Abstract
Increasing population and over-consumption are placing unprecedented demands on agriculture and natural resources. The Earth is suffering from global warning and environmental destruction while our agricultural systems are concurrently degrading land, water, biodiversity, and climate on a global scale. For a sustainable future, green certification, e-commerce, and environment education can boost low-carbon economy with decreasing carbon emissions, but very few researches address them for the hotel industry. This research studies the performance impact of e-commerce, international hotel chain, local hotel chain, and green certification for carbon emission reductions of international tourist hotels of Taiwan. It reveals that, after a sufficiently long time, there is an improvement in the environmental and economic performance of the green-certified hotel group. In addition, it reveals that, as recommended by the operation policy, the international hotel chain group together with e-commerce has better performance than local hotel chain. It is also discussed how to sustain the continuing improvement in low-carbon performance of the hotel industry.
Similar content being viewed by others
Notes
Green seal certification for members in Ch was not mandatory for most hotel chains (see Appendixes 2 and 3). The expenses for water and gas were transformed to their respective consumptive units first (i.e., 12.08 NT$ = 1 unit of water, 13.66NT$ = 1 unit of gas). The respective consumptive units were then transformed into equivalent carbon emissions (i.e., 1 NT$ of electricity fee = 0.138 kg, 1 unit of water = 0.195 kg, and 1 unit of gas = 1.5 kg) (data source: EPA’s data in 2007)
e-commerce means the hotel has a website for consumers or enterprises to book their rooms from internet access and electronic transactions.
References
Bohdanowicz P, Martinac I (2007) Determinants and benchmarking of resource consumption in hotels—case study of Hilton international and Scandic in Europe. Energy Build 39:82–95
Butchart SH, Walpole M, Collen B, Van SA, Scharlemann JP, Almond RE et al (2010) Global biodiversity: indicators of recent declines. Science 328(5982):1164–1168
Chan W (2009) Environmental measures for hotels’ environmental management systems ISO 14001. Int J Contemp Hosp Manag 21(5):542–560
Chen LF (2013a) The little green handbook—seven trends shaping the future of our planet, Ron Nielsen, St. Martin’s press, new York, USA, ISBN 0-312-42581-3. Ecol Econ 89(5):202–203
Chen L (2013b) A sustainable hypothesis for tourist hotels: evidence from international hotel chains. Tour Econ 19(6):1449–1460
Chung Y, Färe R, Grosskopf S (1997) Productivity and undesirable outputs: a directional distance function approach. J Environ Manag 51:229–240
Galloway JN, Leach AM (2016) Sustainability: your feet’s too big. Nat Geosci 9(2)
Gössling S (2002) Global environmental consequences of tourism. Glob Environ Chang 12(4):283–302
Grigg PJ, Hibberd PPL, Kurmi OP, Lam KH et al (2014) Respiratory risks from household air pollution in low and middle income countries. Lancet Respir Med 2(10):823
Griggs D, Staffordsmith M, Gaffney O, Rockström J, Ohman MC, Shyamsundar P et al (2013) Policy: sustainable development goals for people and planet. Nature 495(7441):305–307
Gunasekaran A, Ngai EW (2004) Information systems in supply chain integration and management. Eur J Oper Res 159(2):269–295
He G, Boas I, Mol AP, Lu Y (2016) E-participation for environmental sustainability in transitional urban China. Sustain Sci 1–16. https://doi.org/10.1007/s11625-016-0403-3
Hsieh LF, Lin LH (2010) A performance evaluation model for international tourist hotels in Taiwan: an application of the relational network DEA. Int J Hosp Manag 29:14–24
Huang CW, Ho FN, Chiu YH (2014) Measurement of tourist hotels’ productive efficiency, occupancy, and catering service effectiveness using a modified two-stage dea model in Taiwan. Omega 48:49–59
Lyson TA (2002) Advanced agricultural biotechnologies and sustainable agriculture. Trends Biotechnol 20(5):193–196
Ma X, Liu Y, Wei X, Li Y, Zheng M, Li Y et al (2017) Measurement and decomposition of energy efficiency of northeast China-based on super efficiency DEA model and Malmquist index. Environ Sci Pollut Res 24(24):19859–19873
Manganari EE, Dimara E, Theotokis A (2015) Greening the lodging industry: current status, trends and perspectives for green value. Curr Issue Tour 19(3):1–20
Ming C, Fan J (2013) Carbon reduction in a high-density city: a case study of Langham Place Hotel, Mongkok Hong Kong. Renew Energy 50(3):433–440
Moutinho V, Madaleno M, Robaina (2017) Advanced scoring method of eco-efficiency in European cities. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-017-0540-y
Seele P, Lock I (2017) The game-changing potential of digitalization for sustainability: possibilities, perils, and pathways. Sustain Sci 12(2):1–3
Shu G, Ren TZ, Wang MH (2007) Technology and infrastructure considerations for e-commerce in Chinese agriculture. Agric Sci China 6(1):1–10
Travel and Tourism Market (n.d.) https://www.statista.com/statistics
UN (2016). The Sustainable Development Goals Report 2016
UN (n.d.) The Trade and Development Report (TDR) 2016: structural transformation for inclusive and sustained growth, http://www.unctad.org, access time: 2017–2-1
US Energy Information Administration (1998) https://www.eia.gov/totalenergy/data/annual/previous.php
Zeng Y, Wan L, Guo H (2016) Agricultural e-commerce research: a review of the current status and prospects, China Rural Survey, No 3, 82–93
Acknowledgements
The author wants to thank the editor and two anonymous reviewers for their valuable comments.
Funding
The fund was sponsored by “The key subject of the provincial characteristics of the Nanfang College of Sun Yat-sen University - the funding for the construction of electronic commerce project” and the R&D project “Green IOT data, information security, application of smart buildings” from Nanfang College of Sun Yat-sen University. The author also wants to thank ANU professor Robert Costanza and Ida Kubiszewski for their valuable explanation about why are ecosystem services are importantly related to environmental performance and CSR and well-being beyond current GDP calculation.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible editor: Philippe Garrigues
Appendices
Appendix 1
Appendix 2
Appendix 3
Appendix 4. Methodology
To model the technology to produce desired outputs with jointly undesired outputs, some conditions are needed. If we denote desired outputs by \( y\in {R}_{+}^M \), undesired outputs by \( b\in {R}_{+}^I \), and inputs by \( x\in {R}_{+}^N \), we can denote the output sets as:
The reduction of undesired outputs is costly and can be modeled as:
The desired outputs jointly produced with the undesired outputs can be modeled by:
Based on conditions 1.1~1.3, Chung et al.’s definition for the ML index of productivity between period t and t + 1 is as:
where D′0 is a directional distance function and
where g is a vector of “directions” in which outputs are scaled.
ML index uses directional distance function than traditional Malmquist index using Shephard’s output distance function. The distance functions can be illustrated by Fig. 1. The meaning of point A in efficiency frontier in Malmquist index can be modeled as to scale OC/OA by OA/OC which increases both desired and undesired outputs proportionally from point C. The meaning of point B can be modeled as to scale OC by CB in efficiency frontier in ML index by increasing desired outputs and decreasing undesired outputs in direction Og (the same as CB). From the difference, it means that to increase productivity growth in Malmquist index one may have an increasing cost of pollution as a by-product, which is not what we expected in a sustainability effort. Thus, ML index is preferred since it can be expected to increase productivity growth without increasing undesired outputs.
For the efficiency of calculation, ML index can be indirectly derived from Malmquist index. To associate ML index with Malmquist index can be achieved through their respective based Shephard’s output distance function and directional distance function. The relation between directional distance function and Shephard’s output distance function is as:
where D0(x, y, b) is Shephard’s output distance function used in Malmquist index.
Rights and permissions
About this article
Cite this article
Chen, LF. Green certification, e-commerce, and low-carbon economy for international tourist hotels. Environ Sci Pollut Res 26, 17965–17973 (2019). https://doi.org/10.1007/s11356-018-2161-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-018-2161-5