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Essay Examples - Big Data Solutions for New Zealand Tourism in a Climate of Peak Oil

Home » Essay Examples » Big Data Solutions for New Zealand Tourism in a Climate of Peak Oil


This literature analysis will examine peer-reviewed articles within the past eight years. Through exploration of such articles the researcher will address the use of data analytics for predicting oil pricing and use variance. Oil preservation strategies will ensure environmental conservation and global economic growth within the public and private sectors.

Review of Tourism and Oil

Becken (2003) provided a meta-analysis of current information on tourism and oil consumption. The report affirms a causative relationship between oil pricing, availability, and tourist behaviors. Further, the report identified economic trends that were uncovered from previous research. However, there has been scarce examination of oil consumption within a social or spacial context. An area of focus not addressed in this report is the political implications and proposed policies for mitigating the risk of ‘peak oil’. Although the report references an Oil Depletion Protocol as cited in Campbell (1996), the implementation of a global rationing system has not yet occurred (Becken, 2011).

In 2009, the United States Institute of Peace (USIP) conducted a study examining tourism development in Kenya, Nigeria, and India. As three developing countries tourism has the ability to provide benefits both economic (i.e. job and income generation) and social (i.e. peace and stability) (Honey & Gilpin, 2009). Currently developing countries are seeking to achieve their Millennium Development Goals (MDGs). The report identifies tourism as a primary export earner, for approximately 33 percent of impoverished countries. As of 2008 tourism had already surpassed $1 trillion dollars in global revenue. The report purports that tourism sustainability is dependent upon the proper implementation of an ecotourism model (Honey & Gilpin, 2009).

Another model of ecotourism is found in Tena Ecuador. Austin Beahm (2011) conducted local interviews about the history of tourism during the summer of 2007. What Beahm discovered is that initially the Kichwa Indians did not receive economic compensation from third parties, conducting tourism projects. However, the formation of organizations like RICANCIE enabled Tena to operate indigenously administered tourism events and reap economic benefits (Beahm, 2011). Current challenges include balancing the tourism boon, with emergent requests by oil prospectors to drill for crude oil. There is urgency in combatting foreign oil companies due to the adverse impact on natural resources (i.e. forests, rivers, and oxygen).

Data Analytics

Logar and Van den Bergh (2013), conducted data analytics using an input-output (I/O) analysis model. The goal was to better understand the impact of peak oil prices on tourism behavior in Spain. There were three sequential steps used during this process. The first step used an I/O model to approximate price variance of tourism services in relation to the prices of oil and other fossil fuel. The second step measured the impact of price variance on tourism demand. The third step projected the effect of demand variance on Spain’s economy (Logar & Van den Bergh, 2013).

Although the Logar and Van den Bergh (2013) I/O analysis model is inconclusive, it does reveal key patterns. Most notably empirical data supports the belief that peak oil will occur within this decade. Especially given the unusually high prices of oil prices in 2008 and 2012. Spain’s economy is dependent on the revenue generated from tourism, as one of the most popular destinations (Logar & Van den Bergh, 2013). Various studies project that Spain’s elasticity demand for tourism ranges from -0.30 to -2.16. The elasticity demand for tourism will greatly effect transport sectors (i.e. air, water, and land). However, the I/O model does not account for other variables or causative factors such as global economic crisis, and political instability (Logar & Van den Bergh, 2013).

According to Mohanty et al., (2014), between September 1983 and August 2011 there were periods of oil price fluctuation. The affected industries included airlines, hotels, travel and tourism. This enabled Mohanty to create a regression table that evaluated for risk measures including (market, size, value, and momentum factor) (Mohanty, Nandha, Habis, & Juhabi, 2014). The results of the regression table revealed that oil price risk exposure effected each industry differently. Oil price risk exposure had a higher adverse impact on airlines, recreational services, and restaurants during the period 1983-2011 (Mohanty, Nandha, Habis, & Juhabi, 2014).

Ritchie et al. (2013) conducted a short-term study on the impact of the 2010 BP Gulf oil spill. There were two data sets used to evaluate a significant segment of the accommodation industry, comprised of hotel and vacation rentals. The article aptly pointed out the advantages and disadvantages of secondary data. With primary data analysis the researcher can influence the research design and validate outcomes. However, with secondary data the information has already been published and subsequently may or may not be reliable, valid, or even appropriate (Ritchie, Crotts, Zehrer, & Volsky, 2013).

Critical secondary data included published information by the National Oceanic and Atmospheric Administration (NOAA). The NOAA published a map projecting the probable impact of toxic oil spills. Ritchie et al. (2013), purports that the regions determined as having the highest probability of impact, were subsequently exposed to oil deposits (Ritchie, Crotts, Zehrer, & Volsky, 2013). Implicit within the report is that the areas projected to have low probable impact of toxic oil, were scarcely exposed to oil deposits. However, that is not to indicate that there was no affect, as regions with lower projected impact experienced decreasing demands in industries including lodging (Ritchie, Crotts, Zehrer, & Volsky, 2013).

The report concludes by proposing future studies that research disasters related to tourism from a regional and sector level. Future recommendations also include the inclusion of economic impact models and Computer General Equilibrium modeling. Such studies could aptly provide predictive behaviors associated with disasters, economic impact, and demand changes (Ritchie, Crotts, Zehrer, & Volsky, 2013).

Becken (2011) in a peer-reviewed report presented data regarding tourism in New Zealand. One primary finding was the correlation between the economic viability of origin countries and outbound tourism. An econometric analysis revealed that travel purpose, time frame, oil vulnerabilities, and variant price elasticity, have greater significance than fuel prices.

The report also identified six risk factors of businesses in the tourism industry. Those six factors included exposure, substitution alternatives, market mix, diversification, geographic location, and competition. There are four categories including (accommodation, attraction, transport, and other) that each have energy costs as a percentage of gross operating revenue (OR). Out of those four, accommodation and transport account for the largest (OR) energy costs, with means of (11.2 and 10.1) respectively (Becken, 2011).

Becken and Lennox (2012) present a report that couples a general equilibrium model with a New Zealand CGE model. The causative impact of elevated oil prices includes a 9 percent decrease in tourism exports and 1.7 percent decrease in gross national disposable income.

Strategies for resource allocation within the public and private sectors

Verbano and Crema (2015) present a quantitative approach for evaluating risk. This article is uniquely relevant considering the demand and price volatility of the energy sector. Bernstein (1996) as cited in Verbano and Crema defines risk as a deviation from anticipated results. Given the elasticity demand of the tourism and oil industries, a set of methods is necessary for identifying, measuring, and mitigating commercial risks (Verbano & Crema, 2015).

There are various approaches and associated tools useful for economic risk management. Verbano and Crema (2015) purport methods such as hedging, by citing research conducted by Giorgino and Travaglini (2008). This strategy is where organizations obtain assets that provide counterbalance for projected future risk. The report also cites (Horcher, 2005) in suggesting the increasing use of complex derivatives as a strategy for reduced price risk. Implicit within this understanding is the realization that associated energy risks cannot be completely mitigated. Therefore, partial hedging of volumetric risk is the primary objective (Verbano & Crema, 2015).

Becken conducted a study in (2008) that examined the correlation between ten indicators of oil intensity and the top ten tourist markets to New Zealand. The author purports that energy conservation campaigns should be implemented in locations like Australia, the United Kingdom, and the United States due to their significant oil necessities. Additionally, Japan was identified as having the greatest ratio of energy usage in domestic air travel. Substitute oil-intensive strategies and other high-speed transportation options are recommended (Becken, 2008).

Becken’s study defines eco-efficiency as the ratio between tourists spending and oil use. Although there may be ways to gradually minimize the effect of certain environmental factors total oil use may not decrease. Especially if the required volume for consumer and business needs continues to increase (Becken, 2008).

According to Yeoman et al. Scottish tourism anticipates 50 percent growth between the years 2007 and 2017. As such the examination of the causative relationship between oil, the global economy, and tourism are evaluated. This is accomplished using a two-part process. The first part is a triangulation method using several systems thinking models (Energy Inflation (EI) and Paying for Climate Change (PCC). The resultant data is subsequently used to translate scenarios to quantifiable equilibrium models (Yeoman et al., 2007).

In the (EI) model there is a social consensus that oil reserves are in abundance and therefore efforts are not made to modify demands. This naturally leads to adverse economic reactions, political cataclysm, and environmental chaos. The (PCC) model projects increasing energy costs, and consequently establishes conservation mechanisms like carbon taxes. The report presents a number of potential issues should the EI or PCC predictions become reality. Future strategies could include policies that promote alternative fuel sources, including renewables and nuclear power (Yeoman et al., 2007).

According to Menon and Mahanty (2012), India is ranked third most fuel dependent out of twenty-six countries. Eighty percent of fuel is imported, resulting in excessive prices and unhealthy greenhouse gas emission. The value of implementing a fuel efficiency policy, could include benefiting the four-wheeler sector. However, this could also be nullified based on an equal or greater increase in energy consumption (Menon & Mahanty, 2012). Implicit within this report is that India and other countries need to balance fuel efficiency polies, with energy consumption dependence.

In comparing Energy Efficiency policies of California and New Zealand there are significant distinctions. In California strategic planning is strong and energy policies are prominent within the legislative framework. However, one report indicates that New Zealand’s Energy and Efficiency Act (2000) is not as effective as California’s. Recommendations include the reallocation of energy-efficiency roles within the variant agencies (Eusterfeldhaus & Barton, 2011).

In China there are major movements in sectors including coal, with the formation of the China Taiyuan Coal Exchange (CTCE). Also, there are notable price reforms in the petroleum and electricity sectors respectively. Within the petroleum sector there are four stages that have determined pricing (Ma, Oxley, & Gibson, 2009). Prior to 1981 the rate of petroleum was dictated by the State. However, currently China petroleum pricing is based on the international energy market. The same pattern is emerging in energy pricing with determination based on the international energy market (Ma, Oxley, & Gibson, 2009).

Within the ground or vehicle transportation sector biodiesel is becoming an emergent source of fuel. One journal article documented the results of fifty scientists who conducted studies using various raw and refined oils. The goal was to determine whether biodiesel fuel was a viable fuel alternative (Shahid & Jamal, 2008). One conclusion of the study was that vegetable oil is interchangeable with diesel oil. Rapeseed and palm oil are preferable as they can function as fuel extenders. Another finding was that vegetable oil is useful for small engines, but not for extended periods. Therefore, further research needs to find ways to extend the life of alternatives like biodiesel in vehicles (Shahid & Jamal, 2008).

Current research is also exploring alternative fuels for the aviation industry. Several process technologies being examined for alternative fuel production include Transesterfication, (HRJ) hydroprocessing, and (FT) Fischer-Tropsch. There is also examination of the potential reduction of GHC emission by FT fuels. The data from Auxiliary Power Units reveal data useful for future fuel development (Blakey, Rye, & Wilson, 2011).

Zeppel and Beaumont (2012) present a compelling argument for government tourism agencies playing a more significant role in climate change. Although this report focuses on tourism agencies in Australian States, this information has global relevance. The report purports that there needs to be greater reporting and accountability by tourism agencies regarding their own carbon footprint. The implementation or proposal of carbon reduction strategies and policies is also encouraged. In areas with a greater volume of long haul travelers or increased carbon risk, climate change policies are more prevalent (Zeppel & Beaumont, 2012).

Leaver and Gillingham (2010) present a model (UniSyd) to analyze predictive behavior within New Zealand’s energy sector. The variables within the model that are adjusted include carbon tax, and oil price. Of the five presented scenarios each one achieves different outcomes. For example, the BEV scenario projects a reduction of up to 23 percent of greenhouse gas emission by 2050 (Leaver & Gillingham, 2010). Carbon capture and sequestration (CCS) is projected to reduce greenhouse emission by up to 43 percent by 2050. The article concludes by suggesting that New Zealand policymakers should base their policy selection on the desired environmental or economic outcomes (Leaver & Gillingham, 2010).


Within this literary review scholarly journals reveal causative relationships between factors such as oil pricing, distance of travel, and the environment. While there does not appear to be a conclusive strategy for reducing oil prices several concepts emerge. Each business sector (especially the travel and leisure) industries need to examine their carbon footprint. It is equally important to be preemptive in pursuing alternative fuel and energy options. Various data models have revealed the error of assuming that traditional fuel sources will never run out. Therefore an approach that includes aggressive research and integration of alternative technologies is likely beneficial.

From a macro perspective it is equally important that the government create a legal framework for addressing energy efficiency. While economic factors should influence future policy development it is equally important to consider environmentally friendly approaches. Failure to modify oil demands will inevitably lead to peak pricing, however there will be other causative factors to consider. Finally, many developing countries are dependent on the revenue from tourism so local engagement in the tourism industry is important.


Beahm, A. (2011, Summer). The Slippery Slope of Tourism and Oil in the Amazon: The Story of Tena, Ecuador. Focus on Geography, 54(2), 70-74.

Becken, S. (2008). Developing indicators for managing tourism in the face of peak oil. Tourism Management, 29(1), 695-705.

Becken, S. (2011). A Critical Review of Tourism and Oil. Annals of Tourism Research, 38(2), 359-379.

Becken, S. (2011). Oil, the global economy and tourism. Tourism Review, 66(3), 65-72.

Becken, S., & Lennox, J. (2012). Implications of a long-term increase in oil prices for tourism. Tourism Management, 33(1), 133-142.

Blakey, S., Rye, L., & Wilson, C. W. (2011). Aviation gas turbine alternative fuels_A Review. Science Direct, 2011(33), 1.

Eusterfeldhaus, M., & Barton, B. (2011). Energy Efficiency: a comparative analysis of the New Zealand Legal Framework. Journal of Energy & Natural Resources Law, 29(4), 431-472.

Honey, M., & Gilpin, R. (2009, October). Tourism in the Developing World. Retrieved from http://www.responsibletravel.org/resources/documents/reports/USIP%20Tourism%20in%20the%20Developing%20World.pdf

Leaver, J., & Gillingham, K. (2010). Economic impact of the integration of alternative vehicle technologies into the New Zealand vehicle fleet. Journal of Cleaner Production, 18(1), 908-916.

Logar, I., & Van den Bergh, J. C. (2013, June). The impact of peak oil on tourism in Spain: An input-output analysis of price, demand and economy-wide effects. Energy, 54(1), 155-166.

Ma, H., Oxley, L., & Gibson, J. (2009). China's energy situation in the new millennium. Renewable and Sustainable Energy Reviews, 13(1), 1781-1799.

Menon, B. G., & Mahanty, B. (2012). Effects of fuel efficiency improvements in personal transportation. Case of four-wheelers in India. International Journal of Energy Sector, 6(3), 397-416.

Mohanty, S., Nandha, M., Habis, E., & Juhabi, E. (2014). Oil price risk exposure: The case of the U.S. Travel and Leisure Industry. Energy Economics, 41(1), 117-124.

Ritchie, B. W., Crotts, J. C., Zehrer, A., & Volsky, G. T. (2013). Understanding the Effects of a Tourism Crisis: The Impact of the BP Oil Spill on Regional Lodging Demand. Journal of Travel Research, 53(1), 12-25.

Shahid, E. M., & Jamal, Y. (2008). A review of biodiesel as vehicular fuel. Science Direct, 12(1), 2484-2494.

Verbano, C., & Crema, M. (2015, February). Risk indicators for managing the energy procurement process. International Journal of Productivity and Performance Management, 64(2), 228-242.

Yeoman, I., Lennon, J. J., Blake, A., Galt, M., Greenwood, C., & McMahon-Beattie, U. (2007). Oil depletion: What does this mean for Scottish tourism? Science Direct, 28(1), 1354-1365.

Zeppel, H., & Beaumont, N. (2012). Climate change and tourism futures_Responses by Australian tourism agencies. Tourism and Hospitality Research, 12(2), 73-88.

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