**3. Empirical framework**

#### **3.1 Data**

Between 2005 and 2016, annual data for OECD nations was used. The dataset is based on two main sources: the International Energy Agency (IEA) dataset for residential natural gas consumption and residential natural gas and electricity prices, and the World Bank dataset for per capita income, overall population, population density, urban population percentage, aged population, heating and cooling degree days.

Due to a lack of data, five of the 34 current members of the Organization for Economic Cooperation and Development were excluded from the sample. Due to lacking price data or no reported natural gas demand, Estonia, Iceland, Israel, Norway, and Slovenia were omitted from the list. **Figure 3** depicts the study's precise countries. Every year, each country in the sample is observed, ensuring that the data set is balanced. The following are the descriptive data for the remaining 29 members, as shown in **Table 1**.

The elderly, the urban population, and population density are three crucial variables in the model, taking into account the paper's demographic methodology. The mid-year population is divided by the land area in square kilometers to get the density.

**Figure 4** shows the evolution of the senior population (over 65 years) and the urbanization rate (the fraction of the population living in cities) in OECD countries. The percentage of the population over 65 years old and the population living in urban regions are both growing over time, as seen in **Figure 4**. These two variables are critical since they account for around 17 and 79% of the total in 2016.

Then, the population density4 of different OECD countries in 2016 was exposed as the year of reference (the most recent one). **Figure 5** shows that population density varies dramatically across countries and Korea, Japan, Belgium and Netherlands are the densest countries.

The entire population aged 65 or older in the total population at the national level is included in the empirical model, indicating that each country is aging. The urbanization rate is also included to look into the role of the growing rate of people moving from rural to urban areas. Furthermore, the study considers population density when examining the effects of densely populated countries on residential natural gas use. Other control variables are also included in the model. To determine the income level, the study takes into account the total population (POPit),

<sup>4</sup> Inhabitants/km<sup>2</sup> .

*The Dynamic of Residential Energy Demand Function: Evidence from Natural Gas DOI: http://dx.doi.org/10.5772/intechopen.102451*

#### **Figure 3.**

*Countries in the study. Reference: own elaboration with tableau public software.*


*Heating degree day (HDD) is a quantitative index reflecting demand for energy to heat buildings or businesses. b Cooling degree day (CDD) is a quantitative index reflecting demand for energy to cool buildings or businesses.*

#### **Table 1.**

*Definition of variables and descriptive statistics. N\*T (number of observations time series) =348.*

end-user natural gas price (GPit), and gross domestic product per capita (INCit). In addition, to account for weather effects, the price of electricity (EPit) as the closest replacement, as well as the Heating and Cooling Degree Days (HDDit) and CDDit) were included.

**Figure 4.**

*Elderly and population rate (percentage, 2000–2016). Reference: own elaboration based on World Bank data (with tableau public software).*

**Figure 5.**

*Population density (inhabitants/km<sup>2</sup> , 2016). Reference: own elaboration based on World Bank data (with tableau public software).*

The correlation matrix was used to show the statistical correlation between the dependent variable and the regressors. **Table 2** shows that per capita natural gas demand is positively correlated with the fraction of the elderly (ELDit) and population density (DENit) (GTit). The matrix also reveals a negative relationship between urbanization rate (URBit) and natural gas consumption. Furthermore, natural gas usage is inversely connected with its own prices (GPit) and cooling degree days (CDDit), but favorably with per capita income (INCit), population (POPit), electricity price (EPit), and heating degree days (HDDit). The correlation matrix, on the other hand, is a basic statistical link between two variables; as a result, a more precise specification is required to investigate the impact of demographic variables on natural gas demand.

#### **3.2 Econometric technique**

Household production theory, which considers the consumer as a firm, assumes that households employ inputs (natural gas in this case) to manufacture nonmarket commodities or utility-yielding items. Thus, the demand for welfare services such


**Table 2.** *Correlationmatrix.*
