黄河流域旅游业碳排放效率综合测度及影响因素研究

doi:10.12118/j.issn.1000-6060.2023.193

摘要/Abstract

摘要：

科学量化并分析黄河流域旅游业碳排放效率及其影响因素，对于推动其旅游经济绿色化发展具有重要意义。基于2000—2019年黄河流域9省区面板数据，利用Super-SBM模型从静态和动态2个视角揭示旅游业碳排放效率的时空演变特征，并通过空间杜宾模型探讨影响黄河流域旅游业碳排放效率的关键因素及其空间溢出效应。结果表明：（1） 2000—2019年，黄河流域内旅游业碳排放效率均值呈现先波动上升后下降的趋势，各省区之间的差异不断缩小，空间分布呈现“西低东高”格局。（2）动态效率方面，Malmquist-Luenberger指数和核密度曲线的变化趋势均表明旅游业碳排放效率的极化现象减弱，且技术进步对旅游业碳排放效率变动的贡献更大。（3）影响因素方面，环境规制和城镇化水平在促进当地旅游业碳排放效率的过程中呈现出正向溢出效应；产业结构、对外开放、技术水平和旅游产业集聚对当地和邻地的影响系数均为负；而经济发展水平会抑制旅游业碳排放效率的提升，但对周边地区呈现出显著的正向溢出作用。

关键词: 旅游业碳排放效率, Super-SBM, 空间杜宾模型, 黄河流域

Abstract:

The quantification and assessment of carbon emission efficiency and its influencing factors in the tourism sector are crucial for advancing environmentally-friendly development in the tourism economy of Yellow River Basin, China. This study develops an input-output table for evaluating the carbon emission efficiency of tourism. In addition to traditional factors such as capital, labor, and energy, we incorporate technological innovation. The desired output is the total tourism revenue, while the non-desired output is the carbon emission during tourism, integrated into the Super-Slack-Based Measure (SBM). Employing the Malmquist-Luenberger index and the kernel density estimation method, we analyzed tourism carbon emission efficiency from both static and dynamic perspectives. Furthermore, we established a spatial Durbin model to examine spatial spillover effects. Our results revealed the following: (1) During 2000—2019, the carbon emission efficiency of tourism in the Yellow River Basin initially experienced an upward fluctuation followed by a subsequent downward trend. Provinces and regions exhibited a diminishing difference, with a spatial distribution pattern that indicated lower efficiency in the west and higher efficiency in the east. (2) Regarding dynamic efficiency, both the Malmquist-Luenberger index and kernel density estimation trends indicated a weakening polarization phenomenon in carbon emission efficiency. Technological progress significantly contributed to changes in the tourism industry’s carbon emission efficiency. (3) Influencing factors that include positive spillover effects from environmental regulation and urbanization, enhanced local tourism carbon emission efficiency. Conversely, industrial structure, openness, technology level, and tourism industry agglomeration were found to have adverse effects. Economic development levels hindered the improvement of carbon emission efficiency in tourism; however, they exhibited a notable positive spillover effect on surrounding regions. These findings aim to establish a theoretical foundation for governments to formulate targeted policies for energy-saving and carbon-emission reduction in the tourism sector.

Key words: tourism carbon emission efficiency, Super-SBM, spatial Durbin model, Yellow River Basin

杜娅明, 白永平, 梁建设, 张春悦, 荆林祥, 王立果, 邹嘉铖. 黄河流域旅游业碳排放效率综合测度及影响因素研究[J]. 干旱区地理, 2023, 46(12): 2074-2085.

DU Yaming, BAI Yongping, LIANG Jianshe, ZHANG Chunyue, JING Linxiang, WANG Liguo, ZOU Jiacheng. Comprehensive measurement and influencing factors of carbon emission efficiency of tourism in the Yellow River Basin[J]. Arid Land Geography, 2023, 46(12): 2074-2085.

图/表 12

图1

表1

图2

图3

图4

表2

图5

表3

表4

表5

表6

表7

参考文献 41

[1]	穆学青, 郭向阳, 明庆忠, 等. 黄河流域旅游生态安全的动态演变特征及驱动因素[J]. 地理学报, 2022, 77(3): 714-735. doi: 10.11821/dlxb202203015
	[Mu Xueqing, Guo Xiangyang, Ming Qingzhong, et al. Dynamic evolution characteristics and driving factors of tourism ecological security in the Yellow River Basin[J]. Acta Geographica Sinica, 2022, 77(3): 714-735.] doi: 10.11821/dlxb202203015
[2]	Peeters P, Dubois G. Tourism travel under climate change mitigation constraints[J]. Journal of Transport Geography, 2010, 18(3): 447-457. doi: 10.1016/j.jtrangeo.2009.09.003
[3]	王坤, 黄震方, 曹芳东. 中国旅游业碳排放效率的空间格局及其影响因素[J]. 生态学报, 2015, 35(21): 7150-7160.
	[Wang Kun, Huang Zhenfang, Cao Fangdong. Spatial pattern and influencing factors of carbon dioxide emissions efficiency of tourism in China[J]. Acta Ecologica Sinica, 2015, 35(21): 7150-7160.]
[4]	Tang C C, Zhong L S, Jiang Q O. Energy efficiency and carbon efficiency of tourism industry in destination[J]. Energy Efficiency, 2018, 11(3): 539-558. doi: 10.1007/s12053-017-9598-0
[5]	Li X M, Shi P F, Han Y Z, et al. Measurement and spatial variation of green total factor productivity of the tourism industry in China[J]. International Journal of Environmental Research and Public Health, 2020, 17(4): 1159, doi: 10.3390/ijerph17041159.
[6]	Wu Y X, Lin S W. Efficiency evaluation of Asia’s cultural tourism using a dynamic DEA approach[J]. Socio-Economic Planning Sciences, 2022, 84: 101426, doi: 10.1016/j.seps.2022.101426.
[7]	王钰凯, 郭辉. 新疆旅游业生态效率时空分异与收敛性研究[J]. 干旱区地理, 2022, 45(4): 1320-1331.
	[Wang Yukai, Guo Hui. Spatial and temporal differentiation and convergence studies of the ecoefficiency of Xinjiang tourism industry[J]. Arid Land Geography, 2022, 45(4): 1320-1331.]
[8]	王凯, 刘依飞, 甘畅. 中国环境规制对旅游业碳排放绩效的影响[J]. 中国生态旅游, 2022, 12(4): 603-616. doi: 10.12342/zgstly.20210119
	[Wang Kai, Liu Yifei, Gan Chang. The impact of environmental regulation on the tourism carbon emissions performance in China[J]. Journal of Chinese Ecotourism, 2022, 12(4): 603-616.] doi: 10.12342/zgstly.20210119
[9]	王凯, 何静, 甘畅, 等. 中国旅游产业结构变迁对旅游业碳排放效率的空间溢出效应研究[J]. 中国软科学, 2022(12): 50-60.
	[Wang Kai, He Jing, Gan Chang, et al. Study on the spatial spillover effect of China’s tourism industrial structure change on tourism carbon emission efficiency[J]. China Soft Science, 2022(12): 50-60.]
[10]	何昭丽, 孙慧, 张振龙. 中国入境旅游发展效率及其影响因素研究[J]. 干旱区地理, 2017, 40(6): 1282-1289.
	[He Zhaoli, Sun Hui, Zhang Zhenlong. Technical efficiency and influencing factors of China’s inbound tourism[J]. Arid Land Geography, 2017, 40(6): 1282-1289.]
[11]	Guo L J, Li P Z, Zhang J H, et al. Do socio-economic factors matter? A comprehensive evaluation of tourism eco-efficiency determinants in China based on the geographical detector model[J]. Journal of Environmental Management, 2022, 320: 115812, doi: 10.1016/j.jenvman.2022.115812.
[12]	岳立, 雷燕燕, 王杰. 中国省域旅游业碳排放效率时空特征及影响因素分析[J]. 统计与决策, 2020, 36(16): 69-73.
	[Yue Li, Lei Yanyan, Wang Jie. Spatial-temporal characteristics and influencing factors of carbon emission efficiency of tourism in China Province[J]. Statistics & Decision, 2020, 36(16): 69-73.]
[13]	Meng W Q, Xu L Y, Hu B B, et al. Quantifying direct and indirect carbon dioxide emissions of the Chinese tourism industry[J]. Journal of Cleaner Production, 2016, 126: 586-594. doi: 10.1016/j.jclepro.2016.03.067
[14]	王凯, 邵海琴, 周婷婷, 等. 中国旅游业碳排放效率及其空间关联特征[J]. 长江流域资源与环境, 2018, 27(3): 473-482.
	[Wang Kai, Shao Haiqin, Zhou Tingting, et al. A study on carbon emissions efficiency of tourism and its spatial correlation characteristics in China[J]. Resources and Environment in the Yangtze Basin, 2018, 27(3): 473-482.]
[15]	Kelly J, Willianms P W. Tourism destination energy consumption and greenhouse gas emissions: Whistler, British Columbia, Canada[J]. Sustainable Tourism, 2007, 15(1): 67-90.
[16]	王凯, 邵海琴, 周婷婷, 等. 基于STIRPAT模型的中国旅游业碳排放影响因素分析[J]. 环境科学学报, 2017, 37(3): 1185-1192.
	[Wang Kai, Shao Haiqin, Zhou Tingting, et al. Analysis of impact factors of CO₂ emissions from tourism in China based on STIRPAT model[J]. Acta Scientiae Circumstantiae, 2017, 37(3): 1185-1192.]
[17]	王凯, 杨亚萍, 张淑文, 等. 中国旅游产业集聚与碳排放空间关联性[J]. 资源科学, 2019, 41(2): 362-371. doi: 10.18402/resci.2019.02.14
	[Wang Kai, Yang Yaping, Zhang Shuwen, et al. Spatial correlation between the agglomeration and CO₂ emissions of China’s tourism industry[J]. Resources Science, 2019, 41(2): 362-371.] doi: 10.18402/resci.2019.02.14
[18]	石培华, 吴普. 中国旅游业能源消耗与CO₂排放量的初步估算[J]. 地理学报, 2011, 66(2): 235-243.
	[Shi Peihua, Wu Pu. A rough estimation of energy consumption and CO₂emissionin tourism sector of China[J]. Acta Geographica Sinica, 2011, 66(2): 235-243.]
[19]	谢园方, 赵媛. 长三角地区旅游业能源消耗的CO₂排放测度研究[J]. 地理研究, 2012, 31(3): 429-438. doi: 10.11821/yj2012030005
	[Xie Yuanfang, Zhao Yuan. Measuring carbon dioxide emissions from energy consumption by tourism in Yangtze River Delta[J]. Geographical Research, 2012, 31(3): 429-438.] doi: 10.11821/yj2012030005
[20]	姚丹, 任丽燕, 马仁锋, 等. 长三角旅游业碳排放强度空间格局及影响因素分析[J]. 生态科学, 2021, 40(2): 89-98.
	[Yao Dan, Ren Liyan, Ma Renfeng, et al. Analysis of spatial pattern and influencing factors of carbon emission intensity of tourism industry in Yangtze River Delta[J]. Ecological Science, 2021, 40(2): 89-98.]
[21]	Hunter C, Shaw J. The ecological footprint as a key indicator of sustainable tourism[J]. Tourism Management, 2007, 28(1): 46-57. doi: 10.1016/j.tourman.2005.07.016
[22]	邵海琴, 王兆峰. 长江经济带旅游业碳排放效率的综合测度与时空分异[J]. 长江流域资源与环境, 2020, 29(8): 1685-1693.
	[Shao Haiqin, Wang Zhaofeng. Comprehensive measurement of carbon emissions efficiency of tourism and its spatio-temporal differentiation in the Yangtze River Economic Belt[J]. Resources and Environment in the Yangtze Basin, 2020, 29(8): 1685-1693.]
[23]	王梓瑛, 王兆峰. 碳排放约束下长江经济带旅游产业效率的时空演化及影响因素研究[J]. 长江流域资源与环境, 2021, 30(2): 280-289.
	[Wang Ziying, Wang Zhaofeng. Spatial-temporal evolution and influencing factors of tourism industry efficiency under the constraints of carbon emission in the Yangtze River Economic Zone[J]. Resources and Environment in the Yangtze Basin, 2021, 30(2): 280-289.]
[24]	谭华云, 许春晓, 董雪旺. 旅游业碳排放效率地区差异分解与影响因素探究[J]. 统计与决策, 2018, 34(16): 51-55.
	[Tan Huayun, Xu Chunxiao, Dong Xuewang. Decomposition of regional differences in carbon emission efficiency of tourism industry and exploration of influencing factors[J]. Statistics & Decision, 2018, 34(16): 51-55.]
[25]	王凯, 张淑文, 甘畅, 等. 中国旅游业碳排放效率的空间网络结构及其效应研究[J]. 地理科学, 2020, 40(3): 344-353. doi: 10.13249/j.cnki.sgs.2020.03.002
	[Wang Kai, Zhang Shuwen, Gan Chang, et al. Spatial network structure of carbon emission efficiency of tourism industry and its effects in China[J]. Scientia Geographica Sinica, 2020, 40(3): 344-353.] doi: 10.13249/j.cnki.sgs.2020.03.002
[26]	Liu J, Zhang J F, Fu Z B. Tourism eco-efficiency of Chinese coastal cities: Analysis based on the DEA-Tobit model[J]. Ocean & Coastal Management, 2017, 148: 164-170.
[27]	于法稳, 林珊, 王广梁. 黄河流域县域生态治理的重点领域及对策研究[J]. 中国软科学, 2023(2): 104-114.
	[Yu Fawen, Lin Shan, Wang Guangliang. Research on key areas and countermeasures of county-level ecological governance of Yellow River Basin[J]. China Soft Science, 2023(2): 104-114.]
[28]	王胜鹏, 乔花芳, 冯娟, 等. 黄河流域旅游生态效率时空演化及其与旅游经济互动响应[J]. 经济地理, 2020, 40(5): 81-89.
	[Wang Shengpeng, Qiao Huafang, Feng Juan, et al. The spatio-temporal evolution of tourism eco-efficiency in the Yellow River Basin and its interactive response with tourism economy development level[J]. Economic Geography, 2020, 40(5): 81-89.]
[29]	宋慧林, 马运来. 我国旅游业技术创新水平的区域空间分布特征——基于专利数据的统计分析[J]. 旅游科学, 2010, 24(2): 71-76.
	[Song Huilin, Ma Yunlai. Regional soatial distribution of tourism technology innovation level in China: An statistical analysis based on patent data[J]. Tourism Science, 2010, 24(2): 71-76.]
[30]	黄国庆, 汪子路, 时朋飞, 等. 黄河流域旅游业碳排放脱钩效应测度与空间分异研究[J]. 中国软科学, 2021, 4: 82-93.
	[Huang Guoqing, Wang Zilu, Shi Pengfei, et al. Measurement and spatial heterogeneity of tourism carbon emission and its decoupling effects: A case study of the Yellow River Basin in China[J]. China Soft Science, 2021, 4: 82-93.]
[31]	Tone K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2001, 130(3): 498-509. doi: 10.1016/S0377-2217(99)00407-5
[32]	Anderson P, Petersen N C. A procedure for ranking efficient units in date envelopment analysis[J]. Management Science, 1993, 39(10): 1261-1264. doi: 10.1287/mnsc.39.10.1261
[33]	Young A. Gold into base metals: Productivity growth in the people’s republic of China during the reform period[J]. Journal of Political Economy, 2003, 111(6): 1220-1261. doi: 10.1086/378532
[34]	Patterson M, Mcdonald G. How clean and green is New Zealand tourism[M]. Lincoln: Manaki Whenua, 2004: 56-59
[35]	Chung Y H, Färe R, Grosskopf S. Productivity and undesirable outputs: A directional distance function approach[J]. Journal of Environmental Management, 1997, 51(3): 229-240. doi: 10.1006/jema.1997.0146
[36]	Tran L T. Kernel density estimation on random fields[J]. Journal of Multivariate Analysis, 1990, 34(1): 37-53. doi: 10.1016/0047-259X(90)90059-Q
[37]	曹开军, 龙顺发. 新疆县市旅游产业集聚演变及其影响因素[J]. 经济地理, 2022, 42(12): 205-213. doi: 10.1080/00130095.1966.11729850
	[Cao Kaijun, Long Shunfa. Evolution of tourism industry agglomeration and its influencing factors based on counties in Xinjiang Uygur Autonomous Region[J]. Economic Geography, 2022, 42(12): 205-213.] doi: 10.1080/00130095.1966.11729850
[38]	徐晔, 欧阳婉桦. 江西省城市绿色发展水平动态测度及影响机制[J]. 长江流域资源与环境, 2022, 31(5): 1152-1168.
	[Xu Ye, Ouyang Wanhua. Dynamic measurement of the urban green development level and its influencing mechanism in Jiangxi Province[J]. Resources and Environment in the Yangtze Basin, 2022, 31(5): 1152-1168.]
[39]	童昀, 何彪. 旅游经济的绿色发展效应及其形成机制——中国92个旅游依赖型城市的实证[J]. 中国人口・资源与环境, 2022, 32(4): 134-144.
	[Tong Yun, He Biao. Green development effect of tourism economy and its formation mechanism: Evidence from 92 tourism-dependent cities in China[J]. China Population, Resources and Environment, 2022, 32(4): 134-144.]
[40]	王鑫静, 程钰. 城镇化对碳排放效率的影响机制研究——基于全球118个国家面板数据的实证分析[J]. 世界地理研究, 2020, 29(3): 503-511. doi: 10.3969/j.issn.1004-9479.2020.03.2019211
	[Wang Xinjing, Cheng Yu. Research on the influencing mechanism of urbanization on carbon emission efficiency: Based on an empirical study of 118 countries[J]. World Regional Studies, 2020, 29(3): 503-511.] doi: 10.3969/j.issn.1004-9479.2020.03.2019211
[41]	童昀, 刘海猛, 马勇, 等. 中国旅游经济对城市绿色发展的影响及空间溢出效应[J]. 地理学报, 2021, 76(10): 2504-2521. doi: 10.11821/dlxb202110012
	[Tong Yun, Liu Haimeng, Ma Yong, et al. The influence and spatial spillover effects of tourism economy on urban green development in China[J]. Acta Geographica Sinica, 2021, 76(10): 2504-2521.] doi: 10.11821/dlxb202110012

类型	一级指标	二级指标
投入	资本	旅游业固定资本存量/10⁸元
	劳动力	旅游业年末从业人员/10⁴人
	能源消耗	旅游业能源消耗量/MJ
	技术创新	旅游相关专利累计申请数量/个
产出	期望产出	旅游业总收入/10⁸元
产出	非期望产出	旅游业CO₂排放量/kg

省份	ML指数均值	技术效率变化指数	技术进步指数
山西	1.277	1.115	1.277
内蒙古	1.293	1.200	1.293
山东	1.206	0.994	1.206
河南	1.067	0.953	1.067
四川	1.138	1.036	1.138
陕西	1.157	1.100	1.157
甘肃	1.216	1.083	1.216
青海	1.099	0.987	1.099
宁夏	1.090	0.992	1.090
平均值	1.171	1.051	1.171

年份	全局莫兰指数	年份	全局莫兰指数
2000	0.144^**	2010	0.417^***
2001	0.169^**	2011	0.456^***
2002	0.189^**	2012	0.406^***
2003	0.211^**	2013	0.401^***
2004	0.056^**	2014	0.320^***
2005	0.236^***	2015	0.283^***
2006	0.245^***	2016	0.267^***
2007	0.392^***	2017	0.250^**
2008	0.349^***	2018	0.200^**
2009	0.422^***	2019	0.190^**

变量类型	变量名称	变量解释
被解释变量	旅游业碳排放效率（TCEE）	Super-SBM模型计算得到
解释变量	经济发展水平（ED）	人均GDP/元
	产业结构（IS）	第三产业产值占比/%
	对外开放程度（Open）	进出口总额占GDP比重/%
	环境规制（ER）	环境污染治理投资额占GDP 比重/%
	城镇化水平（Urb）	城镇人口比重/%
	技术水平（TL）	万元GDP能源消耗量（标准煤）/t·(10⁴元)^-1
	旅游产业集聚（TA）	区位熵计算得到^[37]

检验方法	特征值	检验方法	特征值
空间滞后最大似然（LM-Lag）	15.880^***	空间误差似然比（LR-Error）	156.090^***
Robust LM-Lag	52.003^***	Wald-Error	55.840^***
空间误差最大似然（LM-Error）	0.626	豪斯曼（Hausman）	22.450^***
Robust LM-Error	36.749^***	LR-time	169.59^***
空间滞后似然比（LR-Lag）	128.550^***	LR-ind	53.14^***
Wald-Lag	50.060^***