This study examines the effect of tourism development on energy consumption, CO2 and economic growth in China over the period from 1981 to 2010. An extension of ARIMA model was performed to investigate the relationship between variables. Two principle test results emerge from this study. First, increases on visitors may largely give rise to GDP. On the other hand, increase on tourism receipts may result in greater energy consumption and CO2 emission to some extent as compared to number of visitors. However, the amount of effects from either tourism receipts or number of visitors to energy consumption and CO2 emission are limited. From an energy conservation and economic growth point of view, the results support the hypothesis of tourism-led economic growth in the China economy with relatively limited increase of energy consumption and CO2 emission.

This research studies the causal relationship between energy consumption, gross domestic product (GDP), and foreign direct investment (FDI) in Germany for a period of 1971-2010. The empirical results reveal that there is a unidirectional causality running from GDP to energy consumption and from GDP running to FDI in Germany. This is due to the highly rising trends of economic activities in the country which can lead to the expansion in energy consumption. As there is an increase in economic activities within the country, then the growth rate will be in the rising path. As a result, the foreign investors will see the promising future and then invest in the host country. The conservative energy policy is recommended to support the energy saving because it will have little or no adverse effect on GDP. The energy efficiency should be applied by encouraging the use of renewable energy sources in economic activities as an alternative to stimulate the economic growth of the country. Also, the public expenditure should be expanded to increase the country’s economy and attract foreign investors. In addition, the government should support for the service industry such as insurance, finance and banking, and tourism because this type of industry does not consume as much energy as the manufacturing industry does in the overall manufacturing processes. Besides, the government should provide tax credit for the manufacturers who can fulfill the energy efficiency for their operation.

The balance between economic growth and environmental protection has been the core concern of policy makers in developing countries for the past two decades. This study is one of the few studies to empirically inspect the relationship between economic growth, FDI, and energy consumption over the period 1978-2010 in China. The results reveal that there is a unidirectional Granger causality running from GDP to energy consumption. This suggests that increase of GDP will consume more energy and implementing of the energy conservation policies and energy demand management policies in China may not have negative impact on economic growth. Besides, a bi-directional Granger causality has been found between energy consumption and FDI. This implies that Chinese government should cautiously evaluate the positive and negative effects of FDI inflows and put efforts into making more effective control policies on environmental protection.

There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.

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