Electricity consumption is contributed by many types of human activities, such as heating, air conditioning, lighting in both business and residential sectors, and major contributions come from operating equipment in industrial sectors. Whilst lighting and operating equipment might not be directly linked to climate change, heating and air conditioning have a direct impact on air temperature [1] . All the climate-change-related impacts on electricity demand and supply can be easily observed from the quantifiable effects of temperature on the use of heating and air conditioning, and these numbers are usually described by different measurements based on the concept of heating degree days (HDDs) and cooling degree days (CDDs).

HDDs is defined as the sum of negative deviations from the actually measured temperatures to the reference temperature (or base temperature) over a given time period; in contrast, CDDs indicates the sum of positive deviations from the average temperatures to the reference temperature over a given time period. The data frequency of the given time period is usually daily, weekly or monthly. The reference temperature is defined by the temperature level without additionally using electricity for heating or cooling. That is, if the air temperature is comfortable for humans, there will be less electricity consumption for heating or cooling.

The reference temperature can be generally considered to be 18.3˚C (65˚F) [2] . However, Parkpoom and Harrison [3] used 11.7˚C (53˚F) to be the reference temperature in Thailand; Howden and Crimp [4] determined 17.5˚C (63.5˚F) to be the reference temperature for Sydney; Ahmed et al. [5] proposed 14.3˚C (57.7˚F) as the reference temperature for the State of New South Wales in Australia after their calculation; Zachariadis and Hadjinicolaou [6] employed 18˚C (64.4˚F) and 22˚C (71.6˚F) respectively to be the reference temperature of HDDs and CDDs for the area of Mediterranean Europe. In sum, there could be different reference temperatures within different geographical regions.

Global warming could lead to increases in CDDs and decreases in HDDs, concluded by Benestad [7] , whose report indicates that climate change could trigger more energy consumption due to air conditioning in the hot areas. De Cian et al. [8] used the panel data from 31 countries to investigate the relationship between energy consumption and variations in temperature. Their empirical results suggest that higher average temperature leads to more energy consumption during hot seasons in the warmer countries, but less energy is consumed during cold seasons in the colder countries.

Hekkenberg et al. [9] assessed the electricity demand pattern in the relatively temperate climate of the Netherlands. They used daily data over the period from 1970 to 2007 to investigate possible trends in the temperature dependence of electricity demand. Although the Netherlands has the minimum electricity demand in the summer months, however, their empirical results showed significant increases in the temperature dependence of electricity demand in the months of May, June, September, October and during the summer holidays. That is, their alarming result sends a signal to raise future expectations for additional peaks of electricity consumption in summer under the in the influence of climate change.

Moral-Carcedo and Vicéns-Otero [10] figured out that the relationship between electricity demand and temperature is nonlinear, and the nonlinearity is reflected on the threshold temperatures. They employed the threshold regression model (TR) and the logistic smooth transition regression (LSTR) model to build the relationship between electricity demand and temperature in Spain using daily data from 1995 to 2003.

In their research, they created the variable of working day effect to capture the variations of electricity demand caused by the activities in the industrial and commercial sectors as well as by the behaviors of households during holidays and on working days. Hence, they could eliminate those effects from electricity demand, then focus more on the pure effects of temperature on electricity demand. Their results showed that the threshold temperatures of the TR model are 15.5˚C (59.9˚F) and 18.4˚C (65.1˚F), and the threshold temperature of the STR model is 18˚C (64.4˚F).

Bessec and Fouquau [11] investigated the relationship between electricity demand and temperature in 15 European countries over the period from 1985 to 2000 using monthly data. They applied a panel smooth transition regression (PSTR) model to describe the relationship between electricity demand and temperature in those countries and find threshold temperatures for those countries. In addition, in order to estimate the pure effects of temperature on electricity demand, they also followed Moral-Carcedo and Vicéns-Otero [10] , and used dummy variables to represent summer holidays and time trends to filter out other source of electricity consumptions. Their results showed that the nonlinear pattern was more pronounced in the warm countries among the 15 European countries.

Lee and Chiu [12] used the PSTR model and took into account the potential endogeneity biases to examine the relationship between electricity demand and temperature of 24 OECD countries over the period from 1978 to 2004. They provided evidence of a U-shaped relationship between electricity consumption and temperature of 24 OECD countries, and the threshold temperature is approximately 11.7˚C (53˚F).

In sum, to summarize the literature mentioned above, we can highlight two main findings. First, the relationship between electricity consumption and temperature shows nonlinearity in the past cases, so when establishing an econometric model for cases in Taiwan to estimate the effects of temperature on electricity consumption, we should consider possible nonlinear relationship between electricity consumption and temperature. Secondly, the threshold temperature has some policy implications, such as guidance for the management of electricity demand and supply, strategies for mitigating the impact of climate change on electricity.

To give an example of policy implications on electricity management, the Taiwanese government has introduced a policy since the year 2011 to save energy by asking public sectors to operate air conditioners only if the air temperature is higher than 26˚C (78.8˚F). In addition, once the real threshold temperature is found, it can be applied to computation of the data of CDDs in Taiwan to describe the patterns between temperature and electricity consumption both in the past and in the future. That is, if global warming leads to more temperature degree days, we should consider more power system expansion planning in Taiwan to meet the possible increases in electricity demand in the future. Therefore, we believe that it is worth further discussing how to model the real relationship between electricity consumption and temperature.

The objective of this study is to utilize the nonlinear econometric approach (STR model) to analyze the effects temperature has on electricity consumption in Taiwan. The estimated results of the STR model provide two mainly contributions to this study. First, we show the evidence of positively nonlinear relationship between electricity consumption and temperature in Taiwan. Secondly, we find that the average threshold temperature for Taiwan is about 26.384˚C (79.3˚F) over the period from 1983 to 2012. Furthermore, there are variations of threshold temperatures among different sample periods, and range of threshold temperatures lies between 25.364˚C and 27.156˚C. The contributions of this study could be turned into policy implications for policy makers.

The remainder part of this study is organized as follows: Section 2 describes the data source, data descriptive and data processing. Section 3 presents the econometric methodology and the empirical model. Section 4 provides our empirical results. Section 5 is the conclusion of this study.

In this study, we use monthly time-series data which cover the period from 1983 to 2012. The original data of electricity consumption per capita (kWh) are collected from MOEABOE [13] , and the gridded dataset of historical climate information from TCCIP [14] is used to compute the monthly average temperature (˚C) over the period from 1983 to 2012.

Table 1 displays the descriptive statistics on monthly average temperature in the different time period over 1983 to 2012. In Table 1, the mean temperature of the past three decades is between 21.997˚C and 22.383˚C. The coldest month in a year are usually January and February, and the hottest month in a year, July and August. In addition, the mean temperature for summer (June, July and August) is between 27.127˚C and 27.460˚C, and the stand deviation of temperature in