资源与生态学报 ›› 2021, Vol. 12 ›› Issue (1): 56-67.DOI: 10.5814/j.issn.1674-764x.2021.01.006
收稿日期:
2020-06-16
接受日期:
2020-09-01
出版日期:
2021-01-30
发布日期:
2021-03-30
通讯作者:
李涛
HAN Zhiyong1(), LI Tao2,3,*(
), LIU Ximei1
Received:
2020-06-16
Accepted:
2020-09-01
Online:
2021-01-30
Published:
2021-03-30
Contact:
LI Tao
About author:
HAN Zhiyong, E-mail: Supported by:
摘要:
据统计,旅游碳排放已占全球碳排放总量的4.9%,加强对其研究和控制是关乎人类能否可持续发展的重要命题。本文基于地理学的视角,研究了中国2007年到2017年间30个省、市、自治区入境旅游碳足迹时空分布的特征和演化规律。在利用碳足迹综合计算模型和空间分析方法基础上,深入揭示了中国入境旅游碳足迹的时空分布特征及演化规律。结果表明,2007年到2017年间,中国入境旅游碳足迹呈现急速上升又稍有回落的趋势,总量从562.30万t上升到1088.09万t,增长1.94倍,其中交通和邮电业占比最大;近十年来我国多数省市的入境旅游碳足迹变异程度不高,维持在较平稳的状态;空间维度上,则呈现东南向西北方向递减趋势。
韩志勇, 李涛, 刘喜梅. 中国入境旅游碳足迹时空特征与演化研究[J]. 资源与生态学报, 2021, 12(1): 56-67.
HAN Zhiyong, LI Tao, LIU Ximei. Temporal and Spatial Characteristics and Evolution of China’s Inbound Tourism Carbon Footprint[J]. Journal of Resources and Ecology, 2021, 12(1): 56-67.
Province | Average carbon emission coefficient | Province | Average carbon emission coefficient |
---|---|---|---|
Guizhou | 23.82 | Hubei | 22.06 |
Gansu | 23.64 | Tianjin | 21.78 |
Liaoning | 23.50 | Fujian | 21.68 |
Shanxi | 23.50 | Shandong | 21.61 |
Inner Mongolia | 23.39 | Hainan | 20.82 |
Hebei | 23.16 | Jiangsu | 20.80 |
Henan | 23.10 | Beijing | 20.72 |
Hunan | 23.04 | Guangxi | 20.65 |
Xinjiang | 23.03 | Chongqing | 20.61 |
Yunnan | 22.85 | Zhejiang | 20.59 |
Anhui | 22.41 | Shaanxi | 20.54 |
Jilin | 22.37 | Guangdong | 20.45 |
Heilongjiang | 22.21 | Sichuan | 20.32 |
Ningxia | 22.18 | Qinghai | 20.25 |
Jiangxi | 22.08 | Shanghai | 20.13 |
Table 1 Carbon emission coefficients of comprehensive energy consumption for the provinces of China (g MJ-1)
Province | Average carbon emission coefficient | Province | Average carbon emission coefficient |
---|---|---|---|
Guizhou | 23.82 | Hubei | 22.06 |
Gansu | 23.64 | Tianjin | 21.78 |
Liaoning | 23.50 | Fujian | 21.68 |
Shanxi | 23.50 | Shandong | 21.61 |
Inner Mongolia | 23.39 | Hainan | 20.82 |
Hebei | 23.16 | Jiangsu | 20.80 |
Henan | 23.10 | Beijing | 20.72 |
Hunan | 23.04 | Guangxi | 20.65 |
Xinjiang | 23.03 | Chongqing | 20.61 |
Yunnan | 22.85 | Zhejiang | 20.59 |
Anhui | 22.41 | Shaanxi | 20.54 |
Jilin | 22.37 | Guangdong | 20.45 |
Heilongjiang | 22.21 | Sichuan | 20.32 |
Ningxia | 22.18 | Qinghai | 20.25 |
Jiangxi | 22.08 | Shanghai | 20.13 |
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|
(TPTS)sc | 6.85 | 6.30 | 5.80 | 5.63 | 5.26 | 5.23 | 4.69 | 4.80 | 10.11 | 10.36 | 9.39 |
(ARCS)sc | 5.80 | 3.66 | 3.27 | 3.24 | 2.54 | 2.14 | 1.98 | 1.93 | 3.93 | 3.31 | 3.33 |
(ECLS)sc | 1.47 | 1.14 | 0.96 | 0.92 | 0.81 | 0.70 | 0.63 | 0.60 | 1.01 | 1.14 | 1.02 |
Table 2 Stripping coefficient of inbound tourism consumption (unit: %)
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|
(TPTS)sc | 6.85 | 6.30 | 5.80 | 5.63 | 5.26 | 5.23 | 4.69 | 4.80 | 10.11 | 10.36 | 9.39 |
(ARCS)sc | 5.80 | 3.66 | 3.27 | 3.24 | 2.54 | 2.14 | 1.98 | 1.93 | 3.93 | 3.31 | 3.33 |
(ECLS)sc | 1.47 | 1.14 | 0.96 | 0.92 | 0.81 | 0.70 | 0.63 | 0.60 | 1.01 | 1.14 | 1.02 |
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|
(TPTS)cf | 451.66 | 316.48 | 300.95 | 334.27 | 341.96 | 372.79 | 357.66 | 382.51 | 848.85 | 899.97 | 867.81 |
(ARCS)cf | 41.52 | 45.97 | 45.89 | 55.77 | 50.88 | 46.89 | 46.07 | 45.93 | 98.32 | 87.14 | 91.01 |
(ECLS)cf | 69.11 | 79.93 | 71.55 | 73.43 | 70.33 | 64.63 | 62.75 | 61.76 | 110.40 | 135.14 | 129.23 |
Total | 562.30 | 442.37 | 418.38 | 463.48 | 463.18 | 484.31 | 466.48 | 490.20 | 1057.56 | 1122.25 | 1088.09 |
Table 3 Carbon footprint of inbound tourism in China from 2007 to 2017 (′104 t )
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|
(TPTS)cf | 451.66 | 316.48 | 300.95 | 334.27 | 341.96 | 372.79 | 357.66 | 382.51 | 848.85 | 899.97 | 867.81 |
(ARCS)cf | 41.52 | 45.97 | 45.89 | 55.77 | 50.88 | 46.89 | 46.07 | 45.93 | 98.32 | 87.14 | 91.01 |
(ECLS)cf | 69.11 | 79.93 | 71.55 | 73.43 | 70.33 | 64.63 | 62.75 | 61.76 | 110.40 | 135.14 | 129.23 |
Total | 562.30 | 442.37 | 418.38 | 463.48 | 463.18 | 484.31 | 466.48 | 490.20 | 1057.56 | 1122.25 | 1088.09 |
Province | Correction factor | Province | Correction factor |
---|---|---|---|
Beijing | 0.95 | Henan | 1.05 |
Tianjin | 0.99 | Hubei | 1.01 |
Hebei | 1.06 | Hunan | 1.05 |
Shanxi | 1.07 | Guangdong | 0.93 |
Inner Mongolia | 1.07 | Guangxi | 0.94 |
Liaoning | 1.07 | Hainan | 0.95 |
Jilin | 1.02 | Chongqing | 0.94 |
Heilongjiang | 1.01 | Sichuan | 0.93 |
Shanghai | 0.92 | Guizhou | 1.09 |
Jiangsu | 0.95 | Yunnan | 1.04 |
Zhejiang | 0.94 | Shaanxi | 0.94 |
Anhui | 1.02 | Gansu | 1.08 |
Fujian | 0.99 | Qinghai | 0.92 |
Jiangxi | 1.01 | Ningxia | 1.01 |
Shandong | 0.99 | Xinjiang | 1.05 |
Table 4 Correction factors of carbon emission intensity for the provinces and cities of China
Province | Correction factor | Province | Correction factor |
---|---|---|---|
Beijing | 0.95 | Henan | 1.05 |
Tianjin | 0.99 | Hubei | 1.01 |
Hebei | 1.06 | Hunan | 1.05 |
Shanxi | 1.07 | Guangdong | 0.93 |
Inner Mongolia | 1.07 | Guangxi | 0.94 |
Liaoning | 1.07 | Hainan | 0.95 |
Jilin | 1.02 | Chongqing | 0.94 |
Heilongjiang | 1.01 | Sichuan | 0.93 |
Shanghai | 0.92 | Guizhou | 1.09 |
Jiangsu | 0.95 | Yunnan | 1.04 |
Zhejiang | 0.94 | Shaanxi | 0.94 |
Anhui | 1.02 | Gansu | 1.08 |
Fujian | 0.99 | Qinghai | 0.92 |
Jiangxi | 1.01 | Ningxia | 1.01 |
Shandong | 0.99 | Xinjiang | 1.05 |
Province | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 65.65 | 47.03 | 40.71 | 42.63 | 39.84 | 35.51 | 32.68 | 32.93 | 64.44 | 72.67 | 65.35 |
Tianjin | 11.73 | 11.10 | 11.62 | 12.61 | 13.57 | 16.14 | 18.56 | 22.47 | 48.51 | 53.59 | 50.24 |
Hebei | 4.95 | 3.23 | 3.22 | 3.31 | 3.68 | 4.20 | 4.46 | 4.27 | 7.85 | 8.85 | 8.24 |
Shanxi | 3.60 | 3.60 | 4.01 | 4.45 | 4.73 | 5.63 | 6.36 | 2.28 | 4.72 | 5.16 | 5.06 |
Inner Mongolia | 8.82 | 6.87 | 5.89 | 5.74 | 5.57 | 6.01 | 7.40 | 8.09 | 15.20 | 18.43 | 17.91 |
Liaoning | 19.96 | 18.26 | 19.67 | 21.65 | 22.63 | 25.53 | 26.88 | 13.11 | 25.97 | 29.65 | 25.69 |
Jilin | 2.78 | 2.41 | 2.45 | 2.78 | 3.06 | 3.68 | 4.06 | 4.50 | 10.94 | 12.24 | 10.53 |
Heilongjiang | 9.87 | 9.84 | 6.40 | 6.91 | 7.23 | 6.18 | 4.42 | 4.32 | 5.93 | 7.04 | 6.55 |
Shanghai | 65.08 | 50.95 | 43.07 | 52.06 | 41.10 | 36.81 | 34.73 | 38.89 | 79.67 | 89.39 | 82.91 |
Jiangsu | 49.92 | 41.08 | 37.68 | 40.58 | 41.74 | 43.61 | 16.28 | 21.75 | 49.55 | 54.73 | 53.65 |
Zhejiang | 38.57 | 31.70 | 29.94 | 33.00 | 33.20 | 35.31 | 36.52 | 40.86 | 94.40 | 44.55 | 45.40 |
Anhui | 5.33 | 5.18 | 5.72 | 6.48 | 9.38 | 11.66 | 12.24 | 14.22 | 34.25 | 39.41 | 39.69 |
Fujian | 32.54 | 26.41 | 25.42 | 26.33 | 27.97 | 30.49 | 32.61 | 36.72 | 81.43 | 99.37 | 101.15 |
Jiangxi | 2.99 | 2.83 | 2.89 | 3.12 | 3.25 | 3.56 | 3.81 | 4.24 | 8.45 | 8.93 | 8.55 |
Shandong | 20.21 | 15.30 | 17.21 | 18.99 | 19.57 | 21.03 | 19.41 | 17.37 | 42.27 | 45.79 | 42.17 |
Henan | 5.08 | 4.40 | 4.51 | 4.70 | 4.50 | 4.70 | 5.01 | 4.29 | 9.73 | 10.33 | 9.40 |
Hubei | 6.30 | 4.97 | 5.08 | 6.76 | 7.36 | 8.83 | 8.84 | 9.42 | 24.91 | 28.57 | 28.55 |
Hunan | 10.24 | 7.24 | 6.99 | 8.51 | 8.30 | 7.12 | 6.23 | 6.36 | 13.35 | 16.01 | 18.35 |
Guangdong | 123.16 | 95.51 | 92.50 | 103.27 | 100.95 | 106.26 | 109.50 | 120.65 | 247.00 | 262.80 | 250.98 |
Guangxi | 8.24 | 6.32 | 5.99 | 6.79 | 7.71 | 8.79 | 10.51 | 11.20 | 26.73 | 30.92 | 30.42 |
Hainan | 4.34 | 3.33 | 2.60 | 2.74 | 2.78 | 2.41 | 2.31 | 1.93 | 3.49 | 5.04 | 8.72 |
Chongqing | 5.45 | 4.72 | 4.99 | 5.91 | 7.08 | 8.01 | 8.60 | 9.63 | 20.44 | 24.05 | 24.68 |
Sichuan | 7.20 | 1.59 | 2.64 | 2.93 | 4.28 | 5.40 | 5.11 | 6.01 | 16.20 | 22.23 | 18.07 |
Guizhou | 2.13 | 1.42 | 1.19 | 1.26 | 1.14 | 1.34 | 1.58 | 1.55 | 3.72 | 4.16 | 4.15 |
Yunnan | 13.59 | 11.72 | 12.08 | 12.33 | 13.05 | 14.81 | 18.18 | 19.08 | 44.37 | 48.60 | 49.88 |
Shaanxi | 8.70 | 6.90 | 7.14 | 8.51 | 9.44 | 10.92 | 11.32 | 12.53 | 27.75 | 33.23 | 34.15 |
Gansu | 1.15 | 0.19 | 0.13 | 0.14 | 0.15 | 0.18 | 0.16 | 0.08 | 0.23 | 0.31 | 0.30 |
Qinghai | 0.22 | 0.11 | 0.14 | 0.17 | 0.19 | 0.16 | 0.13 | 0.17 | 0.53 | 0.62 | 0.48 |
Ningxia | 0.04 | 0.03 | 0.04 | 0.05 | 0.05 | 0.04 | 0.09 | 0.14 | 0.31 | 0.62 | 0.51 |
Xinjiang | 2.58 | 1.59 | 1.42 | 1.74 | 3.80 | 4.22 | 4.43 | 3.95 | 8.65 | 8.26 | 11.48 |
Table 5 Calculation results of the carbon footprint of inbound tourism in China from 2007 to 2017 (′104 t)
Province | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|---|
Beijing | 65.65 | 47.03 | 40.71 | 42.63 | 39.84 | 35.51 | 32.68 | 32.93 | 64.44 | 72.67 | 65.35 |
Tianjin | 11.73 | 11.10 | 11.62 | 12.61 | 13.57 | 16.14 | 18.56 | 22.47 | 48.51 | 53.59 | 50.24 |
Hebei | 4.95 | 3.23 | 3.22 | 3.31 | 3.68 | 4.20 | 4.46 | 4.27 | 7.85 | 8.85 | 8.24 |
Shanxi | 3.60 | 3.60 | 4.01 | 4.45 | 4.73 | 5.63 | 6.36 | 2.28 | 4.72 | 5.16 | 5.06 |
Inner Mongolia | 8.82 | 6.87 | 5.89 | 5.74 | 5.57 | 6.01 | 7.40 | 8.09 | 15.20 | 18.43 | 17.91 |
Liaoning | 19.96 | 18.26 | 19.67 | 21.65 | 22.63 | 25.53 | 26.88 | 13.11 | 25.97 | 29.65 | 25.69 |
Jilin | 2.78 | 2.41 | 2.45 | 2.78 | 3.06 | 3.68 | 4.06 | 4.50 | 10.94 | 12.24 | 10.53 |
Heilongjiang | 9.87 | 9.84 | 6.40 | 6.91 | 7.23 | 6.18 | 4.42 | 4.32 | 5.93 | 7.04 | 6.55 |
Shanghai | 65.08 | 50.95 | 43.07 | 52.06 | 41.10 | 36.81 | 34.73 | 38.89 | 79.67 | 89.39 | 82.91 |
Jiangsu | 49.92 | 41.08 | 37.68 | 40.58 | 41.74 | 43.61 | 16.28 | 21.75 | 49.55 | 54.73 | 53.65 |
Zhejiang | 38.57 | 31.70 | 29.94 | 33.00 | 33.20 | 35.31 | 36.52 | 40.86 | 94.40 | 44.55 | 45.40 |
Anhui | 5.33 | 5.18 | 5.72 | 6.48 | 9.38 | 11.66 | 12.24 | 14.22 | 34.25 | 39.41 | 39.69 |
Fujian | 32.54 | 26.41 | 25.42 | 26.33 | 27.97 | 30.49 | 32.61 | 36.72 | 81.43 | 99.37 | 101.15 |
Jiangxi | 2.99 | 2.83 | 2.89 | 3.12 | 3.25 | 3.56 | 3.81 | 4.24 | 8.45 | 8.93 | 8.55 |
Shandong | 20.21 | 15.30 | 17.21 | 18.99 | 19.57 | 21.03 | 19.41 | 17.37 | 42.27 | 45.79 | 42.17 |
Henan | 5.08 | 4.40 | 4.51 | 4.70 | 4.50 | 4.70 | 5.01 | 4.29 | 9.73 | 10.33 | 9.40 |
Hubei | 6.30 | 4.97 | 5.08 | 6.76 | 7.36 | 8.83 | 8.84 | 9.42 | 24.91 | 28.57 | 28.55 |
Hunan | 10.24 | 7.24 | 6.99 | 8.51 | 8.30 | 7.12 | 6.23 | 6.36 | 13.35 | 16.01 | 18.35 |
Guangdong | 123.16 | 95.51 | 92.50 | 103.27 | 100.95 | 106.26 | 109.50 | 120.65 | 247.00 | 262.80 | 250.98 |
Guangxi | 8.24 | 6.32 | 5.99 | 6.79 | 7.71 | 8.79 | 10.51 | 11.20 | 26.73 | 30.92 | 30.42 |
Hainan | 4.34 | 3.33 | 2.60 | 2.74 | 2.78 | 2.41 | 2.31 | 1.93 | 3.49 | 5.04 | 8.72 |
Chongqing | 5.45 | 4.72 | 4.99 | 5.91 | 7.08 | 8.01 | 8.60 | 9.63 | 20.44 | 24.05 | 24.68 |
Sichuan | 7.20 | 1.59 | 2.64 | 2.93 | 4.28 | 5.40 | 5.11 | 6.01 | 16.20 | 22.23 | 18.07 |
Guizhou | 2.13 | 1.42 | 1.19 | 1.26 | 1.14 | 1.34 | 1.58 | 1.55 | 3.72 | 4.16 | 4.15 |
Yunnan | 13.59 | 11.72 | 12.08 | 12.33 | 13.05 | 14.81 | 18.18 | 19.08 | 44.37 | 48.60 | 49.88 |
Shaanxi | 8.70 | 6.90 | 7.14 | 8.51 | 9.44 | 10.92 | 11.32 | 12.53 | 27.75 | 33.23 | 34.15 |
Gansu | 1.15 | 0.19 | 0.13 | 0.14 | 0.15 | 0.18 | 0.16 | 0.08 | 0.23 | 0.31 | 0.30 |
Qinghai | 0.22 | 0.11 | 0.14 | 0.17 | 0.19 | 0.16 | 0.13 | 0.17 | 0.53 | 0.62 | 0.48 |
Ningxia | 0.04 | 0.03 | 0.04 | 0.05 | 0.05 | 0.04 | 0.09 | 0.14 | 0.31 | 0.62 | 0.51 |
Xinjiang | 2.58 | 1.59 | 1.42 | 1.74 | 3.80 | 4.22 | 4.43 | 3.95 | 8.65 | 8.26 | 11.48 |
Province | Mean of carbon footprint (×104 t) | Coefficient of variation | Province | Mean of carbon footprint (×104 t) | Coefficient of variation |
---|---|---|---|---|---|
Guangdong | 161.26 | 0.41 | Hunan | 10.87 | 0.37 |
Shanghai | 61.46 | 0.31 | Inner Mongolia | 10.59 | 0.45 |
Beijing | 53.94 | 0.26 | Sichuan | 9.17 | 0.73 |
Fujian | 52.04 | 0.56 | Heilongjiang | 7.47 | 0.23 |
Zhejiang | 46.34 | 0.37 | Henan | 6.67 | 0.35 |
Jiangsu | 45.06 | 0.26 | Jilin | 5.94 | 0.61 |
Shandong | 27.93 | 0.40 | Hebei | 5.63 | 0.36 |
Tianjin | 27.02 | 0.61 | Jiangxi | 5.26 | 0.46 |
Yunnan | 25.77 | 0.58 | Xinjiang | 5.21 | 0.61 |
Liaoning | 24.90 | 0.18 | Shanxi | 4.96 | 0.21 |
Anhui | 18.36 | 0.72 | Hainan | 3.97 | 0.46 |
Shaanxi | 17.06 | 0.60 | Guizhou | 2.36 | 0.50 |
Guangxi | 15.36 | 0.62 | Gansu | 0.30 | 0.94 |
Hubei | 13.96 | 0.65 | Qinghai | 0.29 | 0.60 |
Chongqing | 12.36 | 0.60 | Ningxia | 0.19 | 1.04 |
Table 6 Mean and CV of carbon footprint of inbound tourism in China's provinces from 2007 to 2017
Province | Mean of carbon footprint (×104 t) | Coefficient of variation | Province | Mean of carbon footprint (×104 t) | Coefficient of variation |
---|---|---|---|---|---|
Guangdong | 161.26 | 0.41 | Hunan | 10.87 | 0.37 |
Shanghai | 61.46 | 0.31 | Inner Mongolia | 10.59 | 0.45 |
Beijing | 53.94 | 0.26 | Sichuan | 9.17 | 0.73 |
Fujian | 52.04 | 0.56 | Heilongjiang | 7.47 | 0.23 |
Zhejiang | 46.34 | 0.37 | Henan | 6.67 | 0.35 |
Jiangsu | 45.06 | 0.26 | Jilin | 5.94 | 0.61 |
Shandong | 27.93 | 0.40 | Hebei | 5.63 | 0.36 |
Tianjin | 27.02 | 0.61 | Jiangxi | 5.26 | 0.46 |
Yunnan | 25.77 | 0.58 | Xinjiang | 5.21 | 0.61 |
Liaoning | 24.90 | 0.18 | Shanxi | 4.96 | 0.21 |
Anhui | 18.36 | 0.72 | Hainan | 3.97 | 0.46 |
Shaanxi | 17.06 | 0.60 | Guizhou | 2.36 | 0.50 |
Guangxi | 15.36 | 0.62 | Gansu | 0.30 | 0.94 |
Hubei | 13.96 | 0.65 | Qinghai | 0.29 | 0.60 |
Chongqing | 12.36 | 0.60 | Ningxia | 0.19 | 1.04 |
Fig. 3 Spatial distribution of the inbound tourism carbon footprint in 2007, 2010, 2014 and 2017. Note: Research data has excluded Chinese Tibet, Hong Kong, Macao and Taiwan.
[1] |
Becken S, Patterson M. 2006. Measuring national carbon dioxide emissions from tourism as a key step towards achieving sustainable tourism. Journal of Sustainable Tourism, 14(14):323-338.
DOI URL |
[2] |
Becken S, Shuker J. 2019. A framework to help destinations manage carbon risk from aviation emissions. Tourism Management, 71:294-304.
DOI URL |
[3] |
Browne D, O’Regan B, Moles R. 2009. Use of ecological footprinting to explore alternative domestic energy and electricity policy scenarios in an Irish city-region. Energy Policy, 37(37):2205-2213.
DOI URL |
[4] |
Cadarso M Á, Gómez N, López L A, et al. 2016. Calculating tourism’s carbon footprint: Measuring the impact of investments. Journal of Cleaner Production, 111:529-537.
DOI URL |
[5] | Cao H, Yan S, Lei D. 2014. Evaluation of tourism carbon footprint in Fujian Province in the past ten years. Journal of Safety and Environment, 14(6): 312-317. (in Chinese) |
[6] |
Cheng Q, Su B, Tan J. 2013. Developing an evaluation index system for low-carbon tourist attractions in China—A case study examining the Xixi wetland. Tourism Management, 36:314-320.
DOI URL |
[7] |
Ciers J, Mandic A, Toth L, et al. 2019. Carbon footprint of academic air travel: A case study in Switzerland. Sustainability, 11(1):80. DOI: 10.3390/su11010080.
DOI URL |
[8] |
De Grosbois D, Fennell D. 2011. Carbon footprint of the global hotel companies: Comparison of methodologies and results. Tourism Recreation Research, 36(36):231-245.
DOI URL |
[9] | Dong H, Liu Q, Zhou L. 2018. Dynamic comparison of tourism carbon footprint and carbon carrying capacity in Jiangsu Province. Ecological Economy, 34(11): 185-189. (in Chinese) |
[10] |
Dwyer L, Forsyth P, Spurr R, et al. 2010. Estimating the carbon footprint of Australian tourism. Journal of Sustainable Tourism, 18(18):355-376.
DOI URL |
[11] |
Ewing B, Moore D, Goldfinger S. 2010. Ecological footprint atlas 2010. Bulletin of the World Health Organization, 79(10):971. DOI: 10.1590/ S0042-96862001001000011.
URL PMID |
[12] |
Fan Z, Lei Y, Wu S. 2019. Research on the changing trend of the carbon footprint of residents’ consumption in Beijing. Environmental Science and Pollution Research, 26(26):4078-4090.
DOI URL |
[13] |
Filimonau V, Dickinson J E, Robbins D, et al. 2011. A critical review of methods for tourism climate change appraisal: Life cycle assessment as a new approach. Journal of Sustainable Tourism, 19(19):301-324.
DOI URL |
[14] |
Haider S, Akram V. 2019. Club convergence analysis of ecological and carbon footprint: Evidence from a cross-country analysis. Carbon Management, 10(10):451-463.
DOI URL |
[15] | Hu A H, Huang C Y, Chen C F, et al. 2015. Assessing carbon footprint in the life cycle of accommodation services: The case of an international tourist hotel. International Journal of Sustainable Development & World Ecology, 22(22):313-323. |
[16] |
Huang Q, Kang J, Huang C. 2015. Construction of the comprehensive energy consumption assessment model for star-rated hotels and the difference analysis. Journal of Resources and Ecology, 6(6):164-171.
DOI URL |
[17] |
Juvan E, Dolnicar S. 2014. Can tourists easily choose a low carbon footprint vacation? Journal of Sustainable Tourism, 22(2): 175-194.
DOI URL |
[18] |
Lenzen M, Sun Y Y, Faturay F, et al. 2018. The carbon footprint of global tourism. Nature Climate Change, 8(8):522-528.
DOI URL |
[19] | Li B H, Liu Y P, Dou Y Q. 2012. Analysis of carbon footprint assessment and influencing factors of tourism transportation system in tourist scenic spots—Taking Nanyue Hengshan as an example. Resource Science, 34(5): 956-963. (in Chinese) |
[20] | Liu J, Wen D, Wang Y, et al. 2019. Measuring of tourism eco-efficiency and its comparative research based on carbon emissions. Acta Ecological Sinica, 39(6): 1979-1992. (in Chinese) |
[21] |
Liu H, Xi Y, Guo J E, et al. 2010. Energy embodied in the international trade of China: An energy input-output analysis. Energy Policy, 38(38):3957-3964.
DOI URL |
[22] |
Liu J, Feng T, Yang X. 2011. The energy requirements and carbon dioxide emissions of tourism industry of Western China: A case of Chengdu City. Renewable and Sustainable Energy Reviews, 15(15):2887-2894.
DOI URL |
[23] |
Li Y, Zhao J, Yang T, et al. 2013. Structural decomposition analysis of the decline in China’s CO2 emission intensity 2005-2010. Journal of Resources and Ecology, 4(4):311-316.
DOI URL |
[24] |
Meng W, Xu L, Hu B, et al. 2016. Quantifying direct and indirect carbon dioxide emissions of the Chinese tourism industry. Journal of Cleaner Production, 126:586-594.
DOI URL |
[25] | National Tourism Administration of the People’s Republic of China. 2019. Tourist sample survey data. Beijing, China: China Tourism Press. |
[26] |
Ozturk I, Al-Mulali U, Saboori B. 2016. Investigating the environmental Kuznets curve hypothesis: The role of tourism and ecological footprint. Environmental Science and Pollution Research, 23(2): 1916-1928.
DOI URL PMID |
[27] | Pan Z Q, Liang B. 2016. Research on space-time heterogeneity of tourism industry carbon emission intensity distribution and influencing factors: Analysis of panel data from 30 provinces from 2005 to 2014. Human Geography, 31(6): 152-158. (in Chinese) |
[28] |
Peeters P, Higham J, Cohen S, et al. 2019. Desirable tourism transport futures. Journal of Sustainable Tourism, 27(27):173-188.
DOI URL |
[29] |
Perch-Nielsen S, Sesartic A, Stucki M. 2010. The greenhouse gas intensity of the tourism sector: The case of Switzerland. Environmental Science & Policy, 13(13):131-140.
DOI URL |
[30] |
Peters G P. 2010. Carbon footprints and embodied carbon at multiple scales. Current Opinion in Environmental Sustainability, 2(2):245-250.
DOI URL |
[31] |
Qureshi M I, Elashkar E E, Shoukry A M, et al. 2019. Measuring the ecological footprint of inbound and outbound tourists: Evidence from a panel of 35 countries. Clean Technologies and Environmental Policy, 21(21):1-19.
DOI URL |
[32] |
Ren Y, Zhao C, Fu J. 2019. Dynamic study on tourism carbon footprint and carbon carrying capacity in Sichuan Province. Journal of Geoscience and Environment Protection, 7(7):14-24.
DOI URL |
[33] |
Rico A, Martínez-Blanco J, Montlleó M, et al. 2019. Carbon footprint of tourism in Barcelona. Tourism Management, 70(70):491-504.
DOI URL |
[34] |
Roukounakis N, Valkouma E, Giama E, et al. 2020. The development of a carbon footprint model for the calculation of GHG emissions from highways: The case of Egnatia Odos in Greece. International Journal of Sustainable Transportation, 14(14):74-83.
DOI URL |
[35] |
Sharp H, Grundius J, Heinonen J. 2016. Carbon footprint of inbound tourism to Iceland: A consumption-based life-cycle assessment including direct and indirect emissions. Sustainability, 8(11):1147. DOI: 10.3390/su8111147.
DOI URL |
[36] |
Sun Y Y. 2014. A framework to account for the tourism carbon footprint at island destinations. Tourism Management, 45(45):16-27.
DOI URL |
[37] |
Sun Y Y. 2019. Global value chains and national tourism carbon competitiveness. Journal of Travel Research, 58(58):808-823.
DOI URL |
[38] |
Sun Y Y, Pratt S. 2014. The economic, carbon emission, and water impacts of Chinese visitors to Taiwan: Eco-efficiency and impact evaluation. Journal of Travel Research, 53(53):733-746.
DOI URL |
[39] |
Sun Y Y, Lenzen M, Liu B J. 2019. The national tourism carbon emission inventory: Its importance, applications and allocation frameworks. Journal of Sustainable Tourism, 27(27):360-379.
DOI URL |
[40] |
Thongdejsri M, Nitivattananon V. 2019. Assessing impacts of implementing low-carbon tourism program for sustainable tourism in a world heritage city. Tourism Review, 74(74):216-234.
DOI URL |
[41] |
Tang C, Zhong L, Jiang Q. 2018. Energy efficiency and carbon efficiency of tourism industry in destination. Energy Efficiency, 11(11):539-558.
DOI URL |
[42] |
Tang C, Wan Z, Ng P, et al. 2019. Temporal and spatial evolution of carbon emissions and their influencing factors for tourist attractions at Heritage tourist destinations. Sustainability, 11(21):5944. DOI: 10.3390/su11215944.
DOI URL |
[43] |
Tang C, Zhong L, Fan W, et al. 2015. Energy consumption and carbon emission for tourism transport in world heritage sites: A case of the Wulingyuan area in China. Natural Resources Forum, 39(39):134-150.
DOI URL |
[44] | UNWTO. 2015. UNWTO annual report 2016, Madrid. DOI: 10.1811/ 978928418039. |
[45] |
Wang S, Hu Y, He H, et al. 2017. Progress and prospects for tourism footprint research. Sustainability, 9(10):1847. DOI: 10.3390/su9101847.
DOI URL |
[46] |
Wang S, Wang G, Fang Y. 2016. Factors influencing the energy efficiency of tourism transport in China. Journal of Resources and Ecology, 7(7):246-253.
DOI URL |
[47] |
Whittlesea E R, Owen A. 2012. Towards a low carbon future—The development and application of REAP Tourism, a destination footprint and scenario tool. Journal of Sustainable Tourism, 20(20):845-865.
DOI URL |
[48] |
Wicker P. 2019. The carbon footprint of active sport participants. Sport Management Review, 22(22):513-526.
DOI URL |
[49] |
Wiedmann T. 2009. Editorial: Carbon footprint and input-output analysis—An introduction. Economic Systems Research, 21(21):175-186.
DOI URL |
[50] |
Xiong Z, Li H. 2019. Ecological deficit tax: A tax design and simulation of compensation for ecosystem service value based on ecological footprint in China. Journal of Cleaner Production, 230(230):1128-1137.
DOI URL |
[51] |
Xu Q, Dong Y X, Yang R, et al. 2019. Temporal and spatial differences in carbon emissions in the Pearl River Delta based on multi-resolution emission inventory modeling. Journal of Cleaner Production, 214(214):615-622.
DOI URL |
[52] |
Zha J, Tan T, Yuan W, et al. 2020. Decomposition analysis of tourism CO2 emissions for sustainable development: A case study of China. Sustainable Development, 28(28):169-186.
DOI URL |
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