.569 kg to 54.088 kg. The increase in total emissions was caused by continuous increase in tourist number. The mitigation of emissions per tourist was attributable to the tourists' strengthened awareness of environmental protection, and innovation of energy conservation technologies. New technologies and devices, such as natural gas vehicles and solar power illuminators were the technical support for mitigating emissions per tourist. On the other hand, energy conservation and emission mitigation obligations rendered policy support. The total emissions from tourism of the five provinces and cities during 2009 and 2011 are compared. The results show that emissions from tourism of Hainan remained the lowest. That of Hubei surged from the third place in 2009 to the first place in 2011. Beijing was the only area to show an un-disturbed downward trend. Its emissions from tourism dropped from 12,393.59 thousand t in 2009 to 12,160 thousand t in 2011. Hainan's emissions from tourism were the lowest because its tourist numbers and economic scale were the smallest. Its tourism turnovers were only RMB 21.17 billion in 2009 and RMB 32.404 billion in 2011. These were much lower than those of the other four areas. The rapid increase of Hubei's emissions from tourism was brought by the booming tourism. Hubei's tourism turnover was up by 66% during 2009 and 2011, from RMB 120 billion to RMB 199.2 billion. The emissions per tourist of the five subject areas during 2009 and 2011 are also compared. The results show that Hainan, though with the lowest total emissions, had the highest emissions per tourist. Coming up next was Hubei, with its average emissions per tourist standing at 68.753 kg. Following were Beijing, Zhejiang and Shandong, with their emissions per tourist at 64.60 kg, 46.50 kg and 43.94 kg respectively. The emissions per tourist of Hainan were the highest because most tourists to Hainan traveled by air, a carbon-intensive mean of transport.
Fig. 2. Emissions per Tourist (kg).
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Another cause was the large number of high-end hotels and tourism complexes. Time-wise, only Beijing and Hainan saw their emissions per tourist continued to drop during 2009 and 2011. Zhejiang's emissions per tourist increased before they declined, showing a reverse U-shape. On the other hand, those of Shandong and Hubei showed a U-shape, as they declined before they increased. Looking into the composition of emissions per tourist during 2009 and 2011, it's noted that the transport, storage and postal industries were a larger contributor than the wholesale, retail sale, accommodation and catering industries. The former was the determinant of total emissions of the five provinces and cities, and remained so throughout the period. In the future, these five provinces and cities should make energy conservation and emission mitigation a priority of sustainable tourism development. They should also take this opportunity to upgrade and transform tourism. First, governments should take actions favorable to local situations to lower emissions without sacrificing tourism development. Their rational choices will contribute to sustainable tourism development in China. Second, governments should enhance tourists' awareness of environmental protection and advocate low-carbon tourism. They should adopt political and fiscal measures to support tourism companies' efforts in innovating energy conservation technologies and tourism services. Third, they should attach great importance to emission mitigation in the transport, storage and postal industries. Other actions include: promoting changes in tourism activities, developing energy efficient vehicles, improving the efficiency of traditional energy in the transport industry and popularizing new energy. These shall lower total emissions from tourism. Fourth, governments should view tourist industry and environment from a dynamic perspective. This is because, during 2009 and 2011, only Beijing and Hainan saw their emissions per tourist continuously decreased. The five provinces and cities should make use of existing regional cooperation platforms to boost exchanges on mitigating emissions from tourism. The purpose is to form a normalized emission mitigation mechanism based on exchanges, which will create a win–win situation. Acknowledgements This paper is Supported by the National Natural Science Foundation of China (Grant No. 41101044); National Social Science Youth Foundation (Grant No. 14CGL022). References [1] IEA(International Energy Agency), World energy outlook[R], International Energy Agency, Paris, 2014. [2] L. Yiwen, H. Zongyi, Research on regional difference about carbon emission efficiency in China [J], J. Shanxi Univ. Finance Econ. 37 (2) (2015) 23–32. [3] W.P. Shi Peihua, The basic ideas and initiatives focused on the development of low-carbon tourism [N], China Tourism News, 01-19-2010. [4] Shi Peihua,Feng Ling,Wu Pu. Energy saving and low-carbon of tourism industry development [M]. Beijing: Tourism Press,2010: 37-41. [5] M.K. Loke, P.S. Leung, K.A. Tucker, Energy and tourism in Hawaii [J], Ann. Tour. Res. 24 (2) (1997) 390–401.
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