Published Mar 16, 2017
Desamparados BLAZQUEZ Josep DOMENECH


The World Wide Web (WWW) has become the largest repository of information in the world, providing a data stream that grows at the same time as the scope of the Internet does in society. As with most Information and Communication Technologies (ICTs), its digital nature makes it easy for computer programs to analyze it and discover information. This is why it is being increas­ingly explored as a source of new indicators of technology, economics and development. Web-based indicators can be made available on a real-time basis, unlike delayed official data releases. In this paper, we examine the viability of monitoring firm export orientation from automatically retrieved web variables. Our focus on exports is consistent with the role of internationalization in economic development. To evaluate our approach, we first checked to what extent web variables are capable of predicting firm export orientation. Once these new variables are validated, their automated re­trieval is assessed by comparing the predictive performance of two nowcast models: one considering the manually retrieved web variables, the other considering the automatically retrieved ones. Our results evidence that i) web-based variables are good predictors for firm export orientation, and ii) the process of extracting and analyzing such variables can be entirely automated with no significant loss of performance. This way, it is possible to nowcast not only the export orientation of a firm, but also of an economic sector or of a region.



automatic indicators, Big Data, corporate websites, export, monitoring, nowcasting, web data mining

Ameli, N.; Kammen, D. M. 2014. Innovations in financing that drive cost parity for long-term electricity sustainability: an assessment of Italy, Europe’s fastest growing solar photovoltaic market, Energy for Sustainable Development 19: 130–137. https://doi.org/10.1016/j.esd.2014.01.001

Antonelli, M.; Desideri, U. 2014. The doping effect of Italian feed-in tariffs on the PV market, Energy Policy 67: 583–594. https://doi.org/10.1016/j.enpol.2013.12.025

Barbose, G.; Darghouth, N.; Weaver, S.; Wiser, R. 2013. Tracking the Sun VI. California, Lawrence Berkeley National Laboratory (LBNL).

Branker, K.; Pathak, M. J. M.; Pearce, J. M. 2011. A review of solar photovoltaic levelized cost of electricity, Renewable and Sustainable Energy Reviews 15(9): 4470–4482. https://doi.org/10.1016/j.rser.2011.07.104

Brearley, D. 2009. C-Si photovoltaic trends: design, purchasing and 2009 specs, Solar Pro Magazine June/July: 49–74.

Burns, J. E.; Kang, J.-S. 2012. Comparative economic analysis of supporting policies for residential solar PV in the United States: Solar Renewable Energy Credit (SREC) potential, Energy Policy 44: 217–225. https://doi.org/10.1016/j.enpol.2012.01.045

California Energy Commision (CEC) Energy Technology Development Division. 2001. A guide to photovoltaic (PV) system design and installation. California, Endecon Engineering.

Darling, S. B.; You, F.; Veselka, T.; Velosa, A. 2011. Assumptions and the levelized cost of energy for photovoltaics, Energy and Environmental Science 4: 3133–3139. https://doi.org/10.1039/c0ee00698j

Database of State Incentives for Renewables & Efficiency (DSIRE). 2015. [online], [cited 20 April 2015]. Available from Internet: www.dsireusa.org

Dell’Isola, A. J.; Kirk, S. J. 2003. Life cycle costing for facilities. Massachusetts, Reed Construction Data.

Farris, P. W.; Bendle, N. T.; Pfeifer, P. E.; Reibstein, D. J. 2010. Marketing metrics. New Jersey, Pearson Education.

Federal Reserve Bank (FRB). 2015. [online], [cited 20 April 2015]. Available from Internet: www.feder-alreserve.gov/releases/h15/data.htm#fn11

Frondel, M.; Ritter, N.; Schmidt, C. M.; Vance, C. 2010. Economic impacts from the promotion of renewable energy technologies: the German experience, Energy Policy 38: 4048–4056. https://doi.org/10.1016/j.enpol.2010.03.029

Generation Attribute Tracking System (GATS). 2015. [online], [cited 20 April 2015]. Available from Internet: http://gats.pjm-eis.com/gats2/PublicReports/SolarWeightedAveragePrice

Hass, R.; Panzer, C.; Resch, G.; Ragwitz, M.; Reece, G.; Held, A. 2011. A historical review of promotion strategies for electricity from renewable energy sources in EU countries, Renewable and Sustainable Energy Reviews 15: 1003–1034. https://doi.org/10.1016/j.rser.2010.11.015

HIS Technology. 2014. PV Inverter World Market Report 2014. Colorado, HIS Technology.

Hong, S.; Jung, D. 2012. New and renewable energy law and policy. Seoul, Bobmunsa.

Hong, T.; Koo, C.; Kwak, T. 2013. Framework for the implementation of a new renewable energy system in an educational facility, Applied Energy 103: 539–551. https://doi.org/10.1016/j.apenergy.2012.10.013

Hong, T.; Koo, C.; Park, J.; Park, H. 2014a. A GIS (geographic information system)-based optimization model for estimating the electricity generation of the rooftop PV (photovoltaic) system, Energy 65: 190–199. https://doi.org/10.1016/j.energy.2013.11.082

Hong, T.; Koo, C.; Kwak, T.; Park, H. 2014b. An economic and environmental assessment for selecting the optimum new renewable energy system for educational facility, Renewable and Sustainable Energy Reviews 29: 286–300. https://doi.org/10.1016/j.rser.2013.08.06

International Energy Agency (IEA). 2011. Solar energy perspectives. Paris, IEA.

International Energy Agency (IEA). 2013. Tracking clean energy progress 2013. Paris, IEA.

Interstate Renewable Energy Council (IREC). 2013. U.S. Solar market trends 2012. New York, IREC.

Jiang, A.; Zhu, Y. 2012. Impact of incentives and system efficiency on the life cycle cost of photovoltaic systems, International Journal of Construction Education and Research 8(3): 204–222. https://doi.org/10.1080/15578771.2011.615892

Kim, J.; Hong, T.; Koo, C. 2012. Economic and environmental evaluation model for selecting the optimum design of green roof systems in elementary schools, Environmental Science and Technology 46: 8475–8483. https://doi.org/10.1021/es2043855

Koo, C.; Hong, T.; Lee, M.; Park, H. 2013. Estimation of the monthly average daily solar radiation using geographical information system and advanced case-based reasoning, Environmental Science and Technology 47: 4829–4839. https://doi.org/10.1021/es303774a

Koo, C.; Hong, T.; Park, H.; Yun, G. 2014a. Framework for the analysis of the potential of the rooftop photovoltaic system to achieve the net-zero energy solar buildings, Progress in Photovoltaics: Research and Applications 22(4): 462–478. https://doi.org/10.1002/pip.2448

Koo, C.; Hong, T.; Kim, J.; Lee, M. 2014b. An integrated multi-objective optimization model for determining the optimal solution in the rooftop photovoltaic system, Renewable and Sustainable Energy Reviews 57: 822–837. https://doi.org/10.1016/j.rser.2015.12.205

Koo, C.; Hong, T.; Park, J. 2014c. Development of the life-cycle economic and environmental assessment model for establishing the optimal implementation strategy of the rooftop photovoltaic system, Technological and Economic Development of Economy (in press). https://doi.org/10.3846/20294913.2015.1074127

Korea Power Exchange (KPX). 2009. Global Electric Power Industry Trends. Seoul, KPX.

Laleman, R.; Albrecht, J. 2014. Comparing push and pull measures for PV and wind in Europe, Renewable Energy 61: 33–37. https://doi.org/10.1016/j.renene.2012.04.025

Lee, M.; Koo, C.; Hong, T.; Park, H. 2014. Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a Geostatistical technique, Environmental Science and Technology 48: 4604–4612. https://doi.org/10.1021/es405293u

Lee, M.; Hong, T.; Koo, C. 2015a. An economic impact analysis of state solar incentives for improving financial performance of residential solar photovoltaic systems in the United States, Renewable and Sustainable Energy Reviews 58: 590–607. https://doi.org/10.1016/j.rser.2015.12.297

Lee, M.; Hong, T.; Yoo, H.; Koo, C.; Kim, J.; Jeong, K.; Jeong, J.; Ji, C. 2015b. Establishment of a base price for the Solar Renewable Energy Credit (SREC) from the perspective of residents and state governments in the United States, Renewable and Sustainable Energy Reviews (in press). https://doi.org/10.1016/j.rser.2016.11.086

Minister of Natural Resources. 2004. RETScreen international: results and impacts 1996–2012. Canada, Minister of Natural Resources.

Minister of Natural Resources. 2010. Clean energy project analysis: RETScreen engineering and cases textbook. Canada, Minister of Natural Resources.

Nevin, R.; Watson, G. 1998. Evidence of rational market valuations for home energy efficiency, The Appraisal Journal 68: 401–409.

Open PV Project Breakeven Analysis. 2015. [online], [cited 20 April 2015]. Available from Internet: http://openpv.nrel.gov/breakeven

PJM. 2015. [online], [cited 20 April 2015]. Available from Internet: http://www.pjm.com/about-pjm

Renewable Energy Policy Network for the 21st Century (REN21). 2013. Global Status Report 2013. Paris, REN21.

SolarPowerRocks. 2015. [online], [cited 20 April 2015]. Available from Internet: www.solarpowerrocks.com

SRECTrade. 2015. [online], [cited 20 April 2015]. Available from Internet: www.srectrade.com

Swift, K. D. 2013. A comparison of the cost and financial returns for solar photovoltaic systems installed by businesses in different locations across the United States, Renewable Energy 57: 137–143. https://doi.org/10.1016/j.renene.2013.01.011

U.S. Energy Information Administration (EIA). 2015. [online], [cited 20 April 2015]. Available from Internet: www.eia.doe.gov

USInflation.org. 2015. [online], [cited 20 April 2015]. Available from Internet: http://usinflation.org/us-inflation-rate/

Wilson, R.; Luckow, P.; Biewald, B.; Ackerman, F.; Hausman, E. 2012. 2012 Carbon dioxide price forecast. Massachusetts, Synapse Energy Economics.

Yingli Solar. 2015. [online], [cited 20 April 2015]. Available from Internet: www.yinglisolar.com/en/